-
0018-9340 (c) 2013 IEEE. Personal use is permitted, but
republication/redistribution requires IEEE permission.
Seehttp://www.ieee.org/publications_standards/publications/rights/index.html
for more information.
This article has been accepted for publication in a future issue
of this journal, but has not been fully edited. Content may change
prior to final publication. Citation information:
DOI10.1109/TC.2014.2308211, IEEE Transactions on Computers
1
Maximizing P2P File Access Availability inMobile Ad hoc Networks
Though Replication for
Efficient File SharingKang Chen, Student Member, IEEE, Haiying
Shen*, Senior Member, IEEE,
AbstractFile sharing applications in mobile ad hoc networks
(MANETs) have attracted more and more attention in recent years.
Theefficiency of file querying suffers from the distinctive
properties of such networks including node mobility and limited
communicationrange and resource. An intuitive method to alleviate
this problem is to create file replicas in the network. However,
despite the effortson file replication, no research has focused on
the global optimal replica creation with minimum average querying
delay. Specifically,current file replication protocols in mobile ad
hoc networks have two shortcomings. First, they lack a rule to
allocate limited resourceto different files in order to minimize
the average querying delay. Second, they simply consider storage as
resource for replicas, butneglect the fact that the file holders
frequency of meeting other nodes also plays an important role in
determining file availability.Actually, a node that has a higher
meeting frequency with others provides higher availability to its
files. This becomes even moreevident in sparsely distributed
MANETs, where nodes meet disruptively. In this paper, we introduce
a new concept of resource forfile replication, which considers both
node storage and meeting frequency. We theoretically study the
influence of resource allocationon the average querying delay and
derive a resource allocation rule to minimize the average querying
delay. We further propose adistributed file replication protocol to
realize the proposed rule. Extensive trace-driven experiments with
synthesized traces and realtraces show that our protocol can
achieve shorter average querying delay at a lower cost than current
replication protocols.
Index TermsMANETs, Peer-to-Peer, File Sharing, File
Availability
F
1 INTRODUCTIONWith the popularity of popularity of mobile
devices,i.e., smartphones and laptops, we envision the future
ofMANETs consisted of these mobile devices. By MANETs,we refer to
both normal MANETs and disconnectedMANETs (or delay tolerant
networks (DTNs). The for-mer has a relatively dense node
distribution in a localarea while the latter has sparsely
distributed nodes thatopportunistically meet each other. On the
other side, theemerging of mobile file sharing applications (e.g.,
Qik [1]and Flixwagon [2]) also motivates the investigation onthe
peer-to-peer (P2P) file sharing over such MANETs.
The local P2P model provides three advantages.Firstly, it
enables file sharing when no base stationsare available (e.g.,
rural area). Secondly, with the P2Parchitecture, the bottleneck on
overloaded servers incurrent client-server based file sharing
systems can beavoided. Thirdly, it exploits the otherwise wasted
peer topeer communication opportunities among mobile nodes.As a
result, nodes can freely and unobtrusively accessand share files in
the distributed MANET environment,which can possibly support some
interesting applica-tions. For example, mobile nodes can share
files basedon users proximity [3] in the same building or a
localcommunity. Tourists can share their travel experiences
* Corresponding Author. Email: [email protected]; Phone: (864)
6565931; Fax: (864) 656 5910.
The authors are with the Department of Electrical and Computer
Engi-neering, Clemson University, Clemson, SC, 29634.E-mail:
{kangc, shenh}@clemson.edu
or emergency information with other tourists throughdigital
devices directly even when no base station isavailable in remote
areas. Drivers can share road orweather information through the
vehicle-to-vehicle com-munication.
However, the distinctive properties of MANETs, in-cluding node
mobility, limited communication range andresource, have rendered
many difficulties in realizingsuch a P2P file sharing system. For
example, file search-ing turns out to be non-trivial and time
consumingsince nodes in MANETs move around freely and canexchange
information only when they are within thecommunication range.
Broadcasting can quickly discoverfiles, but it generates the
broadcast storm problem [4]with high energy consumption.
Probabilistic routing andfile discovery protocols [5][7] avoid
broadcasting byforwarding a query to a node with higher probability
ofmeeting the destination. But the opportunistic encoun-tering of
nodes in MANETs makes file searching andretrieval non-trivial.
File replication is an effective way to enhance fileavailability
and reduce file querying delay. It createsreplicas for a file to
improve its probability of beingencountered by requests.
Unfortunately, it is impracticaland inefficient to enable every
node to hold the repli-cas of all files in the system considering
limited noderesources. Also, file querying delay is always a
mainconcern in a file sharing system. Users often desire toreceive
their requested files quickly no matter whetherthe files are
popular or unpopular. Thus, a critical issue israised for further
investigation: how to allocate the limitedresource in the network
to different files for replication so thatthe overall average file
querying delay is minimized?
-
0018-9340 (c) 2013 IEEE. Personal use is permitted, but
republication/redistribution requires IEEE permission.
Seehttp://www.ieee.org/publications_standards/publications/rights/index.html
for more information.
This article has been accepted for publication in a future issue
of this journal, but has not been fully edited. Content may change
prior to final publication. Citation information:
DOI10.1109/TC.2014.2308211, IEEE Transactions on Computers
2
Recently, a number of file replication protocols havebeen
proposed for MANETs [8][12]. In these proto-cols, each individual
node replicates files it frequentlyqueries [8][10], or a group of
nodes create one replicafor each file they frequently query
[10][12]. In theformer, redundant replicas are easily created in
the sys-tem, wasting resources. In the latter, though
redundantreplicas are reduced by group cooperation, neighbor-ing
nodes may separate from each other due to nodemobility, leading to
large query delay. There are alsosome works addressing content
caching in more sparselydistributed MANETs (disconnected
MANETs/DTNs) forefficient data retrieval [13][19] or message
routing [20].They basically follow an intuitive way to cache
datathat are frequently queried on places that are
visitedfrequently by mobile nodes. Both the two categories
ofreplication methods fail to thoroughly consider that anodes
mobility affects the availability of its files.
In spite of the efforts, current file replication protocolslack
a rule to allocate limited resource to different filesfor replica
creation in order to achieve the minimumglobal average querying
delay, i.e., global search effi-ciency optimization under limited
resource. Moreover,they simply consider storage as the resource for
replicas,but neglect that a nodes frequency to meet other
nodes(meeting ability in short) also influences the availabilityof
its files. Files in a node with a higher meeting abilityhave higher
availability.
In this paper, we introduce a new concept of resourcefor file
replication, which considers both node storageand node meeting
ability. We theoretically study theinfluence of resource allocation
on the average queryingdelay and derive an optimal file replication
rule thatallocates resources to each file based on its
popularityand size. To the best of our knowledge, this work isthe
first attempt to theoretically investigate the problemof resource
allocation for replica creation to achieveglobal file searching
optimization in MANETs. We fur-ther propose a file replication
protocol based on therule, which approximates the minimum global
queryingdelay in a fully distributed manner. Our experiment
andsimulation results show the superior performance of theproposed
protocol in comparison with other representa-tive replication
protocols.
The remainder of this paper is organized as follows.Section 2
presents an overview of the related works.Section 3 presents the
analysis and modeling of the influ-ence of the resource allocation
on file searching efficiencyunder two representative mobility
models. Section 4details the file replication protocol. In Section
5, 6, and 7,the performance of our proposed system is
evaluatedthrough real traces and synthesized mobility. Section
8concludes the paper.
2 RELATED WORK2.1 File Sharing in Normal MANETsThe topic of file
replication for efficient file sharing ap-plications in MANETs has
been studied recently. In [10][12], individual or a group of nodes
decide the list
of files to replicate according to file visiting frequency.Hara
[10] proposed three file replication protocols: StaticAccess
Frequency (SAF), Dynamic Access Frequencyand Neighborhood (DAFN)
and Dynamic Connectivitybased Grouping (DCG). In SAF, each node
replicatesits frequently queried files until its available
storageis used up. SAF may lead to many duplicate replicasamong
neighboring nodes when they have the same in-terested files. DAFN
eliminates duplicate replicas amongneighbors. DCG further reduces
duplicate replicas in agroup of nodes with frequent connections. It
sums theaccess frequencies of all nodes in a group and
createsreplicas for files in the descending order. Though DAFNand
DCG enable replicas to be shared among neighbors,neighboring nodes
may separate from each other dueto node mobility. Also, they incur
high traffic load inidentifying duplicates or managing groups.
Zhang et al [11] proposed to let each node collectaccess
statistics from neighbors to decide the creationor relinquishment
of a replica. Duong and Demeure [12]proposed to group nodes with
stable connections and leteach node checks its group members
potential possibil-ity of requesting a file and their storage
status to decidereplicate the file or not. Also, each node notifies
all othernodes in the system about its newly created files
bybroadcasting. Yin and Cao [9] proposed to cache popularfiles on
the intersection nodes of file retrieval paths.Though it is
effective for popular files, it fails to utilize allstorage space
in nodes other than the intersection nodes.
Gianuzzi [21] investigated the probability of acquiringa file,
which has n replicas in the network, from thepotentially
partitioned network. He also studied thefile retrieval performance
when erasure coding [22] isemployed to segment files. Chen [23]
discussed how todecide the minimal number of mobile servers
neededto satisfy the requirement that every data item can
beobtained within at most k (k 1) hops by any node inthe system.
Moussaoui et al. [8] proposed two steps of filereplication, primary
replication and dynamic replication,to disseminate replicas in the
network in order to meetuser needs and prevent data loss in the
case of networkpartition. In the primary replication step, newly
createdfiles are distributed evenly among nodes that are threehops
away from each other through replication. Later,when the network
topology changes, dynamic replica-tion is conducted, in which each
node checks its visitingfrequency to a file or the density of a
file to make thereplication decision.
2.2 File Sharing in Disconnected MANETs/DTNsHuang et al. [13]
discussed how to cache files in serversto realize the optimal file
availability to mobile users inWiFi-based wireless networks based
on node mobilitypattern, AP topology and file popularity. However,
thefile servers in this paper are fixed nodes connecting toAPs,
while we consider a more general P2P scenario, inwhich all mobile
nodes are both file servers and clients.Pitkanen and Ott [17]
proposed the DTN storage moduleto leverage the DTN
store-carry-and-forward paradigm
-
0018-9340 (c) 2013 IEEE. Personal use is permitted, but
republication/redistribution requires IEEE permission.
Seehttp://www.ieee.org/publications_standards/publications/rights/index.html
for more information.
This article has been accepted for publication in a future issue
of this journal, but has not been fully edited. Content may change
prior to final publication. Citation information:
DOI10.1109/TC.2014.2308211, IEEE Transactions on Computers
3
and make DTN nodes keep a copy of a message for alonger period
of time required by forwarding. Gao etal. [14] proposed a
cooperative caching method in DTNsby copying each file to the node
in each network centrallocation, which is frequently visited by
other nodes.When the central node is full, less-popular replicas
aremoved to its neighbor nodes. However, central nodesmay be
frequently changed, leading to frequent filetransfers and high
overhead. QCR [15] leverages cachingfor multimedia content
dissemination in opportunisticnetworks. It considers data retrieval
delay and the prob-ability that users will require the same content
basedon previously experiences to decide the caching policy.SEDUM
[20] also uses replication to create redundantmessages in routing
for DTNs, thereby enhancing rout-ing success rate. PSEPHOS [16]
considers three factorsincluding data access frequency, user
preference andnode mobility to decide the data caching. The
authorin [18] considers the contact duration in DTNs to
betterimprove data retrieval probability through replication.In
[19], both social community structures and contactduration among
nodes are considered to decide whereand how much to cache data in
DTNs. However, thesemethods fail to consider that the mobility of a
nodeaffects the availability of files or messages and fur-ther
optimize the replication distribution to enhance fileavailability
or routing success rate.
2.3 Modeling Replication Optimization ProblemWe present the
general process to model the expected
file querying delay with file replication. We let mi be
theprobability that a nodes newly met node in the comingtime
interval is node i, which reflects the meeting abilityof the files
on node i. We also use Xij to denote whethernode i owns file j or
its replication. Then, the averagenumber of time intervals needed
to meet a specific file,say file j, can be represented as:
Tj =1
Ni=1
miXij
(1)
Then, the average number of intervals needed to satisfya request
is
T =
Fj=1
qj Tj =
Fj=1
qjNi=1
miXij
, (2)
where qj is the probability of querying file j. WithFormula (2),
we can formulate the global optimizationproblem as minimizing T ,
which can be further utilizedto deduce the optimal replication
rule.
However, the calculation of mi may be complex andmakes the
minimization problem non-trivial. We willdiscuss how this is
handled with the two commonmobility models in Section 3.
3 THEORETICAL ANALYSIS OF GLOBALLYOPTIMAL FILE REPLICATION
3.1 Node Movement ModelsRecall that we consider two types of
MANETs (i.e., nor-mal MANETs and disconnected MANETs) in this
paper.In the research area of MANETs, usually, the randomwaypoint
model (RWP) [24] is used for the normalMANETs and the
community-based mobility model [25]is used for the disconnected
MANETs (and DTNs). Thus,we also use the two models to represent the
two typesof MANETs in theoretical analysis. We leave the
analysisfor other mobility models (i.e., created by Bonn MotionTool
[26]) as our future work.
3.1.1 Random Waypoint Model for Normal MANETsAs some MANET
replication protocols [10], [11], [21],we use the random waypoint
model (RWP) [24] tomodel node mobility in normal MANETs. In RWP,
nodesrepeatedly move to a randomly selected point at arandom speed,
which means each node has roughlysimilar probability to meet other
nodes. However, nodesusually have different probabilities of
meeting nodes inreality (i.e., nodes with faster speed can meet
others morefrequently). We hence let each node have a randomly
ob-tained speed, rather than continuously varying a nodesspeed in
different paths as in the normal RWP model.
3.1.2 Community-Based Mobility Model for Discon-nected MANETsThe
community-based mobility model [25] has beenused in some content
dissemination or routing algo-rithms for disconnected MANETs/DTNs
[27], [28] todepict node mobility. In this model, the entire test
areais split into different sub-areas, denoted as caves. Eachcave
holds one community. A node belongs to one ormore communities
(i.e., home community). The routinesand (or) social relationships
of a node tend to decide itsmobility pattern. When moving, a node
has probabilityPin to stay in the home community and probability1
Pin to visit a foreign community. A node moveswithin its home
communities for most of the time (i.e.,Pin usually is large).
Please refer to [25] for more detail.
3.1.3 Assumptions and LimitationsWith above two mobility models,
our analysis replies
on two assumptions: 1) the probability of meeting acertain node
is the same for all nodes (RWP model)or all nodes in its home
community (community-basedmodel) and 2) nodes move independently in
the network(both models). The two assumptions may not hold inreal
cases, which limits the applicability of the analysisresults in our
paper to different real scenarios. However,the analysis results can
provide instructions on file repli-cation because the two models
can represent key char-acteristics in real mobility and have been
widely used inresearch works [10], [11], [21], [27], [28]. We also
havebriefly discussed how to expand the analysis to
generalscenarios, which do not have the two assumptions, inSection
2.3 and 3.2.3. Due to the complexity of suchgeneral cases, we leave
the detailed research without thetwo assumptions to future
work.
-
0018-9340 (c) 2013 IEEE. Personal use is permitted, but
republication/redistribution requires IEEE permission.
Seehttp://www.ieee.org/publications_standards/publications/rights/index.html
for more information.
This article has been accepted for publication in a future issue
of this journal, but has not been fully edited. Content may change
prior to final publication. Citation information:
DOI10.1109/TC.2014.2308211, IEEE Transactions on Computers
4
3.2 Theoretical Analysis
TABLE 1: Notations in analysis.
Notation Meaningqj The probability of querying file j in the
systemmi The probability that the next encountered node is node ipj
The probability of obtaining file j in the next encountered nodeN
Total number of nodesVi Node is meeting ability (i.e., frequency of
meeting nodes)Si Storage space of node iV Average meeting ability
of all nodes in the systemF Total number of files in the systembj
Size of file jXij Whether node i contains file j or notVjk Meeting
ability of the kth node that holds file jnj The number of nodes
holding file j or its replicasAj Allocated resource for file j for
replicationTj Average number of time intervals needed to meet file
jT Average number of time intervals needed to meet a fileR Total
amount of resource in the systemPj Priority value of file j, Pj
=
qj/bj
In this section, we theoretically analyze the influenceof the
file replica distribution on the overall queryefficiency in MANETs
under the two mobility modelsfollowing the process introduced in
Section 2.3. Pleaserefer to Table 1 for the meanings of
notations.
3.2.1 Optimal File Replication with the RWP modelIn the RWP
model, we can assume that the inter-meetingtime among nodes follows
exponential distribution [29],[30]. Then, the probability of
meeting a node is inde-pendent with the previous encountered node.
Therefore,we define the meeting ability of a node as the
averagenumber of nodes it meets in a unit time and use it
toinvestigate the optimal file replication. Specifically, if anode
is able to meet more nodes, it has higher probabilityof being
encountered by other nodes later on. We use mito denote the
probability that the next node a requestholder meets is node i.
Then, mi is proportional to nodeis meeting ability (i.e., Vi). That
is
mi =ViN
k=1Vk
=Vi
V N(3)
where N denotes the total number of nodes and Vdenotes the
average meeting ability of all nodes.
We use vector (Vj1, Vj2, . . . , Vjnj ) to denote the meet-ing
abilities of a group of nodes holding file j or itsreplica, where
nj is the number of file j (includingreplicas) in the system. Then,
the probability that a nodeobtains its requested file j from its
encountering node isthe sum of the probabilities of encountering
nodes thathold file j or its replica. That is,
pj =
Ni=1
miXij =
Ni=1
Vi
V NXij =
njk=1
Vjk
V N(4)
where Xij is a zero-one variable that denotes whethernode i
contains file j or its replica.
As stated above, a nodes probability of being encoun-tered by
other nodes is proportional to the meeting abil-ity of the node.
This indicates that files residing in nodeswith higher meeting
ability have higher availability thanfiles in nodes with lower
meeting ability. So we take intoaccount both meeting ability and
storage in measuring
a nodes resource. When a replica is created in a node,
itoccupies the memory on the node. Also, its probabilityof being
met by others is decided by the nodes meetingability. This means
that the replica naturally consumesboth the storage resource and
the meeting ability re-source of the node. Therefore, we denote the
resourceon a node by SiVi, in which Si denotes node is storagespace
and Vi denotes its meeting ability. Then, the totalamount of
resource in the system (R) is:
R =Ni=1
SiVi (5)
Thus, the total resource allocated to file j is:
Rj = bj
njk=1
Vjk (6)
where bj is the size of file j. Based on Equation (6),Equation
(4) can be represented as
pj =
bj
njk=1
Vjk
bjV N=
Rj
bjV N(7)
Thus, the probability of meeting file j after k (k =1, 2, 3, )
time intervals (i.e., average inter-meeting timeamong nodes) is
(1 pj)k1pjand the average number of time intervals needed for
anode to meet a node containing file j is
Tj =
k=1
k(1 pj)k1pj =1
pj=bjV N
Rj(8)
We use qj [0, 1] to denote the probability of a nodesoriginating
a request for file j in the system during a unitof time period
(
Fj=1 qj = 1). Then, the average number
of intervals needed to satisfy a request is
T =
Fj=1
qjTj =
Fj=1
qjbjV N
Rj= V N
Fj=1
qjbjRj
(9)
We aim to minimize the global file querying delay(i.e., T ) by
file replication. According to Equation (9),T is decided by qj , bj
and Rj , and the values of qjand bj are decided by the system.
Thus, the problem ofoptimal resource allocation is then converted
to findingthe optimal amount of resource (Rj) for each file j
underthe restriction of total available resource in order toachieve
the minimum average querying delay.
Suppose Bj = qjbj , with Equations (5) and (9), theproblem of
optimal resource allocation is expressed by
min(T ) = min{F
j=1
qjbjRj} = min{
Fj=1
BjRj} (10)
subject to:F
j=1
Rj R.
Equation (9) also indicates that each Rj should be aslarge as
possible in order to minimize T . Therefore, weassume all resources
(R) are allocated.
-
0018-9340 (c) 2013 IEEE. Personal use is permitted, but
republication/redistribution requires IEEE permission.
Seehttp://www.ieee.org/publications_standards/publications/rights/index.html
for more information.
This article has been accepted for publication in a future issue
of this journal, but has not been fully edited. Content may change
prior to final publication. Citation information:
DOI10.1109/TC.2014.2308211, IEEE Transactions on Computers
5
Fj=1
Rj = R (11)
By applying Formula (11), Formula (10) is changed to
min(T ) = min{B1
R1+
B2
R2+ +
BF
R (R1 +R2 + +RF1)} (12)
Next, we try to find the value of Rj (1 j F 1)that satisfies
Formula (12). Specifically, we first calculatethe first order
(necessary) condition by differentiating Ton each Rj (1 j F 1)
respectively, and find thevalue of Rj that makes the differentiated
formula equal0. The resultant formulas after differentiation
are
B1R21 BF{R (R1 +R2 + +RF1)}2
= 0 (13)
BF1R2F1
BF{R (R1 +R2 + +RF1)}2= 0 (14)
Combine all of the above F 1 equations, we getB1R21
=B2R22
=B3R23
= = BF1R2F1
=BFR2F
(15)
To achieve the minimal average delay, the second
order(sufficient) condition should be larger than 0 as below:
2B1R31
2BF{R (R1 +R2 + +RF1)}3> 0 (16)
2BF1R3F1
2BF{R (R1 +R2 + +RF1)}3> 0 (17)
If Equation (15) is true, based on Equation (11), Formu-las (16)
and (17) can be transformed to below.
(1
RF 1R1
)2B1R21
> 0 (18)
(
1
RF 1RF1
)2BF1R2F1
> 0 (19)
When RF < Rj (j [1, F 1]), Equations (18) and (19)(and also
the second order condition) are satisfied. Recallthat above result
is obtained when we replace RF withR(R1+R2+ +RF1) in Equation (10).
If we replaceRk (k [1, F ]) with R (R1 + Rk1 +Rk+1 + RF ),the
second order is also satisfied when Rk < Rj (j [1, F ], j 6= k).
In summary, the second order is satisfiedwhen the resource
allocated for one file is less thanthe resource allocated for any
other file. This conditionis always true because there always
exists a file withthe minimum allocated resource. Therefore, as
long asthe first order condition (Equation (15)) is satisfied,
thesecond order condition is also satisfied.
Then, according to Equation (11) and Equation (15),we can see
that the optimal allocation is
Rj =
Bj
Fk=1
Bk
R (j = 1, 2, 3, , F ) (20)
This means that the optimal resource allocation isachieved
through the square root policy, i.e., the portionof resource for
file j is in direct proportion of the square
root of Bj :
Rj Bj bj
njk=1
Vjk bjqj (21)
That is njk=1
Vjk qjbj
njk=1
Vjk Pj (22)
We callqj/bj the Priority Value (P ) of file j as it
represents the relative priority in acquiring resource forthe
global optimization on querying delay.
Based on Formula (22), we derive the Optimal FileReplication
Rule (OFRR) that gives the direction for theoptimal resource
allocation for each file that leads to theminimum average file
querying delay under the RWPmodel.
OFRR. In order to achieve minimum overall file queryingdelay,
the sum of the meeting ability of replica nodes of file jshould be
proportional to Pj =
qj/bj .
3.2.2 Optimal File Replication with the Community-Based Mobility
Model
In this section, we conduct the analysis under
thecommunity-based mobility model. Unless otherwisespecified, we
use the same notations in Table 1 (which isfor the RWP model) but
add to each notation to denotethat it is for the community-based
mobility model. Recallthat in the RWP model, we can assume that the
inter-meeting time of nodes follows exponential distribution.Based
on this assumption, we can calculate the proba-bility that a newly
met node is node i (i.e., mi), which isused to find the expected
time T to satisfy a request andfinally deduce OFRR to minimize T .
However, underthe community-based mobility model, this
assumptiondoes not hold [31]. This makes it difficult to calculate
mi,which makes the process of minimizing the overall delayT a
formidable problem. To deal with this problem,rather than
considering meeting ability, we consider eachnodes satisfying
ability. It is defined as a nodes abilityto satisfy queries in the
system (denoted by V i ) and iscalculated based on the nodes
capacity to satisfy queriesin each community.
We use Nc to denote the number of nodes in commu-nity c. Then,
community c holds NcN fraction of nodesin the system. Node is
satisfying ability to communityc depends on both the number of
different nodes in cit meets in a unit time period (denoted by
Mic), andthe number of queries generated by nodes in c. In
thismodel, since nodes file interests are stable during a cer-tain
time period, we assume that each nodes queryingpattern (i.e.,
different querying rates for different files)remains stable during
a certain period of time.
Then, the number of nodes in a community representsthe number of
queries for a given file generated in thiscommunity. As a result, a
file holder has low abilityto satisfy queries from a small
community. Thus, weintegrate each communitys fraction of nodes
(i.e., NcN )into the calculation of the satisfying ability.
Therefore,
-
0018-9340 (c) 2013 IEEE. Personal use is permitted, but
republication/redistribution requires IEEE permission.
Seehttp://www.ieee.org/publications_standards/publications/rights/index.html
for more information.
This article has been accepted for publication in a future issue
of this journal, but has not been fully edited. Content may change
prior to final publication. Citation information:
DOI10.1109/TC.2014.2308211, IEEE Transactions on Computers
6
V i =
Cc=1
MicNcN
(23)
where C is the total number of communities.Given nj nodes that
hold file j or its replicas, we
again use vector (V j1, Vj2, . . . , V
jk, . . . , V
inj
) to denotethe satisfying abilities of these nodes. Then, the
overallability of nodes in the system to satisfy requests for filej
(denoted by Oj) is the sum of all the satisfying abilitiestimes a
redundancy elimination factor .
Oj =
njk=1
V jk ( [0, 1]) (24)
is added because different holders of file j may meetthe same
requester for file j in the same time unit. Sincethe requester has
only one request for file j, only thefirst meeting satisfies the
file request, and the subsequentmeetings do not satisfy any
requests for file j. In otherwords, denotes the discount on the
overall satisfyingability considering the fact that the satisfying
abilities ofdifferent file holders may overlap.
Then, the number of time intervals (i.e., average inter-meeting
time among nodes) needed to satisfy a requestfor file j is
T j =1
Oj=
1
njk=1
V jk
(25)
Recall that bj denotes the size of file j and qj denotes
theprobability of initiating a request for file j from nodesin the
system. Similar to Equation (6), the total resource(satisfying
resource and storage resource) allocated to file
j can be represented by Rj = bjnjk=1
V jk. As a result,
the average number of time intervals needed to satisfya request
in the system is
T =
Fj=1
qjT j =
Fj=1
qj1
njk=1
V jk
=1
Fj=1
qjbjRj
(26)
Then, the problem of optimal resource allocation can beexpressed
by
min(T ) = min{F
j=1
qjbjRj} = min{
Fj=1
BjRj} (27)
subject to: Fj=1
Rj R.
We can find that Equation (27) is the same as Equa-tion (10).
Then, we follow the same process after Equa-tion (10) and deduce
the OFRR rule in disconnectedMANETs as
njk=1
V jk qjbj
njk=1
V jk Pj (28)
We see that the OFRR under the community-basedmobility model
(Equation (28)) is the same as the OFRRdeduced with the RWP model
(Equation (22)) except thatV jk is the satisfying ability (Equation
(23)) in the formerwhile is the meeting ability (defined in Table
1) in thelater. It is intriguing to find that Equation (23) turns
to be
the same as the definition of Vi in Table 1 if the numberof
community is 1. This means that the OFRR expressedby Equation (22)
is a special case of the OFRR expressedby Equation (28). As a
result, our previously deducedOFRR can be the OFRR for MANETs under
the twomobility models.
It is interesting to find that the OFRR matches thesquare root
assignment rule derived by Kleinrock [32]for the link capacity
assignment in wireless communica-tion to maximize the network
efficiency. It also matchesthe findings in [33] that when file
servers may be un-available due to node dynamism, the wired P2P
contentdistribution systems can achieve the maximum file hitrate
when available storage is allocated in proportion toa constant
value plus ln(qj/bj) for each file.
3.2.3 Extension to General Node Mobility ModelsIn the above two
subsections, we deduced the OFRR
rule in RWP mobility model and community-based mo-bility model
following the basic idea in Section 2.3.However, above analysis
relies on two assumptionsmentioned in Section 3.1.3, which may not
hold ingeneral node mobility models. Therefore, it is nontrivialto
extend above analysis to general cases directly. Specif-ically, in
certain mobility models, different nodes mayhave different visiting
preferences or patterns, makingdifferent nodes probabilities of
meeting node i in thenext encountering (mi) lack a direct general
expression.
However, there are some ways to make the analysis ingeneral
cases possible. For example, we can incorporatenew factors into mi
to express each nodes distinctpattern, e.g., active levels and
community identities.These factors usually represent how frequent a
nodemeets other nodes. We can also first measure the
meetingabilities of different nodes in a real scenario. Then, wecan
assign labels to each node to indicate its roughmeeting ability.
With these simplifications, mi can beexpressed and the analysis can
be conducted. We leavethe research following such a direction to
future work.
On the other hand, there are possibly fixed nodes inthe system,
which are naturally supported in our anal-ysis. This is because we
only care a nodes storage andmeeting ability regarding creating
file replicas. Thoughfixed nodes do not move, they can meet other
nodes,which means their meeting abilities can be measured oreven
formulated. As a result, fixed nodes are regardedthe same as mobile
nodes in the system.
3.3 Meeting Ability Distribution in Real TracesWe measured the
meeting ability distribution from realtraces to confirm the
necessity to consider node meetingability as an important factor in
the resources allocationin our design. Specifically, for normal
MANETs, we usedthe Dartmouth trace [34], which was obtained
throughan outdoor project in Dartmouth College. The traceprovides
position records of 35 laptop nodes movingrandomly and
independently across different sections ofan open field. For
disconnected MANETs, we used theMIT Reality trace [35] and the
Haggle trace [36]. In the
-
0018-9340 (c) 2013 IEEE. Personal use is permitted, but
republication/redistribution requires IEEE permission.
Seehttp://www.ieee.org/publications_standards/publications/rights/index.html
for more information.
This article has been accepted for publication in a future issue
of this journal, but has not been fully edited. Content may change
prior to final publication. Citation information:
DOI10.1109/TC.2014.2308211, IEEE Transactions on Computers
7
15
18
21
24
27
30
1 6 11 16 21 26 31
Meetin
gability(x10
3 )
Nodesequence
Dartmouttrace
(a) In a connected MANET.
0
4
8
12
16
20
1 11 21 31 41 51 61 71 81 91
Meetin
gability(1
02)
Nodesequence
MitRealitytraceHaggletrace
(b) In disconnected MANETs.Fig. 1: Meeting ability
distribution.
former, 97 smart phones were distributed to studentsand
faculties at MIT. In the latter, 98 iMotes were as-signed to
scholars attending the Infocom06 conference.In both traces, nodes
contact records were recorded.
For each trace, we measured the meeting abilities of allnodes
and ranked them in decreasing order, as shownin Figure 1(a) and
Figure 1(b). We see that in all thethree traces, node meeting
ability is distributed in awide range. This matches our previous
claim that nodesusually have different meeting abilities. Also, it
verifiesthe necessity of considering node meeting ability as
aresource in file replication since if all nodes have
similarmeeting ability, replicas on different nodes have
similarprobability to meet requesters, and hence there is noneed to
consider meeting ability in resource allocation.
4 DISTRIBUTED FILE REPLICATION PROTO-COL
In this section, we propose a distributed file
replicationprotocol that can approximately realize the optimal
filereplication rule (OFRR) with the two mobility models ina
distributed manner. Since the OFRR in the two scenar-ios (i.e.,
Equation (22) and Equation (28)) have the sameform, we present the
protocol in this section withoutindicating the specific scenario.
We first introduce thechallenges to realize the OFRR and our
solutions to thesechallenges. Then, we propose a replication
protocol torealize OFRR and analyze the effect of the protocol.
4.1 Challenges and Solutions to Achieve the OFRRChallenge 1:
resource allocation without a centralserver. OFRR shows that in
order to realize the globallyoptimal querying delay, each files
popularity (qj) andsize (bj), and the system resource (R)
information (bothnode storage size and moving ability) must be
known inorder to decide the portion of resource for each file
forreplica creation. Specifically, suppose there are F files inthe
system with b1q1 bF qF and total resource R, theresource allocated
to file j (Rj) should be
Rj = Rbjqj/
Fk=1
bkqk (29)
Then, an intuitive way to achieve this goal is to setupa central
server to collect all above-mentioned informa-tion, conduct the
resource allocation for each file, anddistribute the information to
file owners to replicate theirfiles. However, the nature of the
distributed network,node mobility and transmission range constraint
becomeobstacles of building such a central service. For
example,
since nodes are constantly moving and have limitedcommunication
ranges, it is impossible for each nodeto update its information to
or receive information fromthe server timely. Thus, a severe
challenge is to enablea node to distributively figure out the
proper portion ofresource for each of its files without a central
server.
Even when each node knowsbjqj/
Fk=1
bkqk of
each of its files, the total amount of resources availablein the
system may change due to node joins and depar-tures, which makes it
difficult for a node to calculate theportion of resource of each of
its file (Rj). For example,suppose there are only two files in the
system, say f1 andf2, and the ratio of their allocated resources
should be4:1. If the total amount of resourceR = 40, the amount
ofresource allocated to f1 is 32. If R = 60, the amount forf1
should be adjusted to 48. Further, the time-varying filepopularity
(qj) make the problem even more formidable.Therefore, OFRR cannot
be simply realized by lettingeach node distribute replicas of a
file until an absoluteamount of resource is used.
Solution to Challenge 1: resource competition. OFRR(i.e, Formula
(22)) requires that for each file, the sum ofits replica nodes
meeting abilities,
nFk=1 VFk, is propor-
tional to its priority value P . In other words, OFRR canbe
shown by
P1/
n1k=1
V1k = P2/
n2k=1
V2k = PF /nFk=1
VFk (30)
where nj (j [1, 2, , F ]) represents the number ofreplica nodes
of file j. Then, we can let each file, say filej, periodically
compete for the resource with its currentPj/
njk=1
Vjk. In one competition, the file with the highestPj/
njk=1
Vjk wins and receives resource for one replica.After a file
creates a replica, its Pj/
njk=1
Vjk decreases.The competition stops when all available resource
isallocated and no one can win a competition. Thus, fileswith
larger Pj/
njk=1
Vjk win more competitions andreceive more resource and files
with smaller Pj/
njk=1
Vjkonly win few competitions and receive less resource.
Thecompetition gradually lets each file receive its deservedportion
of resource based on OFRR. By enabling fileowners to distributively
compete for resource for theirfiles, we can realize OFRR without a
central server.
Challenge 2: competition for distributed resource.In a MANET,
all available resource is scattered amongdifferent nodes moving
around in the network. Thisposes three problems. First, different
file owners arescattered and can hardly gather together to conduct
theresource competition. Second, after a file is replicated toa
number of nodes, it is difficult to collect the popularityof the
replicas to update the P of the file. Third, sincethe number of
nodes met by a file owner is limited, asingle file owner cannot
distribute replicas efficiently andquickly. We propose a
work-around for this problem.Specifically, we regard a file and its
newly created replicaas two different files, which participate in
further compe-tition independently with evenly split P . However,
thisbrings another challenge: since replica nodes of a file
arescattered in the network, how to ensure that the overall
-
0018-9340 (c) 2013 IEEE. Personal use is permitted, but
republication/redistribution requires IEEE permission.
Seehttp://www.ieee.org/publications_standards/publications/rights/index.html
for more information.
This article has been accepted for publication in a future issue
of this journal, but has not been fully edited. Content may change
prior to final publication. Citation information:
DOI10.1109/TC.2014.2308211, IEEE Transactions on Computers
8
Vjk is proportional to the overall P of the file? We
solve it in next subsection.Note the competition used in the
description is not
to show that resources are very limited. It is only toshow the
process of resource allocation, which can beviewed as a probability
based resource allocation algo-rithm. Such a solution increases the
complexity of thesystem. However, this is caused by the distributed
natureof MANETs. We will investigate how to reduce thecomplexity in
the next step. For example, we can checkwhether files can reduce
the frequency of competitionbut still get the deserved amount of
resources.
Solution to Challenge 2: distributive competition onselective
resources. In the solution to Challenge 1, eachfile periodically
competes for resource with its currentPj/
njk=1
Vjk. However, as previously mentioned, it isa challenge to keep
the overall P proportional to theoverall
Vjk while replica holders are scattered. We in-
directly resolve this problem by keeping the average V ofthe
replica nodes of a file close to V . Then, Formula (22)can be
re-expressed as
nj V qjbj nj
qjbj nj Pj (31)
In such a case, when the number of replicas of each fileis
proportional to its Pj =
qj/bj , OFRR is satisfied.
To attain this goal, we let each node deliberately selecta
neighbor node to create replicas of its file so that theaverage
meeting ability of replica nodes of the file isequal or closest to
V . Considering the diverse mobilityof nodes in the network, a node
should be able to findreplica nodes whose average meeting ability
equals Vduring its movement. Then, based on Equation (31),each node
only needs to consider the P of each file inthe resource
completion. Upon winning a competitionfor a file, a node splits the
files P evenly betweenthe file and the replica. After this, the
popularity ofeach file/replica is continuously updated based on
thenumber of requests received for it in a unit time period,which
is used to update its priority value P .
When a replica is deleted in the competition, wecannot reverse
the process of priority split because itis very difficult to track
locations of the holders ofthe original file in a distributed
manner due to themobility of nodes in MANETs. Fortunately, we can
usethe querying popularity q to handle this problem. Inthis case,
the qs (or P s) of other replicas of the fileincrease since they
receive more requests for the file asthe total amount of requests
is stable. That is, the sumof the replicas P s equals the overall P
of the originalfile j (Pi). The increase of priority value caused
by thereplica deletion can be regarded as the reversed processof
priority split. As a result, the number of replicas ofeach file is
proportional to the sum of meeting ability ofits replica nodes,
realizing Formula (22).
4.2 Design of the File Replication ProtocolThe two solutions to
handle the challenges in achievingOFRR described above are maximal
approximation to
File Priority competition
Replica creation &
priority split
Success
Try at most K times
Select one neighbor by the OFRR RULE
Failure
Fig. 2: Replica distribution process.
realize the OFRR in a distributed manner. Based on thesolutions,
we propose the Priority Competition and Splitfile replication
protocol (PCS). We first introduce how anode retrieves the
parameters needed in PCS and thenpresent the detail of PCS.
In PCS, each node dynamically updates its meetingability (Vi)
and the average meeting ability of all nodesin the system (V ).
Such information is exchanged amongneighbor nodes. We explain the
detail of this step inSection 4.3. Each node also periodically
calculates thePj =
qj/bj of each of its files. The qj is calculated by
qj = uj/U , where uj and U are the number of receivedrequests
for the file and the total number of queriesgenerated in a unit of
time period, respectively. Notethat U is a pre-defined system
parameter.
In the solution to Challenge 2, nodes replicate
filesdistributively and select replicate nodes to ensure thatthe
average meeting ability of replica nodes of a file theclosest to V
. That is, Vn
jV , where nj is the number of
created replicas of file j and Vnj
is the average meetingability of these replica nodes. Therefore,
each node needsto keep track of nj and Vnj of each of its file.
Aftercreating a replica, the node increases nj by 1 and
updatesVn
jusing the V of the new replica node.
With the above information, we introduce the processof the
replication of a file in PCS. Based on OFRR, since afile with a
higher P should receive more resource, a nodeshould assign higher
priority to its files with higher Pto compete resource with other
nodes. Thus, each nodeorders all of its files in descending order
of their P sand creates replicas for the files in a top-down
mannerperiodically. Algorithm 1 presents the pseudo-code forthe
process of PCS between two encountered nodes. Indetail, suppose
node i needs to replicate file j on thetop of the list, as shown in
Figure 2, it keeps trying toreplicate file j on nodes it encounters
until one replicais created or K attempts have been made. If file j
isreplicated, its P is split and it is inserted to its new placein
the list. Next, the node fetches the file from the top ofthe list
and repeats the process. If file j fails to replicateafter K
attempts, the node stops launching competitionuntil the next
period.
Following the solution to Challenge 2, a replicatingnode should
keep the average meeting ability of thereplica nodes for file j
around V . Node i first checksthe meeting abilities of neighbors
and then chooses theneighbor k that does not contain file j and
makes V newn
j=
(njVnj +Vk)/(nj +1) the closest to V as the replica node
candidate. It is possible that V newnj
is far away from V .Therefore, we set a deviation range r. If
creating a replicain the selected neighbor makes (V newn
jV ) > r, then the
-
0018-9340 (c) 2013 IEEE. Personal use is permitted, but
republication/redistribution requires IEEE permission.
Seehttp://www.ieee.org/publications_standards/publications/rights/index.html
for more information.
This article has been accepted for publication in a future issue
of this journal, but has not been fully edited. Content may change
prior to final publication. Citation information:
DOI10.1109/TC.2014.2308211, IEEE Transactions on Computers
9
node does not replicate file j in the selected neighboruntil it
has a different set of neighbors.
In the case that (V newnj V ) r, if the selected
neighbors available storage is larger than the size of filej
(Sj), it creates a replica for file j directly. Otherwise,a
competition is launched among the replica of file jand replicas
already in the neighbor node based ontheir P s. The priority value
of the new replica is set tohalf of the original files P .
According to the solutionto Challenge 1, the probability that a
replica wins theresource competition is proportional to its P ,
i.e., areplicas probability of being selected to be removedis
inversely proportional to its P . Then, suppose thereare d replicas
in competition, we let each replica beresponsible for a range that
equals its 1/P in range space[0,
dk=1 1/Pk]. The neighbor node randomly chooses a
number in [0,d
k=1 1/Pk], and the replica whose rangeowns the number is
selected to be removed. The neigh-bor node repeats above process
until available storage isno less than the size of file j.
If file j is among the selected files, it fails the compe-tition
and will not be replicated in the neighbor node.Otherwise, all
selected files are removed and file j isreplicated. If file j
fails, node i will launch anotherattempt for file j until the
maximum number of attempts(K) is reached. The setting of K attempts
is to ensure thateach file can compete with a sufficient subset of
replicasin the system. If node i fails to create a replica for file
jafter K attempts, then replicas in node i with smaller P sthan
file j are unlikely to win a competition. Thus, at thismoment, node
i stops replicating files until next round.Finally, all available
resource in the system is allocatedto replicas according to their P
s (i.e., OFRR is realized).
According to the Solution to Challenge 2, we regardfile js
replica as a different file from file j in PCS.Therefore, if node i
successfully creates a replica forfile j, it splits the files P
evenly between file j andthe new replica. Thus, each files priority
is P/2. Afterthe splitting, the two copies of file j involve in
furtherresource competition independently. Note that we do notsplit
files in the PCS algorithm but split the priority valueof a file
when a replica is created.
The replication for a file stops when the communi-cation session
of the two involved nodes ends. Then,the node will continue the
replication process for thefile again after excluding the
disconnected node fromthe neighbor node list. Since the popularity
of filespopularity and P s and available system resource changeas
time goes on, each node periodically executes PCSto dynamically
handle these time-varying factors. Eachnode also periodically
calculates the popularity of itsfiles (qj) to reflect the changes
on file popularity (dueto node querying pattern and rate changes)
in differenttime periods. The periodical file popularity update
canautomatically handle file dynamism. The popularity ofnewly added
files will be calculated and hence these fileswill be considered in
resource allocation. Similarly, thoseof deleted files will not be
calculated and hence these filewill not be considered in resource
allocation.
Algorithm 1 Pseudo-code of PCS between node i and
k.i.createReplicasOn(k) //node i tries to create a replica on node
kk.createReplicasOn(i) //node k tries to create a replica on node
iProcedure createReplicasOn (node)
nCount 0 //initialize a countthis.orderFilesByP() //order files
by priority valueFor (each file f in current node) //try to replica
each file
If (node.compete4File(f) == true)
//competitionnode.createAReplica4(f) //create a replica if win
elsenCount nCount+1
If nCount K //try at most K timesBreak
end ProcedureProcedure compete4File() //Compete for file j
While (nRemainningMem < j.size())nSum nTotal nRandom fFile 0
//initilizationFor (each file f (including j) in current node)
nTotal nTotal+1/PfnRandom generateARandomNumber() % nTotalFor
(each file f (including j) in current node)
nSum nSum+1/PfIf (nSum >= nRandom)
fFile = f Break //pick the fileIf (fFile = j) //j is the picked
file, competition fails
return falseElse //win the competition
select fFiledelSelectedFiles() //delete the selected filesreturn
true
end Procedure
4.3 How to Collect Meeting Ability InformationIn a MANET, nodes
periodically exchange beacon mes-sages to discover neighbor nodes.
The frequency of thebeacon messages depends on the mobility of
nodes. Thesize of a beacon message usually is several bytes. To
savecommunication cost, the values of Vi and V are piggy-backed
into beacon messages. Since Vi and V are onlyseveral bytes, the
piggybacking only slightly increasesthe size of the beacon message.
In normal MANETs,a nodes meeting ability (Vi) is simply measured
bythe frequency it meets other nodes. In disconnectedMANETs, a node
needs to know the distribution ofdifferent communities to calculate
its satisfying ability(Equation (23)). We then let each node
piggyback itscommunity ID and the community information it knowsin
the beacon message. Also, its hard to collect thesatisfying
abilities of all nodes in distributed MANETsin a timely manner
since nodes are sparsely distributed.We let each node simply use
the average meeting abilityof all so far encountered nodes as that
for all nodes inthe system. As nodes meet more and more nodes,
thecalculated value can generally represent that of all nodes.
4.4 Analysis of the Effectiveness of PCSIn this section, we
briefly prove the effectiveness of PCS.We refer to the process in
which a node tries to copy afile to its neighbors as one round of
replica distribution.
Recall that when a replica is created for a file with P ,the two
copies will replicate files with priority P/2 inthe next round.
This means that the creation of replicaswill not increase the
overall P of the file. Also, aftereach round, the priority value of
each file or replica is
-
0018-9340 (c) 2013 IEEE. Personal use is permitted, but
republication/redistribution requires IEEE permission.
Seehttp://www.ieee.org/publications_standards/publications/rights/index.html
for more information.
This article has been accepted for publication in a future issue
of this journal, but has not been fully edited. Content may change
prior to final publication. Citation information:
DOI10.1109/TC.2014.2308211, IEEE Transactions on Computers
10
updated based on the received requests for the file. Then,though
some replicas may be deleted in the competition,the total amount of
requests for the file remains stable,making the sum of the Ps of
all replicas and the originalfile roughly equal to the overall
priority value of thefile. Then, we can regard the replicas of a
file as anentity that competes for available resource in the
systemwith accumulated priority P in each round. Therefore, ineach
round of replica distribution, based on our designof PCS, the
overall probability of creating a replica foran original file j,
denoted by Psj , is proportional to itsoverall Pj . That is:
Psj Pj (32)Then, suppose total M rounds of competition are
con-ducted, the expected number of replicas, denoted by nj ,for
file j is
nj =MPsj nj Pj (33)Therefore, we conclude that the PCS can
realize Equation(31), in which the number of replicas of each file
isproportional to its P , thereby realizing the OFRR.
We further briefly discuss the security and
incentiveconsiderations for PCS in Appendix B .
5 PERFORMANCE EVALUATION IN NORMALMANETS WITH THE RWP MODELTo
evaluate the performance of PCS in normal MANETs,we conducted
experiments on both the GENI Orbittestbed [37], [38] and the NS-2
[39] simulator. TheGENI testbed consists of 400 nodes equipped with
wire-less cards. We used the Dartmouth real-world MANETtrace [34],
which provides the mobility trace of 35 laptopsmoving in an open
field, to drive node mobility in bothexperiments. In order to
validate the adaptability ofPCS, we used two routing protocols in
the experiments.We first used the StaticWait protocol [40] in the
GENIexperiment, in which each query stays on the sourcenode waiting
for the destination. We then used a proba-bilistic routing protocol
(PROPHET) [6], in which a noderoutes requests to the neighbor with
the highest meetingability. We set a larger TTL for Static Wait
since it needsmore time to find a file holder. We used 95%
confidenceinterval when handling the experimental results.
We evaluated the performance of PCS in normalMANETs in
comparison with several MANET replica-tion algorithms: SAF [10],
DCG [10], PDRS [12] andCACHE [9]. The details of these protocols
can be foundin Section 2. To better validate our analysis, we
alsocompared PCS with Random, which places replicas onnodes
randomly, and OPTM, which is a centralizedprotocol that calculates
the ideal number of replicas foreach file based on our derived
optimal replication rule.OPTM represents the best possible
performance can beobtained by the OFRR. In order to evaluate our
protocolunder different network sizes and node mobilities, wealso
conducted simulation on the NS-2 with differentnetwork sizes and
node mobilities synthesized by themodified RWP model. Due to page
limit, the results ofthese tests are shown in Appendix A.
Table 2 shows the parameters used in experiments,unless
otherwise specified. The parameters are deter-mined by referring to
the settings in [9], [41] and the realtrace. According to the works
in [9], [42], we determinedthe file size and storage space on each
node. As thework in [33], the probability of originating requests
fordifferent files in each node followed a Zipf distributionand the
Zipf parameter was set to 0.7. Initially, fileswere evenly
distributed to each node and no replicaexisted in the system. In
the synthesized mobility, thespeed of a node was randomly chosen
from the rangeof [s/2, 3s/2], where s is the configured average
nodemovement speed. Since the real trace does not indicatethe
communication range of each node, we set thecommunication range to
100m in the simulation and to60m in the GENI experiment in order to
see the influenceof different transmission ranges on the
performance. Weevaluated the performance of PCS with K = 3.
We used the following metrics in the experiments: Hit Rate. This
refers to the percent of requests that
are successfully resolved by either original filesor replicas.
This metric shows the effectiveness ofreplication protocols in
enhancing file availability.
Average delay. This is the average delay of all re-quests. To
make the comparison fair, we included allrequests in the
calculation. For unresolved requests,we set their delays as the
TTL. This metric showsthe efficiency of replication protocols in
terms of filequerying delay.
Replication cost. This is the total number of messagesgenerated
in creating replicates. This metric showsthe overhead of
replication protocols.
Cumulative Distribution Function (CDF) of the propor-tion of
replicas. This is the CDF of the proportion ofreplicas of each
file. This metric reflects the amountof resource allocated to each
file for replication.
TABLE 2: Simulation parameters.Real trace Synthesized
mobility
Environment Parameters GENI / NS-2 NS-2Simulation area 600m 300m
1000m 1000mNode ParametersNumber of nodes 35 60Communication range
60m / 100m 250mAverage movement speed - 6m/sThe size of a file (kb)
1 10 1 10Number of files in each node 10 10Storage space for
replicas (kb) 50 50Query ParametersInitialization period 500s /
800s 200sQuerying period 1500s / 1200s 600sTTL of each request
1000s / 200s 200sTotal time for each test 3000s / 3000s 1000s
5.1 Performance in the Trace-Driven GENI experi-ments5.1.1 Hit
Rate and Average DelayTable 3 shows the results of each protocol in
the trace-driven experiments on GENI. We see that the hit rates
indifferent replication protocols follow RandomOPTM. We see that
OPTM and PCS
-
0018-9340 (c) 2013 IEEE. Personal use is permitted, but
republication/redistribution requires IEEE permission.
Seehttp://www.ieee.org/publications_standards/publications/rights/index.html
for more information.
This article has been accepted for publication in a future issue
of this journal, but has not been fully edited. Content may change
prior to final publication. Citation information:
DOI10.1109/TC.2014.2308211, IEEE Transactions on Computers
11
lead to higher hit rate and lower average delay thanothers. This
is attributed to the guidance of OFRR,which aims to minimize the
average querying delay byconsidering both storage and meeting
ability as resourceto enhance overall file availability. PCS
generates slightlylower hit rate and around 20% higher average
delay thanOPTM. This is because OPTM has the knowledge of
allinformation needed in OFRR beforehand, while PCS hasto
distribute replicas in a fully distributed manner.
On the contrary, other protocols only replicate fileslocally,
creating redundant replicas and failing to achievehigh file
availability under node mobility. Random hasthe worst performance
on hit rate and average delay.This is because Random only randomly
creates replicasfor files and fails to assign more resources to
popu-lar files, which are queried more frequently by nodes.CACHE
only utilizes the storage on intersection nodes,which indicates
that it fails to fully utilize storage spacein all nodes.
Therefore, it cannot create as many replicasas other protocols and
exhibits a low hit rate and ahight delay. In SAF, each node
replicates its frequentlyqueried files until its memory is filled
up. Then, almostall resources are allocated to popular files.
Therefore,SAF cannot optimize query delay globally. In PDRS,a node
replicates files interested by its neighbors thathave less storage
resource than itself. However, as thesharing of replicas is not in
the whole group, PDRS onlyrenders a slightly performance
improvement over SAF.DCG further improves SAF and PDRS by
conducting thefile replication on a group level. It eliminates
duplicatereplicas among group members and uses released mem-ory for
other replicas, thereby generating higher hit rateand smaller
average delay.
We find that the 1st percentiles of the delays of allprotocols
are 0.01. This is because some requests areimmediately satisfied by
direct neighbors. The 99th per-centiles of the delays of the
protocols approximatelyfollow the relationship on average delay.
Above resultsjustify that PCS enhances the file searching
efficiency byits global optimization of file availability. The fact
thatRandom leads to worse performance than all methodsthat give
priority to popular files when creating replicasalso justify that a
resource allocation strategy is neces-sary for file availability
optimization.
TABLE 3: Experimental results of the trace-driven GENI
experiments.Protocol Hit rate Average / 1% / 99% delay (s)
Replication costRandom 0.840139 263.176 / 0.01 / 991.9843
13387CACHE 0.842454 260.469 / 0.01 / 994.2487 0SAF 0.857341
259.1768 / 0.01 / 997.1095 0PDRS 0.863074 256.1983 / 0.01 /
991.2384 175140DCG 0.878559 251.3287 / 0.01 / 993.3947 67549PCS
0.898823 240.7031 / 0.01 / 990.4522 28983OPTM 0.910370 195.1776 /
0.01 / 990.1296 14542
5.1.2 Replication Cost
From the table, we find that the replication costs ofdifferent
protocols follow PDRS>DCG>PCS>OPTMRandom>SAF=CACHE=0.
PDRS shows the highestreplication cost because it needs to
broadcast each newfile to all nodes in the system. DCG incurs
moderate
replication cost because group members need to ex-change
information to reduce duplicate replicas. PCS hasa low replication
cost because each node only tries atmost K times to create a new
replica for each file itholds. OPTM and Random have a very low cost
sincenodes only need to communicate with the central serverfor
replica list. SAF and CACHE have no replication costsince they do
not need to exchange information amongnodes for file replication.
However, SAF generates a lotof redundant replicas, and Random and
CACHE lead tolow performance.
5.1.3 Replica DistributionFigure 3 shows the CDF of the
proportion of re-
source allocated to each file for replica creation in dif-ferent
protocols. From the figure, we find that PCSexhibits the closest
similarity to OPTM while otherprotocols follow:
DCGRandomCACHEPDRSSAF,where means closer similarity to OPTM.
Combining
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0 35 70 105 140 175 210 245 280 315
CDFofth
eprop
ortio
nofre
plicas
Filesequenceindecreasingorderofpopularity
PCS DCGSAF CACHEOPTM PDRSRandom
PCS
DCG
OPTM
SAF
PDRSCACHE Random
Fig. 3: CDF of the resource allo-cated to replicas in
trace-drivenGENI experiment.
the results on average de-lay, we find an interest-ing
phenomenon: exceptCACHE and Random, aprotocol with closer
sim-ilarity to OPTM has lessaverage delay. This provesthe
correctness of our the-oretical analysis and theresultant OFRR rule
ex-pressed in Formula (22).CACHE has a low performance because it
does notutilize all storage space, though it exhibits similarity
withPDRS. Random creates replicas for each file randomlywithout
considering their popularity, leading to a lowperformance since
popular files are not replicated withpriority. We also observe that
the CDFs of the proportionof resource allocated to replicas of DCG,
CACHE, PDRSand SAF increases to 0.9 quickly. This is because
theyallocate most resources to popular files, resulting in alot of
replicas for these files. Though these protocols canreduce the
delay of queries for popular files, they cannotreduce the delay for
unpopular files. PCS is superiorover these protocols because it can
globally reduce thequery delay for all files.
5.2 Performance in the Trace-Driven Simulation5.2.1 Hit Rate and
Average DelayTable 4 shows the results of each protocol in the
trace-driven experiments on NS-2. We see the hit rates andaverage
delays of the seven protocols follow the samerelationship as in
Table 3 due to the same reasons. Wefind that the average delays of
the seven protocols aremuch less than those in the GENI experiment.
This iscaused by two reasons. First, the trace-driven
simulationadopts the PROPHET for file searching, which can
locatefiles more quickly than the StaticWait searching protocolused
in the GENI experiment. Second, the communi-cation range of two
nodes (100m) in the simulation islarger than that in the GENI
experiment (60m), leading
-
0018-9340 (c) 2013 IEEE. Personal use is permitted, but
republication/redistribution requires IEEE permission.
Seehttp://www.ieee.org/publications_standards/publications/rights/index.html
for more information.
This article has been accepted for publication in a future issue
of this journal, but has not been fully edited. Content may change
prior to final publication. Citation information:
DOI10.1109/TC.2014.2308211, IEEE Transactions on Computers
12
to shorter searching delay since a node can reach moreneighbors.
The hit rates of the seven protocols are lowerthan those in the
GENI experiment. This is because thetrace-driven simulation used
much smaller TTL. Therelative performance between different
protocols in thesimulation matches that in the GENI experiment,
whichfurther proves the effectiveness of PCS.
5.2.2 Replication CostFrom Table 4, we find that the replication
costsof different protocols follow
PDRS>DCG>PCS>OPTMRandom>SAF=CACHE=0. This matches the
results inTable 3 and the reasons are the same.
5.2.3 Replica DistributionFigure 4 shows the CDF of the
proportion of
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0 35 70 105 140 175 210 245 280 315
CDFofth
eprop
ortio
nofre
plicas
Filesequenceindecreasingorderofpopularity
PCS DCGSAF CACHEOPTM PDRSRandom
PCS
DCG
Random
SAFPDRS
CACHE
OPTM
Fig. 4: CDF of the resource allo-cated to replicas in
trace-drivensimulation.
resource allocated to repli-cas of each file in theseven
protocols. From thefigure, we find similartrend as that in Figure
3.That is, except CACHEand Random, a proto-col with closer
similar-ity to OPTM has less av-erage delay. This furtherproves the
correctness ofour analysis through the trace-driven simulation.
TABLE 4: Simulation results of the trace-driven
experiments.Protocol Hit rate Average / 1% / 99% delay (s)
Replication costRandom 0.828652 67.9564 / 0.00175637 / 193.259
4695CACHE 0.830038 64.6417 / 0.00172859 / 191.703 0SAF 0.837664
62.1525 / 0.00172887 / 190.896 0PDRS 0.842982 61.0969 / 0.00172652
/ 191.279 246454DCG 0.848559 59.0611 / 0.00172883 / 189.270
14510PCS 0.868749 50.2859 / 0.00172885 / 188.550 9846OPTM 0.878677
41.2282 / 0.00172874 / 188.428 4721
6 PERFORMANCE EVALUATION IN DISCON-NECTED MANETS WITH THE
COMMUNITY-BASED MOBILITY MODELIn order to evaluate the performance
of PCS in dis-
connected MANETs, we conducted event-driven exper-iments with
the MIT Reality project [35] trace and theHaggle project [36]
trace. The MIT Reality trace lastsabout 2.56 million seconds (Ms),
while the Haggle projecttrace lasts about 0.34 Ms. Both traces
represent typi-cal disconnected MANET scenarios. We used the
StaticWaiting routing protocol [40] in this test.
We evaluated the performance of PCS in comparisonwith DCG [10],
CACHE-DTN [14], OPTM, and Random.CACHE-DTN is a caching algorithm
for DTNs. It cacheseach file in the central node of each network
centerlocation (NCL). If a central node is full, its replicas
arestored in its neighbor nodes according to their popu-larity. A
higher popular replica is stored closer to thecentral node. The
experiment settings and measurementmetrics are the same as in
Section 5 unless otherwisespecified below. The total number of
queries was set to6000Rp, and Rp is the query rate and was varied
in the
range of [2, 6]. In the experiment with the Haggle traceand the
MIT Reality trace, all queries were generatedevenly in the time
period of [0.3Ms, 2.3Ms] and [0.05Ms,0.25Ms], and the TTL of each
query was set to 0.3Msand 0.04Ms, respectively. We again adopted
the 95%confidence interval when handling experimental data.
6.1 Hit Rate
Figure 5(a) and Figure 6(a) plot the hit rates ofthe five
methods with the Haggle trace and theMIT Reality trace,
respectively. We see that in bothscenarios, the hit rates follow
OPTM>PCS>CACHE-DTN>DCG>Random. OPTM and PCS achieve
higherhit rate than other methods because they follow thededuced
OFRR. However, since PCS realizes OFRR in adistributed way, it
presents slightly inferior performancecompared to OPTM. CACHE-DTN
considers the inter-mittent connection properties of disconnected
MANETsand replicates each file to every NCL, leading to highdate
accessibility, though not optimal. DCG only con-siders temporary
connected group for data replication,which is not stable in
disconnected MANETs. Therefore,it has a low hit rate. Random
assigns resources to filesrandomly, which means it cannot create
more replicasfor popular files, leading to the lowest hit rate.
Such aresult proves the effectiveness of the proposed PCS
onimproving the overall file availability and the correctnessof our
derived OFRR for disconnected MANETs.
We also see that the hit rates of different methodsfluctuate
slightly when the query rate increases. Thisis because the hit rate
is not affected by the queryrate. Even when the number of query
increases, thefile availability remains on the same level and leads
tosimilar hit rates, as shown in the two figures.
6.2 Average Delay
Figure 5(b) and Figure 6(b) demonstrate the averagedelays of the
five methods with the Haggle trace and theMIT Reality trace,
respectively. We find that with bothtraces, the average delays
follow OPTM
-
0018-9340 (c) 2013 IEEE. Personal use is permitted, but
republication/redistribution requires IEEE permission.
Seehttp://www.ieee.org/publications_standards/publications/rights/index.html
for more information.
This article has been accepted for publication in a future issue
of this journal, but has not been fully edited. Content may change
prior to final publication. Citation information:
DOI10.1109/TC.2014.2308211, IEEE Transactions on Computers
13
0.63
0.66
0.69
0.72
0.75
0.78
0.81
0.84
2 3 4 5 6
Hitrate
Queryrate
PCS DCGCACHEDTN OPTMRandom
(a) Hit rate.
16
20
24
28
32
2 3 4 5 6
Averagede
lay(x10
3 s)
Queryrate
PCS DCGCACHEDTN OPTMRandom
(b) Average delay.
8.0E+05
2.8E+06
4.8E+06
6.8E+06
8.8E+06
2 3 4 5 6
Replicationcost
Queryrate
PCS DCGCACHEDTN OPTMRandom
(c) Replication cost.
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0 110 220 330 440 550 660 770 880
CDFofth
eprop
ortio
nofre
plicas
Filesequenceindecreasingorderofpopularity
PCS DCGCACHEDTN OPTMRandom
(d) CDF of allocated resources.
Fig. 5: Performance of the file replication protocols with the
Haggle trace.
0.40
0.44
0.48
0.52
0.56
0.60
0.64
0.68
2 3 4 5 6
Hitrate
Queryrate
PCS DCGCACHEDTN OPTMRandom
(a) Hit rate.
22
25
28
31
34
37
2 3 4 5 6
Averagede
lay(x10
4 s)
Queryrate
PCS DCGCACHEDTN OPTMRandom
(b) Average delay.
8.0E+05
2.8E+06
4.8E+06
6.8E+06
2 3 4 5 6
Replicationcost
Queryrate
PCS DCGCACHEDTN OPTMRandom
(c) Replication cost.
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0 110 220 330 440 550 660 770
CDFofth
eprop
ortio
nofre
plicas
Filesequenceindecreasingorderofpopularity
PCS DCGCACHEDTN OPTMRandom
(d) CDF of allocated resources.
Fig. 6: Performance of the file replication protocols with the
MIT Reality trace.
6.3 Replication CostFigure 5(c) and Figure 6(c) show the
replication costsof the five methods with the Haggle trace and the
MITReality trace, respectively. OPTM and Random have thelowest
replication cost while the costs of the other threemethods follow
PCS
-
0018-9340 (c) 2013 IEEE. Personal use is permitted, but
republication/redistribution requires IEEE permission.
Seehttp://www.ieee.org/publications_standards/publications/rights/index.html
for more information.
This article has been accepted for publication in a future issue
of this journal, but has not been fully edited. Content may change
prior to final publication. Citation information:
DOI10.1109/TC.2014.2308211, IEEE Transactions on Computers
14
protocols that only consider storage space as resource,we also
consider file holders ability to meet nodes asavailable resource
since it also affects the average query-ing delay. This new concept
enhances the correctness ofthe deduced rule and the effectiveness
of the accordinglydeveloped replication protocol. Finally, we
designed thePriority Competition and Split replication protocol
(PCS)that realizes the proposed optimal replication rule in afully
distributed manner. Extensive experiments on bothreal-world GENI
testbed, NS-2, and event-driven simula-tor with real trace and
synthesized mobility confirm boththe correctness of our theoretical
analysis and the effec-tiveness of PCS in MANETs. In this study, we
focus on astatic set of files in the network. In our future work,
wewill theoretically analyze a more complex environmentincluding
file dynamics (file addition and deletion, filetimeout) and dynamic
node querying pattern.
ACKNOWLEDGMENTThis research was supported in part by U.S. NSF
grantsOCI-1064230, CNS-1049947, CNS-1025652,
CNS-1025649,CNS-1057530 and CNS-0917056, Microsoft Research
Fac-ulty Fellowship 8300751, and Sandia National Laborato-ries
grant 10002282.
REFERENCES[1] Qik, http://qik.com/.[2] Flixwagon,
http://www.flixwagon.com/.[3] C. Palazzi and A. Bujari, A
delay/disruption tolerant solution
for mobile to mobile file sharing. in Proc. of IFIP/IEEE
WirelessDays, 2010.
[4] Y. Tseng, S. Ni, and E. Shih, Adaptive approaches to
relievingbroadcast storms in a wireless multihop mobile ad hoc
network,in Proc. of ICDCS, 2001, pp. 481488.
[5] B. Chiara, C. Marco, and et al., Hibop: A history based
routingprotocol for opportunistic networks, in Proc. of WoWMoM,
2007.
[6] A. Lindgren, A. Doria, and O. Schelen, Probabilistic routing
inintermittently connected networks, MC2R, vol. 7, no. 3, pp. 1920,
2003.
[7] F. Li and J. Wu, MOPS: Providing content-based service
indisruption-tolerant networks, in Proc. of ICDCS, 2009.
[8] S. Moussaoui, M. Guerroumi, and N. Badache, Data
replicationin mobile ad hoc networks, in Proc. of MSN, 2006, pp.
685697.
[9] L. Yin and G. Cao, Supporting cooperative caching in ad
hocnetworks, TMC, vol. 5, no. 1, pp. 7789, 2006.
[10] T. Hara and S. K. Madria, Data replication for improving
dataaccessibility in ad hoc networks, TMC, vol. 5, no. 11, pp.
15151532, 2006.
[11] J. Zheng, J. Su, K. Yang, and Y. Wang, Stable neighbor
basedadaptive replica allocation in mobile ad hoc networks, in
Proc.of ICCS, 2004.
[12] H. Duong and I. Demeure, Proactive data replication
semanticinformation within mobility groups in MANET, in Proc.
ofMobilware, 2009.
[13] Y. Huang, Y. Gao, and et al., Optimizing file retrieval in
delay-tolerant content distribution community, in Proc. of ICDCS,
2009.
[14] W. Gao, G. Cao, A. Iyengar, and M. Srivatsa, Supporting
cooper-ative caching in disruption tolerant networks. in Proc. of
ICDCS,2011.
[15] J. Reich and A. Chaintreau, The age of impatience: optimal
repli-cation schemes for opportunistic networks. in Proc. of
CoNEXT,2009.
[16] S. Ioannidis, L. Massoulie, and A. Chaintreau,
Distributedcaching over heterogeneous mobile networks. in Proc. of
SIG-METRICS, 2010.
[17] M. J. Pitkanen and J. Ott, Redundancy and distributed
cachingin mobile DTNs, in Proc. of MobiArch, 2007.
[18] X. Zhuo, Q. Li, W. Gao, G. Cao, and Y. Dai, Contact
durationaware data replication in delay tolerant networks. in Proc.
ofICNP, 2011.
[19] X. Zhuo, Q. Li, G. Cao, Y. Dai, B. K. Szymanski, and T. L.
Porta,Social-based cooperative caching in DTNs: A contact
durationaware approach. in Proc. of MASS, 2011.
[20] Z. Li and H. Shen, Sedum: Exploiting social networks in
utility-based distributed routing for DTNs, TC, 2012.
[21] V. Gianuzzi, Data replication effectiveness in mobile
ad-hocnetworks, in Proc. of PE-WASUN, 2004, pp. 1722.
[22] S. Chessa and P. Maestrini, Dependable and secure data
storageand retrieval in mobile wireless networks, in Proc. of DSN,
2003.
[23] X. Chen, Data replication approaches for ad hoc wireless
net-works satisfying time constraints, IJPEDS, vol. 22, no. 3, pp.
149161, 2007.
[24] J. Broch, D. A. Maltz, D. B. Johnson, Y. Hu, and J. G.
Jetcheva, Aperformance comparison of multi-hop wireless ad hoc
networkrouting protocols, in Proc. of MOBICOM, 1998, pp. 8597.
[25] M. Musolesi and C. Mascolo, Designing mobility models
basedon social network theory, MCCR, vol. 11, pp. 5970, 2007.
[26]
Http://web.informatik.uni-bonn.de/IV/BoMoNet/BonnMotion.htm.[27] P.
Costa, C. Mascolo, M. Musolesi, and G. P. Picco, Socially-
aware routing for publish-subscribe in delay-tolerant mobile
adhoc networks, IEEE JSAC, vol. 26, no. 5, pp. 748760, 2008.
[28] M. Musolesi and C. Mascolo, Car: Context-aware adaptive
rout-ing for delay-tolerant mobile networks. TMC, 2009.
[29] H. Cai and D. Y. Eun, Crossing over the bounded domain:
fromexponential to power-law inter-meeting time in MANET. in
Proc.of MOBICOM, 2007.
[30] R. Groenevelt, P. Nain, and G. Koole, The message delay
inmobile ad hoc networks. Perform. Eval., vol. 62, pp. 210228,
2005.
[31] G. Sharma, R. Mazumdar, and N. B. Shroff, Delay and
capacitytrade-offs in mobile ad hoc networks: A global perspective.
inProc. of INFOCOM, 2006.
[32] L. Kleinrock, Queueing Systems, Volume II: Coputer
Applications.John Wiley & Sons, 1976.
[33] J. Kangasharju, K. W. Ross, and D. A. Turner, Optimizing
fileavailability in peer-to-peer content distribution, in Proc. of
IN-FOCOM, 2007.
[34] R. S. Gray, D. Kotz, C. Newport, N. Dubrovsky,A. Fiske, J.
Liu, C. Masone, S. McGrath, and Y. Yuan,CRAWDAD data set
dartmouth/outdoor (v.
2006-11-06),http://crawdad.cs.dartmouth.edu/dartmouth/outdoor.
[35] N. Eagle, A. Pentland, and D. Lazer, Inferring social
networkstructure using mobile phone data, PNAS, vol. 106, no. 36,
2009.
[36] A. Chaintreau, P. Hui, J. Scott, R. Gass, J. Crowcroft, and
C. Diot,Impact of human mobility on opportunistic forwarding
algo-rithms, in Proc. of INFOCOM, 2006.
[37] GENI project, http://www.geni.net/.[38] Orbit,
http://www.orbit-lab.org/.[39] The Network Simulator ns-2,
http://www.isi.edu/nsnam/ns/.[40] T. Spyropoulos, K. Psounis, and
C. Raghavendra, Efficient rout-
ing in intermittently connected mobile networks: The
single-copycase, ACM/IEEE Transactions on Networking, 2007.
[41] M. Lu and J. Wu, Opportunistic routing algebra and its
applica-tion, in Proc. of INFOCOM, 2009.
[42] T. Hara, Effective replica allocation in ad hoc networks
forimproving data accessibility, in Proc. of INFOCOM, 2001.
[43] Z. Li and H. Shen, Analysis of cooperation incentive
strategiesin mobile ad hoc networks, TMC, 2012.
[44] B. Chen and M. C. Chan, MobiCent: a credit-based
incentivesystem for disruption tolerant network. in Proc. of
INFOCOM,2010.
Kang Chen Kang Chen received the BS degreein Electronics and
Information Engineering fromHuazhong University of Science and
Technol-ogy, China in 2005, and the MS in Communica-tion and
Information Systems from the Gradu-ate University of Chinese
Academy of Sciences,China in 2008. He is currently a Ph.D studentin
the Department of Electrical and ComputerEngineering at Clemson
University. His researchinterests include mobile ad hoc networks
anddelay tolerant networks.
Haiying Shen received the BS degree in Computer Science and
Engineering from Tongji University, China in 2000, and the MS and
Ph.D. degrees in Computer Engineering from Wayne State University
in 2004 and 2006, respectively. She is currently an Assistant
Professor in the Holcombe Department of Electrical and Computer
Engineering at Clemson University. Her research interests include
distributed and parallel computer systems and computer networks,
with an emphasis on peer-to-peer and content delivery networks,
mobile computing, wireless sensor networks, and grid and cloud
computing. She was the Program Co-Chair for a number of
international conferences and member of the Program Committees of
many leading conferences. She is a Microsoft Faculty Fellow of 2010
and a member of the IEEE and ACM.
Cheng-Zhong Xu received B.S. and M.S. degrees from Nanjing
University in 1986 and 1989, respectively, and a Ph.D. degree in
Computer Science from the University of Hong Kong in 1993. He is
currently a Professor in the Department of Electrical and Computer
Engineering of Wayne State University and the Director of Suns
Center of Excellence in Open Source Computing and Applications. His
research interests are mainly in distributed and parallel systems,
particularly in scalable and secure Internet services, autonomic
cloud management, energy-aware task scheduling in wireless embedded
systems, and high performance cluster and grid computing. He has
published more than 160 articles in peer-reviewed journals and
conferences in these areas. He is the author of Scalable and Secure
Internet Services and Architecture (Chapman & Hall/CRC Press,
2005) and a co-author of Load Balancing in Parallel Computers:
Theory and Practice
Haiying Shen Haiying Shen received the BSdegree in Computer
Science and Engineeringfrom Tongji University, China in 2000, and
theMS and Ph.D. degrees in Computer Engineeringfrom Wayne State
University in 2004 and 2006,respectively. She is currently an
Assistant Pro-fessor in the Department of Electrical and Com-puter
Engineering at Clemson University. Herresearch interests include
distributed computersystems and computer networks, with an
em-phasis on P2P and content delivery networks,
mobile computing, wireless sensor networks, and cloud computing.
Sheis a Microsoft Faculty Fellow of 2010, a senior member of the
IEEE anda member of the ACM.