Peer-to-Peer Systems
Winter semester 2014
Jun.-Prof. Dr.-Ing. Kalman Graffi
Heinrich Heine University Düsseldorf
Peer-to-Peer Systems
Monitoring the Quality of Peer-to-Peer Systems
– Introduction to P2P Quality Monitoring
– Gossip-based solutions
– Tree-based solutions
3 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
Quality of Service in P2P Networks
Quality of service (QoS)
“The well-defined and controllable behavior of a system with
respect to quantitative parameters”
Challenges for providing (constant) quality in p2p systems:
Various scenarios
• Distributed storage
• Content delivery
• Discovery and contacting of users
Dynamics over time
• Network size
• Churn
Peer heterogeneity
• Peer capacities
• Connectivity
User
Overlay
Application
Devices
Network
Manage-ment
4 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
Monitoring and Controlling System Metrics
Examples: Statistics: minimum, maximum, average, standard deviation
P2P system: peer count, online time,
Overlay “metrics”: Hop count, routing delay
Overhead: bandwidth consumption and traffic (per message type)
Resources: Free / used CPU, memory, storage space, bandwidth
Monitoring Obtain (global) knowledge on the system statistics
• Aggregatable: Size of statistics for 10 or 1000 peers equal
• Statistics must be fresh, monitoring mechanism of low overhead
Management / control Modify system to meet desired QoS properties
• Example: User defines valid interval for lookup delay
• Control mechanism to be fast responding and stable
5 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
Motivation for Monitoring and Management
Monitoring:
Information on system status can be used for optimized decisions
• E.g. peer count defines size of time-to-live
• E.g. churn pattern defines stabilization frequency
Necessary to identify (bad) quality of mechanisms
• Too much overhead
• Too slow routing
• Efficiency leaks
Helps in designing better mechanisms
Management / Controlling
Allows automated adaptation of system performance
Maintains quality level under uncontrollable dynamics
Adaptation only on-the-fly, no re-deployment possible
Both: Essential for commercial use
6 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
Deployment and Running a P2P System
Is everything running fine?
How to debug and to gain insight?
How to improve the running system?
Underlay:
The Internet
Structured
Overlay: DHT
H(„my data“)
= 3107
2207
7.31.10.25
peer-to-peer.info
12.5.7.31
95.7.6.10
86.8.10.18
planet-lab.orgberkeley.edu
29063485
201116221008
709
611
89.11.20.15
?
7 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
P2P Monitoring
Goal: Obtaining statistics on the global system Input: local states 𝑥𝑖(𝑡) of peers 𝑝𝑖 at time 𝑡 Output: global status (aggregate) at time t: 𝐹 𝑡 = 𝑓(𝑥1 𝑡 , … , 𝑥𝑛 𝑡 )
Statistical information: Aggregatable: average, minimum, maximum, sum, standard deviation
More difficult: median, top 1000 …
Aggregate function: 𝑓 𝑑𝑜𝑢𝑏𝑙𝑒∗ → 𝑑𝑜𝑢𝑏𝑙𝑒 Matches non-empty set of values to a single value
Hierarchical computational property Associative: 𝑓 𝑣1, 𝑣2, 𝑣3 = 𝑓 𝑓(𝑣1, 𝑣2), 𝑣3 = 𝑓 𝑣1, 𝑓(𝑣2, 𝑣3)
Commutative: 𝑓 𝑣1, 𝑣2 = 𝑓 𝑣2, 𝑣1
Operations: Add local statistics
Get global statistics
8 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
Aggregation Functions
9 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
General Challenges for Monitoring
Robustness Handling Churn
Coping with Link-Losses
Scalability Scaling in terms of participating peers
Scaling in terms of exchanged information
Performance High precision, low outliers
Efficiency Lightweight solution
Minimize complexity: easier to use, more robust
Applicability Applicable on every (structured) p2p overlay
Independent of any application
10 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
Design Decisions for P2P Monitoring
Topology: distributed vs. centralized
Layering: new vs. integrated
Monitoring topology: mesh, tree, ring, star … If tree, tree node position assignment: deterministic vs. dynamic
If dynamic roles, role assignement: heterogeneous vs. homogeneous
Monitoring scope: complete vs. interpolated
Monitoring view generation: proactive vs. reactive
Monitoring view update: push vs. pull
11 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
Approaches for P2P Monitoring
Centralized monitoring
SNMP, Nagios
Overlay-specific solutions
DASIS
Uniform Sampling
Gossiping
Tree-based structure
SkyEye.KOM
Variant: many trees
Variant: push-based protocol
Variant: reactive update pattern
Peer-to-Peer Systems
Monitoring the Quality of Peer-to-Peer Systems
– Selected Monitoring Approaches
13 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
► Centralized Monitoring
Idea Server receives, compiles and presents statistics
All relevant network devices attached
Example RFC 1988: SNMP – simple network monitor protocol
Nagios: open source solution • „Industry standard in IT infrastructure monitoring“
AMT: Intel‘s Active Management Technology • System-on-a-chip: device discovery, monitoring, remote shut-
down/restart
Pros: Works well for small networks (<10000)
Monitoring server becomes bandwidth/CPU/memory bottleneck
Cons: Not scalable to millions, single-point-of-failure
More a “reverse-multicast”
14 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
► Overlay-specific Solutions
Idea: extend existing overlays with monitoring abilities
Strong interleaving with a given overlay
Optimized and bound for specific overlay
DASIS (2004)
Distributed Approximative System Information Service (DASIS)
Assumes structured overlay with prefix-based routing
Modifies
• Routing tables
• Stabilization protocols
Goal
Every node with peerID = 1234567…n should know the statistics
for his prefixes 1,12,123,1234… K. Albrecht, R. Arnold, M. Gähwiler, and R. Wattenhofer: Aggregating Information in
Peer-to-Peer Systems for Improved Join and Leave. In IEEE P2P ’04: Proceedings
of the International Conference on Peer-to-Peer Computing, 2004
15 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
DASIS: Protocol Details
Monitoring protocol: per induction
To obtain status for prefix 1234…i
Aggregate own status for 1234…i(i+1)
With status information of “neighbor” 1234…i NOT(i+1)
Preferred implementation approach
Hook into p2p overlay routing
• Add columns for statistic information
of specific neighbor
Piggyback monitoring information
while stabilizing with neighbors
K. Albrecht, R. Arnold, M. Gähwiler, and R. Wattenhofer: Aggregating Information in
Peer-to-Peer Systems for Improved Join and Leave. In IEEE P2P ’04: Proceedings
of the International Conference on Peer-to-Peer Computing, 2004
16 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
DASIS Evaluation
Some notes Piggybacking has no influence of traffic overhead
• But reduces amount of packages relevant?
Strong interleave with p2p overlay limits applicability
• Strong assumptions on p2p overlay applicability?
• Both cannot be improved independently
Pro Monitoring topology relies on p2p overlay topology
• Avoids redundant (costy) stabilization protocols
Every node is informed about statistics
Cons Strong interleave with p2p overlay not extendable, applicable
Inefficient: exchanging monitoring information with O(logN) peers
17 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
► Uniform Sampling
Idea
Only as a representative set of nodes and interpolate results
Representative set: obtained by random walk
• With correction to not be biased to high-degree nodes
Random walk
Start at random node x
Probability to visit neighbor y:
Pos(x): Probability for being at specific node x
• Convergence of stationary distribution
Observation:
• Stationary distribution (Pos(x)) is biased to peers with high degree
• Random walks may visit nodes twice D. Stutzbach, R. Rejaie, N. Duffield, S. Sen, W. Willinger:
On Unbiased Sampling for Unstructured Peer-to-Peer Networks.
In: Proceedings of the 6th Internet Measurement Conference, IMC (2006)
18 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
Uniform Sampling in Undirected Graphs
Metropolized Random Walk with Backtracking (MRWB)
Modified transition matrix:
Protocol: Selecting the next step from peer x Select a neighbor y of x uniformly at random
Query y for a list of its neighbors, to determine its degree
Generate a random number, p, uniformly between 0 and 1
• If then y is the next step
• Otherwise, remain at x as the next step D.Stutzbach, R.Rejaie, N.Duffield, S.Sen, W.Willinger: On unbiased
sampling for unstructured peer-to-peer networks. In: Proceedings of the 6th
Internet Measurement Conference, IMC (2006)
𝑄 𝑥, 𝑦 =
𝑃 𝑥, 𝑦 ∙ min𝑑𝑒𝑔𝑟𝑒𝑒 𝑥
𝑑𝑒𝑔𝑟𝑒𝑒 𝑦, 1 𝑖𝑓 𝑥 ≠ 𝑦
1 − 𝑄 𝑥, 𝑦 𝑖𝑓 𝑥 = 𝑦𝑥≠𝑦
𝑝 ≤𝑑𝑒𝑔𝑟𝑒𝑒 𝑥
𝑑𝑒𝑔𝑟𝑒𝑒 𝑦
19 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
Uniform Sampling in Undirected Graphs
Protocol continued
Reactive protocol: interested peer initiates a sample query
Sample query uses random walk:
• Time-to-live is decreased
• Peers attach (aggregate) their statistics
• Once time-to-live vanished:
– send results to querying peer
Pros
Wide applicability
Cons
Limited scope: obtained results questionable
Not all statistics supported: sum, minimum, maximum
High overhead: every node queries for himself
Peer-to-Peer Systems
Monitoring the Quality of Peer-to-Peer Systems
– Gossip-based Approaches
21 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
► Gossiping Protocols
Idea:
Communicate only with neighbors (gossip)
• Assumes no specific overlay topology
Exchange and aggregate information
• E.g. calculate averages, minimum, maximum
Characteristics
Gossip protocols are round-based (epochs)
For every round
• Each node selects a subset of nodes to interact with (pairwise)
• The selection function is often probabilistic;
• Nodes interact via “small” messages
• Local state changes due to new information
In general: “quick” convergence
D. Kempe, A. Dobra,J. Gehrke, “Gossip-Based Computation of Aggregate
Information,” IEEE Symposium on Foundations of Computer Science (FOCS’03)
22 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
Example Average Calculation
Example: 12 nodes
Initial state
After 1 round
• With communication links
After 5 rounds
After 10 rounds
23 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
Calculation of the Sum
One nodes starts with 1, all others with 0
Create average, once converged: 𝑠𝑢𝑚 =1
𝑎𝑣𝑒𝑟𝑎𝑔𝑒
Example:
24 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
Assumptions
Input: local states 𝑥𝑖(𝑡) of peers 𝑝𝑖 at time 𝑡
Initialization defines aggregation function
round(0){
1. 𝑠0,𝑖 = 𝑥𝑖 (for average calculation)
2. 𝑤0,𝑖 = 1 (for average calculation)
3. 𝑠𝑒𝑛𝑑 𝑠𝑖 , 𝑤𝑖 𝑡𝑜 𝑠𝑒𝑙𝑓 }
Round (r>0){ 1. Let {(𝑠𝑟−1
∗ , 𝑥𝑟−1∗ )} be all pairs sent to 𝑖 during round r-1
2. 𝑠𝑟,𝑖 = 𝑠𝑟−1∗
∗ ; 𝑤𝑟,𝑖 = 𝑤𝑟−1∗
∗
3. Choose a target node 𝑗 ≠ 𝑖 uniformly at random
4. Send the pair (1
2𝑠𝑟,𝑖 ,1
2𝑤𝑟,𝑖) to j and self
5. 𝑠𝑟,𝑖
𝑤𝑟,𝑖 is the estimate of aggregate in round r }
Gossip-Protocol: PushSum
25 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
Initialization of PushSum
Result of gossiping: Input: 𝑠𝑖 , 𝑤𝑖
Output: F t =𝑎𝑣𝑒𝑟𝑎𝑔𝑒(𝑠𝑖)
𝑎𝑣𝑒𝑟𝑎𝑔𝑒(𝑤𝑖)
Calculating the average: For all nodes: 𝑠𝑖 = 𝑥𝑖; wi = 1
Output:F t =𝑎𝑣𝑒𝑟𝑎𝑔𝑒(𝑥𝑖)
1
Node count: One single node: 𝑠𝑖 = 1;𝑤𝑖 = 1 All other nodes: 𝑠𝑖 = 1;𝑤𝑖 = 0
Output: F t =1
1/𝑛 with 1/n being the average share of 1 among n peers
Calculating the sum: One single node: 𝑠𝑖 = 𝑥𝑖; 𝑤𝑖 = 1 All other nodes: 𝑠𝑖 = 𝑥𝑖; 𝑤𝑖 = 0
Output: F t =𝑎𝑣𝑒𝑟𝑎𝑔𝑒(𝑥𝑖)
1/𝑛
26 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
Observation:
During the gossiping: local input 𝑥𝑖 cannot be changed
Aggregations round-based (epochs)
Initializing an Epoch
Centralized
• Several leader algorithms exist
– E.g. node with maximum specific value
• Epoch may be restarted periodically by leader
Decentralized
• Besides local measure (𝑥𝑖) and weight (𝑤𝑖): add version number
• Every node may start new epoch concurrently
– Larger version numbers dominate smaller ones
– Epoch runs out after convergence and minimal value variations (< 𝜀)
Initializing an Epoch and Peer Election
27 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
Performance and Complexity of Push-Sum
Performance: precision
Simulations with 1M nodes
• Gossip every 5 second
For most time:
• False values
• Although convergence exist
Problem
• Peer count starts always at 0
Convergence time • n = number of nodes
• ε = accepted relative error
Push-Sum converges quickly
Problem:
• Huge message overhead per node
W. Terpstra, C. Leng, A. Buchmann: Brief Announcement: Practical Summation via
Gossip, ACM Symposium on Principles of Distributed Computing (PODC 2007)
In-
pre-
cise
28 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
Pros:
General applicability in (expander) graphs
• No assumptions on the overlay topology
Converges to final values
Cons:
Slow procedure, epochs are long
Lots of redundant communication
High traffic overhead
Gossip-based Monitoring Evaluation
Peer-to-Peer Systems
Monitoring the Quality of Peer-to-Peer Systems
– Tree-based Approach: SkyEye.KOM
30 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
► Tree-based Monitoring Mechanism
Idea:
Create (additional) tree topology on top of DHT
Protocol:
• Periodically
– Calculate aggregate of own local view and received from child nodes
– Send aggregate to parent node
• Root calculates global view
– And passes global view to all peers
Example: SkyEye.KOM (2009)
Assumes structured p2p overlay
Aims at high precision with low overhead
31 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
Design decisions in SkyEye.KOM
New layer (vs. integrated) New layer allows wider applicability
Set on top of KBR-compatible structured p2p overlays
Proactive (vs. reactive) System state information is continuously interesting for all users
Monitoring topology: tree (vs. bus, ring, star, mesh) Fixed out and in degree
Position assignment: dynamic and deterministic Deterministic IDs used in topology, dynamically resolved with DHT
For all structured P2P overlays Covered by DHT-function: route(msg, key), lookup(key)
32 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
Assumptions
Reliable structured p2p overlay
“Key-based Routing” – operations
• boolean isMyKey(Key K)
• void route(key K, Message M, node hint)
Building a tree topology
Introduce new overlay layer
• With own ID space ([0,1[)
• Practical infinite granularity
Create tree topology in new overlay
• Using routing of p2p structured overlay
Concept of new layer
Decouples from specific p2p overlay
Unified ID space [0,1]
1 10
50
20 30
40
45 15
P2P Overlay
0 1 0.09 0.2 0.31 0,4 0.5 0.6 0.75 0.9
Internet
33 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
SkyEye.KOM: Tree Topology
Tree of information domains Domain: ID interval
• E.g. [0, 0.5[ or [0.75, 0.875[
• Largest domain, level 0: [0,1[
Domain ID: “middle value” in interval
Domain size split in β parts per level
Domain IDs build tree topology Node degree: β child nodes
Tree topology of Domain IDs does not change over time!
• Only the peers responsible for the Domain IDs might change
Assignment of peers to domains dynamic
1 10
50
20 30
40
45 15
P2P Overlay
0 1 0.09 0.2 0.31 0,4 0.5 0.6 0.75 0.9
Internet
0.5
0.25
0.375
0,3125
0.75
0.875 0.625 0.125
Domain Domain ID
0.3125
0.375
0.25
0.5
34 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
SkyEye.KOM: Node Placement in Tree
Tree-overlay p2p overlay Reconvert to
• E.g. = /
Coordinator: • Responsible for Domain ID
• Check via DHT function – isMyKey(Key K)
Peers to Domain ID assignment Peer p calculates for each
level l the Domain in which its own ID is located
• (note: sometime adaptation needed)
For those domains, they calculate the Domain IDs (
If peer is responsible for : position defined
Example:
Node ID ∈ {0,… , 2160 − 1}
Tree ID = Node ID /
(2160 − 1)
Example: 0.63
• Responsibility range: 0.61-
0.64
Check for resp. ID (β = 2)
• Level 0: 0.5 no
• Level 1: 0.75 no
• Level 2: 0.625 yes
Position of peer: 0.625
Parent node: 0.75 (tree ID)
• Route messages to node
ID 0.75 * (2160)
35 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
Detailed Algorithm to Identify Position in the Tree
36 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
SkyEye.KOM: Communication
Example tree
Tree degree (β) = 2
• Results in logarithmic tree
size
Balanced, if ID space
balanced
Not always β children
• Peers may be
Coordinators at various
levels
37 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
SkyEye.KOM: Communication Protocol
Gathering global view All peers measure local
status
Periodically sent to parent peer
• Update Interval (UI)
Aggregation Direct: count, sum, minimum,
maximum, sum of squares
Derived: mean, variance, std. deviation
Dissemination of global view Global view in root
Every update message is acknowledged
Contains global view from level above
Global view
Local measures, (synchronized signal in simulations)
Aggregated
view
β child nodes
… 1a 1b
1β
1. Independent updates
in UI intervals per node
2b 2a
2β
2. ACKs with view of parent
peer for every update
38 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
Time and Overhead Estimation
Convergence time: Aggregation up the tree and pushing global view down in periodic update intervals (UI)
Tree height dominated by β
Overhead: Per node: 1+ β messages
Very small, independent of N
Typical size of β : 4, 8, 16
Convergence:
Gather and disseminate time 𝑂 logβ 𝑁 ∙ 𝑈𝐼
39 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
SkyEye.KOM – Variant: Synchronized Communication
Idea:
Loosely synchronize updates messages
Push results once global view is created
Approach:
Push with ACKs: synchronization delay
Peers adapt their update offset
Updates processed in a row, from leafs to root
Push global view with “special-ACK”
Gather and disseminate time:
𝑂(logβ 𝑁 + 𝑈𝐼)
Before: 𝑂 logβ 𝑁 ∙ 𝑈𝐼
40 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
SkyEye.KOM – Variant: Synchronized Communication
Protocol
Assumption:
• Transmission delay < 2 sec
Global view message from root
• Contains offset, when to send next update
– E.g. UI = 60 seconds
• Is decreased every step
– by time 𝑡∆
– E.g. by 3 seconds
Resulting transmission offset
• Current time in root 𝑡0
• Average transmission time 𝑡𝑠𝑒𝑛𝑑
• Level 𝑖 sends at time: 𝑡𝑖 = 𝑡0 + (𝑈𝐼 − 𝑖 ∙ 𝑡∆ + 𝑖 ∙𝑡𝑠𝑒𝑛𝑑)
Example: Root – sends out message
• Offset 60 sec
Level 1 • Transmission time: 1s
• Offset 57 sec
Level 2 • Transmission time: 1s
• Offset 54 sec
Level 3 • Transmission time: 1s
• Offset 51 sec
…
Level i • Transmission time: 1s
• Offset 𝑈𝐼 − 𝑖 ∙ 𝑡∆
Sending times Level i: 𝑈𝐼 − 𝑖 ∙ 𝑡∆ + 𝑖 ∙ 𝑡𝑠𝑒𝑛𝑑 …
Level 3: 54 sec
Level 2: 56 sec
Level 1: 58 sec
Root receives next global view in 59 sec
41 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
Evaluation of SkyEye.KOM
Simulation Setup
Node count 5000
Churn: Join, KAD churn
Tree degree = 4
Update interval = 60sec
Observation:
Time delayed, precise
monitor
Very low overhead
• <100 bytes / s
• Overhead is precisely
monitored
42 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
Some Remarks on SkyEye.KOM
Robustness against churn If peer fails: automatically replaced in the DHT
Updates are routed to new peer for aggregation
Costs One update: ~1kb,
Out + in degree = 1 + tree degree (2, 4 or 8)
Independent of position in the tree!
Age of information: Limited by tree depth, O(log (N))
Influenced by update period
Pro: Low overhead, good precision
Continuous monitoring
Cons: Churn on higher located nodes shows impact
Applicability limited to DHTs
Peer-to-Peer Systems
Monitoring the Quality of Peer-to-Peer Systems
– Comparison of various Approaches
44 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
► Benchmarking of Monitoring Solutions
Structured / tree-based solutions Limited in applicability key-based routing needed
Unstructured / gossip-based solutions Needs only connected graph not limited in application
Question: Why do not we use only gossip-based solutions?
Answer: They are inefficient
Imprecise OR costy
Comparative Evaluation Node count: 1000, churn
SkyEye.KOM: UI=15 sec, β = 8, synchronized (every 20min)
PushSum: 30 messages per epoch
Centralized for comparison, UI = 60 sec
Same overhead allowed for all monitoring approaches
45 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
Churn – Reference: Node Count
Simulation setup Churn with joining and
instantly leaving nodes
Both decentralized solutions
• Use ca. 200 bytes/s per node
• For better comparability
Aggregation time
Of global view
Performance of tree similar
to centralized solution
Gossip-approach slower
• Sawtooth: epochs
46 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
Reference Signals: Steps, Sawtooth and Sine
PushSum Imprecise monitoring
Epochs are visible
Although same traffic overhead
Centralized and tree-based Precise
Tree become imprecise with too much churn
47 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
Impact of Parameters
In SkyEye.KOM:
Increase in branchingfactor
Tree less deep
Shorter paths
More precise monitoring
In Gossip-based approach
Increase Rounds per Epoch
Better convergence of
nodes
Information becomes
outdated
48 HHU – Technology of Social Networks – JProf. Dr. Kalman Graffi – Peer-to-Peer Systems – http://tsn.hhu.de/teaching/lectures/2014ws/p2p.html
Summary on P2P Monitoring Solutions
Several approaches exist
Gossip-based solutions
• Pro: applicable in any graph
• Cons: expensive and imprecise
Tree-based solutions
• Pro: precise and of low overhead
• Con: rely on DHT substrate (which also induces costs)
Centralized solutions
• Pro: quick, fast and cheap
• Cons: do not scale
Next Questions:
How to use monitored system status
To improve the quality of the system?