Decision Optimization Techniques for Efficient Delivery of Multimedia Streams

Decision Optimization Techniques for Efficient Delivery of Multimedia

Streams

Mugurel Ionut Andreica, Nicolae Tapus

Politehnica University of Bucharest,

Computer Science & Engineering Department

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Summary

• Motivation

• Context

• Improving Heuristics based on Conflict Graphs

• Online Analysis of Traffic (Self-) Similarity

• Kth Best Resource Selection

• Conclusions & Future Work

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Motivation

• QoS guarantees for multimedia streams – strictly necessary– minimum required bandwidth– (more or less) constant latency– reduced jitter

• well-established QoS improvement solution– bandwidth reservation mechanism

• requires:– new business model from ISPs– lease network links for short(er) durations, but

providing guaranteed end-to-end bandwidths– (currently: flat fee lease of constant

upload/download bandwidth links; not end-to-end)

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Context - Data Transfer Scheduling Model (1/2)

• one (centralized) data transfer manager– knows the network topology (structure)– has full control over the network

• many data transfer requests– duration (D) (non-preemptive = a contiguous

time interval)– earliest start time (ES)– latest finish time (LF)– minimum required bandwidth (Bmin)– source (src)– destination (dst)– profit (pr)

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Context - Data Transfer Scheduling Model (2/2)

• request handling modes– batch mode (multiple requests at a time)

• conflicts between the requests in the same batch are modeled by using conflict (hyper-)graphs

• heuristic algorithms are used in order to (repeatedly) compute maximum weight (profit) independent sets

– online mode (1 request at a time)• handle the requests in the order of arrival• verify if the request can be granted (satisfying the request’s

constraints/parameters)• grant the request (resource allocation/reservation) or reject it

– interpretation of time: time slot-based (discrete) or event-based (continuous)

• low response times– a complex strategy would take too long– even a simple strategy may take too long! => need some

efficient techniques (data structures)

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Context - Data Transfer Scheduling Framework

• multiple (interconnected) modules

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Improving Heuristics based on Conflict Graphs (1/2)

• pairs of data transfer requests may be in conflict– they require exclusive access to the same

network resources, during overlapping time intervals

• construct a conflict graph CG• compute a maximum weight independent

set (MWIS) in CG– i.e. select a set of non-conflicting requests with

maximum total weight

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Improving Heuristics based on Conflict Graphs (2/2)

• computing MWIS : NP-hard• heuristics based on repeated vertex extraction

– in case of unit weights: as long as CG contains any more edges (conflicts):

• extract (remove) from CG the vertex with the largest degree

• algorithm for implementing the above heuristic in O(N+M) time– N=number of vertices in CG– M=number of edges in CG

• extensions to other types of repeated vertex extraction heuristics (e.g. minimum degree, maximum number of already extracted neighbors, etc.)

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Online Analysis of Traffic(Self-) Similarity (1/3)

• 2 arrays tr1 and tr2 of T values (one value per time slot)

• a function eval(x,y) (e.g. |x-y|)• an aggregate function aggf (e.g. +,max)• answer efficiently the following types of

queries:– Q(a,b,len)=aggf(eval(tr1(a+i), tr2(b+i)))

(0≤i≤len-1)• self-similarity queries when tr1=tr2• such queries: useful for traffic analysis &

traffic pattern detection

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Online Analysis of Traffic(Self-) Similarity (2/3)

• we divide the T time slots into T/k groups of k time slots each (the last group may be shorter)

• group j: time slot interval [left(j),right(j)]– nslots(j)=right(j)-left(j)+1

• compute Tagg(i,j)=eval(tr(1)(i+q), tr(2)(left(j)+q)) (0≤q≤nslots(j)-1; 1≤i≤T-nslots(j)+1) => O(T2) preprocessing

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Online Analysis of Traffic(Self-) Similarity (3/3)

• Q(a,b,len): can answer in O(1) time for an interval of approx. k slots => O(k+T/k)

• can also allow updates: change the value of tr1(i) (tr2(i)) to v

• O(T·k) per update (if aggf is not invertible)

• O(T) time if aggf has an inverse

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Kth Best Resource Selection

• each resource (e.g. network path) has d features (e.g. bandwidth, latency, etc.) => modelled as a d-dimensional point– plus a weight (e.g. how valuable it is)

• select the Kth largest weight among all the resources whose d features belong to an orthogonal range– we do not want to always allocate the best

resource• use a multi-dimensional data structure (for

efficient ortogonal range counting queries) + binary search the Kth weight

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Conclusions & Future Work

• introduced– data transfer scheduling model– data transfer scheduling framework

• developed techniques for– improving heuristics based on clonflic graphs– online analysis of traffic data– resource selection (and allocation)

• future work– large-scale testing of the proposed techniques

• the data transfer scheduling framework is already implemented

– develop new algorithms for scheduling multimedia streams

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Thank You !