Towards Cost-Effective Low-Delay Cloud-Assisted Video Conferencing

Mohammad H. Hajiesmaili,Minghua Chen, Lok To Mak

May 2014

Zhi Wang Chaun Wu Ahmad Khonsari

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Tele-health-care Online education

Press Agency International Business

With 51.7% annual user growth[1]: video conferencing is the fastest-growing multimedia service Video conferencing users will surpass audio conferencing users by 2015

Video conferencing applications

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The maximum no. of parties in a session

Multi-party video conferencing

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• ITU-T Recommendation G.114• Delay <=150 is good, the same as PSTN• Delay >400, unacceptable

2. Stringent end-to-end delay constraints

1. High bandwidth and processing demand by heterogeneous users

High processing demand Transcoding is required

• Resolution (~ 100)[1]

• Hardware (~ 2800)• Mobile OS (~ 14)• Request for more than 40

different representations High bandwidth demand

HD support

Key Challenges in Video Conferencing

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Client-Server Architecture

Delay can be considerable

Servers help in resource-intensive tasks

Peer-to-Peer Architecture

Fail to resolve high demand challenge

Low delay conferencing

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State-of-the-Art Cloud Architecture for Video Conferencing

Cloud-assisted solution CAN address the two key challenges

But it brings out new design challenges

Users only care about sending/receiving video streams to/from the agents

Cloud agents accomplish all resource-intensive tasks.

High demand: by moving the tasks into the agents

Delay requirement: by reliable and dedicated backbone

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Operational costs of the service provider

Cost of using the cloud can be high (processing and data transfer) According to our estimate, if Skype moves to cloud and just 20% of its traffic requires

data transfer between agents, then the monthly bill will be about 2 million USD.

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Subscription policy is the key mechanism that can make a big difference in delay and cost

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What is the optimal user-transcoding subscription that minimizes the delay and the costs?

User/Transcoding Subscription (UTS)

U: the total no. of users

L: the total no. of cloud agents

State Space:

Delay Constraint

Capacity Constraint

Key question

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We provide the first possible answer to the important questionWe devised a parallel and adaptive algorithm based

on Markov Approximation framework with sound performance guarantee.

We carried out Internet-scale experiments and our solution outperforms alternative solution by 77% cost reduction with better conferencing delay.

Contribution

Our solution can save up to 1.54 million USD per month for Skype (assuming it moves to cloud)!

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subject to:

Transcoding capacity constraint

delay constraint

Bandwidth capacity constraints

Delay/cost objective function

Delay Traffic Processing

Integer variables

Problem formulation: User/Transcoding Subscription (UTS)

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A Naïve approach is not applicableUse abundant cloud resources to solve the

problemHigh computational complexityCan not handle dynamics in sessions and agents

Parallel and adaptive solution is preferredBy Parallel solution, each session can solve its

subscription problem locally Scales with the problem size

Adaptive solution tackles the dynamics

The solution can be an approximate solution

Solution approach

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Log-sum-exp approximation

Parallel Markov Chain Design

Markov approximation framework

Combinatorial network problems

Formulation

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The intuition behind the framework

Markov approximation frameworkThe search is intelligent and is guided by an

underlying Markov chain

Naïve exhaustive search Visit configurations and keep the track of

the best configuration

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The Markov chain design is application-specificThe state space is all feasible configurationsDesign Space: Two degrees of freedom

1. Add or remove transition edge pairs2. Designing transition rate

Parallel implementation by We allow only one change in each transition

Just one session is subject to change The transition rate is proportional to the difference of

local objective of the current and the target subscription.

Markov chain design

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The original Markov approach suffers from slow convergence.

When the agents‘ resources are limited Finding a feasible initial subscription is challenging

We are interested in a close-to-optimal initial pointFaster convergence of Markov-based solutionA resource-aware scheme to increase the success rate of

subscription Initialization by nearest policy

Resource-obliviousInitial traffic cost is high

Session bootstrapping

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1Constructs a set of potential agents:• Select top nngbr nearest agents

2Rank the agent based on:• Residual capacities• Inter-agent proximity

3 For each user select the top ranked agent in its vicinity

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AgRank Algorithm

Inspired by Google PageRank To measure the popularity of web pages

We use the same theory To reflect both the residual capacities

and inter-agent proximity in ranking

27+67=94 < 20+117=137

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6 Amazon EC2

instances

Oregon, Virginia, California, Tokyo, Singapore, Ireland

10 private PC nodes

5 nodes in North America, 4 nodes in Asia, 1 node in Europe

10 actual conferencing

sessionseach with 3-5 participants

A prototype of real-world cloud-assisted video conferencing system

Tractable scenarios with actual data

A set of internet-scale trace-driven experiments

Large-scale world-wide scenarios

100 random

runs

200/256 PlanetLab nodes

7 Amazon EC2 instances

~ 50 conferencing sessions

Experiments

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~75% inter-agent traffic reduction

Nearest policy as initialization

~14% conferencing delay reduction

Simultaneous traffic and delay reduction on prototype system

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Nearest policy as initialization Our AgRank policy as initialization

Initial inter-agent traffics

~ 22 vs. ~16

Initial conferencing delay

~ 310 vs. ~305

Nearest vs. AgRank

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6 sessions at initial time -> 4 more sessions arrive at t = 40 -> 3 sessions depart at t = 80

Our solution can handle dynamics

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77% traffic cost reduction 2% delay reduction

100 random runs each one with 200 PlanetLab nodes

Internet-scale Experiments

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AgRank#3 initializes 100% of VC scenarios under average bandwidth capacity 750 Mbps, while resource-oblivious nearest policy can serve only 8% of scenarios.

Higher Success Rate of AgRank vs. Nearest

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Conclusion

subject to:

Cloud-assisted video conferencing User/Transcoding Subscription Markov approximation solutionUser/Transcoding Subscription

A win-win solution for both the users and the provider.

A significant cost and delay reduction in cloud-assisted video conferencing by an approximate solution. Parallel and adaptvie Close-to-optimal initialization

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To improve the prototype systemImplement the mobile version of the software

To improve the convergence of the algorithmTo consider the joint problem of optimal rate

allocation and subscriptionRecent advances in delay-constrained networks

Single unicast throughput with network codingVideo conferencing

Evinces delay-sensitivityBut in multi-cast setting

It is a beginning rather than an end

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Thank you!mohammad@ie.cuhk.edu.hk