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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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10
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212
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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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OR
SP
TO
SG
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JPSFU
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CUHK
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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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OR
SP
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117
150
181
81 45
CUHK
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SFU
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