1 ShadowStream: Performance Experimentation as a Capability in Production Internet Live Streaming...

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ShadowStream: Performance Experimentation as aCapability in Production Internet Live Streaming Networks

Present by: Chen Alexandre Tian (HUST)Richard Alimi (Google)Richard Yang (Yale)David Zhang (PPLive)

Live Streaming is Widely Used

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• Many recent major events live streamed on the Internet

• Many daily events are streamed as well• Justin.tv, livestream, …

State of Art of Live Streaming System

Hybrid system (e.g., Adobe Flash 10.1 and later) CDN seeding P2P with BitTorrent-like protocols

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Performance of Live Streaming System Become Difficult to Understand/Predict

System software becoming more complex

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Internet Environment Complexity

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ADSL Modem BufferPowerBoostInter-ISP throttling……

Misleading results if not considering real network features.

Need Evaluation at Right Scale

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Misleading results if not considering the target scale.

Key Idea of ShadowStream

The production system provides an ideal evaluation platform: real users, real networks, at scale.

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Starting Point: Use Experiment Algorithm On Real User

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First Challenge: How to achieve both accuracy and user protection?

ExperimentCDN Protection

New pieces inject here

Virtual PlaypointMiss record here

UserPlaypoint

Two seconds later:

Issues of CDN Protection

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• Scale• 100,000 Clients @ 1 mbps rate ->100Gbps• More demand with concurrent test channels

• Network bottleneck• There can be bottlenecks from CDN edge servers to streaming clients

New Idea: Scaling Up with Stable Protection

Observation: there already exists a stable version w/ reasonable performance

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Issue: Losses of Experiment Accuracy.

Why Loss Accuracy?

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Converge to a Balance Point

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We should observe m(θ0), but instead we actually observe m(θ’).

Putting-Together: Cascading Protection for Accuracy and Scalability

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Q: Any remaining challenge?

Real user behaviors differ from testing behaviors

Idea: transparently orchestrate experimental scenarios from existing, already playing clients

Virtual arrivals/virtual departures

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Test specificationTriggeringVirtual arrival controlVirtual departure control

Independent Arrivals Achieving Global Arrival Pattern

Peer generate arrival times by drawing random numbers independently according to the same cumulative distribution function.

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From Idea to System

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Challenge: How to minimize developers’ engineering efforts?

Streaming Hypervisor

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Hypervisor API need for each streaming engine getSysTime() getLagRange(), getMaxStartupDelay() writePiece(), getPieceMap()

Computing Windows Bounds

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• Hypervisor calls getLagRange()

Sharing and Information Flow Control

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Compositional Software framework

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Example: Adding an admission control component

Evaluation:Experiment Accuracy & Protection

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Only CDN as the Protection:

Cascaded Protection:

Evaluation: Experimental Opportunities

SH Sports channel and HN Satellite channel, pplive, September 6, 2010

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Evaluation: Accuracy of Distributed Arrivals

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Arrival function from “Performance and Quality-of-Service Analysis of a Live P2P Video Multicast Session on the Internet”. Sachin Agarwal, Jatinder Pal Singh, Aditya Mavlankar, Pierpaolo Bacchichet, and Bernd Girod, In Proceedings of IWQoS 2008. Springer, June 2008

Take Home IdeaMany Internet-scale systems are unique systems that are difficult to build/test.The ShadowStream scheme consists of following key ideas: Conduct shadow experiments using real system,

real users Protection and accuracy present dual challenges

Use Stable for scalable protection Introduce external resources (CDN) to remove

interference on competing resources Create shadow behaviors from real users

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Thanks for coming!

Questions?

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Metric of Live Streaming Performance

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Piece missing ratio

Backup Slides

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Streaming of the Internet

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Virtual Sliding WindowA streaming engine has two sliding windows: an upload window (P2P) and a download window (CDN and P2P). Each engine call getSysTime() to Hypervisor, based on real system time and time shifted value, Hypervisor assign a virtual system time to each engine.Each engine calculate x(left) and x(right) of download windowEach engine advances its sliding window at the channel rate μ pieces per second.

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The reasoning behind

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•CDN see the original miss-ratio/supply-ratio curve•P2P Protection see the curve minus δ

Specification

Define multiple classes of clients (e.g., cable or DSL, estimated upload capacity class, or network location)A class-wide arrival rate function λj(t)Client’s lifetime is determined by the distribution Lx

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Local Replacement for Uncontrolled Early Departures

Capturing client stateSubstitution

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Triggering Condition

Predict(t): autoregressive integrated moving average (ARIMA) method that uses both recent testing channel states and the past history of the same program

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Independent Arrivals Algorithm

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CDN Capacity and window length

CDN window set to 4 seconds The TCP retransmission timeout is 3

seconds for piece loss 1 extra second for waiting retransmitted

piece

Window length

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Starting up the engineWhen starting a streaming engine x, the Streaming Hypervisor gives x pointers to its download and upload windows.at time a(s), the client join test channel and Stable engine starts.at time a(e) >a(s), the client join testing, the Experiment Engine and CDN Protection Engine start.After starting, an engine begins to download pieces starting from the target playpoint to the end of its download window.The piece before startup should be protected by CDN, which would be counted by CDN capacity calculation

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ShadowStream Outline

Motivation and ChallengeExperiment Protection and AccuracyExperiment OrchestrationImplementationEvaluation

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Client Substitution

Client substitution delay with client dynamics.

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Backup Slides

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Sec. 8: Limitation Discussion

(Do we really need this?) If Exp consumes resources while no piece received at all (Give priority to Protection?)Download link are bottleneck

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Modeling P2P ProtectionGiven experiment engine e, target rate R,

the miss ratio is mR,e(θ) , or, me(θ)

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Given protection engine e, its target rate is me(θ), the required rescue bandwidth is Θk(me(θ),p)* me(θ)= η(e,p,θ)

P2P Protection no accurate result

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•If P1 is the protection, there would exist balance point(s)•If P2 is the protection, there would be a negative feed-back loop•In either cases, there is no accuracy at all

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Live Streaming

Live Streaming on Internet Live Audio/Video Content Distribution

on Internet e.g., NBC Winter Olympics 2010 live

Using Microsoft Silverlight® P2P live streaming

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Example: PPLive

From PPLive’s Presentation Founded by Graduate Students from Huazhong

University of Science & Technology PPLive is

An online video broadcasting and advertising network provides online viewing experience comparable to TV

An efficient P2P technique platform and test bench

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Estimated global installed base 75 million

Monthly active users* 20 million

Daily active users 3.5 million

Peak concurrent users 2.2 million

Monthly average concurrent users 1.5 million

Weekly average usage time 11 hours

Not Yet!

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Challenges

How to achieve both experiment accuracy and user protection?How to produce desired experiment pattern?How to minimize developers’ engineering effort?

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Starting Point: Use Experiment Alg. On Real User

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A simple example

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• No user-visible pieces misses • Missing piece 91 is recorded• Piece download assignment is adaptive

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Three issues delete

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• Information flow control: Although piece 91 is downloaded by the Protection Engine, it should not be labeled as downloaded in the Experiment Engine.• Duplicate avoidance: Since both Experiment Engine and Protection Engine are running, if their download windows overlap, they may download the same piece.• Experiment feasibility: This lag from realtime is determined when client i joins the test channel with the Protection Engine to make experiment and protection feasible.