Multimedia Proxy Caching Mechanism for Quality Adaptive Streaming Applications in the Internet R. Rejaie, H. Yu, M. Handley, D. Estrin

2 USC INFORMATION SCIENCES INSTITUTE Motivation Rapid growth of Internet streaming apps – Naturally, caching would be helpful Benefits – Reduce server load and network load – Reduce startup latency – Improve quality of delivered streams Anything new?

3 USC INFORMATION SCIENCES INSTITUTE Streams vs Web Pages Congestion controlled (TCP-friendly) Realtime constraint  varied quality Both challenge and oppurtunity for caching Congestion controlled (TCP) No realtime constraint  fixed quality (0/1)Intro

4 USC INFORMATION SCIENCES INSTITUTE Assumptions Congestion controlled streams, e.g., RAP Quality adaptation [RHE99] – Layered encoding: sub- structure within streams – Adjust quality based on long term bandwidth variationIntro Quality (active layers) Time

5 USC INFORMATION SCIENCES INSTITUTE Design Overview Goal: efficient cache state – Quality  Popularity – Quality  Recent Clients’ Bandwidth Two key mechanisms – On-demand prefetching – Fine-grain replacementDesign

6 USC INFORMATION SCIENCES INSTITUTE Internet Cache Miss Playback from origin server to clients through cache Cache intercepts and stores the stream No benefitDesignCache Server Client ClientClient

7 USC INFORMATION SCIENCES INSTITUTE Delivered quality is determined by: – Cached stream quality – Available client bandwidth Cache Hit Playback from cache – Lower startup latency InternetDesignCache Client ClientClient Server * What if cached quality < client bandwidth? prefetchingon-demand

8 USC INFORMATION SCIENCES INSTITUTE Cached stream Pre-fetched dataPrefetching Stream sub-structure – Layers – Segments Cached stream quality < client bandwidth – Missing segments – New layers Goal – Playback deadline Time L 0 L 1 L 2 L 3 L 4 Quality (no. active layers)Design Played back stream

9 USC INFORMATION SCIENCES INSTITUTE Prefetching Required pieces are predicted by QA – Sliding prefetching window – Layer priority – Preemptable by new requests Tradeoff – Playback deadline and prediction accuracy Time L 0 L 1 L 2 L 3 L 4 QualityDesign t0t0t0t0 T  prefetching window t 0 : current playout time T: lookahead interval  : window width t 0 +  T 

10 USC INFORMATION SCIENCES INSTITUTE Replacement Algorithm Converge to efficient cache state: – Quality  Popularity – Quality  Recent Clients’ Bandwidth Prefetching alone cannot reach the goal – What if cached quality > client bandwidth? – Does not matter if cache space is , but … * Replacement should be fine-grainedDesign

11 USC INFORMATION SCIENCES INSTITUTE Replacement Algorithm Per-layer popularity – Interests and bandwidth from recent clients – Weighted hit: PlaybackTime/StreamDuration – Layered encoding guarantees monotonicity Fine-grain flushing – Based on per-layer popularity – Per-segmentDesign

12 USC INFORMATION SCIENCES INSTITUTE Simulation: Setup RAP + QA Background traffic – 10 FTP, 9 RAP – Average 56Kb (2.8 layers) between cache/server High-bw/low-bw clients – Controls average client bandwidth Sequential request Using ns-2Evaluationd1 d2 E F0 F9 R0 R8 S 1.12Mb C2 1.5Mb 1.5Mb 1.5Mb 1.5Mb C1 56Kb F0’ F9’ R0’ R8’ 1.5Mb 1.5Mb 1.5Mb server cache low bw client high bw client

13 USC INFORMATION SCIENCES INSTITUTE Simulation: Scenarios Effect of prefetching – Simplest case: 1 stream, high bandwidth client Effect of replacement + prefetching – 10 streams, Zipf popularity – Cache size = 0.4  total stream size – Impact of popularity: 95% high-bw requests – Impact of client bandwidth: 95% low-bw requestsEvaluation

14 USC INFORMATION SCIENCES INSTITUTE Simulation: Quality Metrics Completeness – Percentage of a stream layer in cache Continuity – Average “chunk” length – How scattered is a cached layerEvaluation

15 USC INFORMATION SCIENCES INSTITUTE Simplest Case: Completeness Evaluation

16 USC INFORMATION SCIENCES INSTITUTE Simplest Case: Continuity Evaluation

17 USC INFORMATION SCIENCES INSTITUTE Effect of Popularity Evaluation

18 USC INFORMATION SCIENCES INSTITUTE Effect of Popularity Evaluation

19 USC INFORMATION SCIENCES INSTITUTE Effect of Client Bandwidth Evaluation

20 USC INFORMATION SCIENCES INSTITUTE Effect of Client Bandwidth Evaluation

21 USC INFORMATION SCIENCES INSTITUTE Conclusion Stream caching mechanism: – Congestion control + quality adaptation – Pre-fetching + fine-grain replacement Result: – Efficient cache state

22 USC INFORMATION SCIENCES INSTITUTE Future Work Efficient cache state vs performance – Byte hit ratio – Delivered quality Extensive simulation – E.g. access patterns, bandwidth distribution Layer utility functions, other replacement patterns, …