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Generating Streaming Access Workload for Performance Evaluation

Generating Streaming Access Workload for Performance Evaluation. Shudong Jin 3nd Year Ph.D. Student (Advisor: Azer Bestavros). Project Overview. This project aims to develop a G enerator of I nternet S treaming M edia O bject access workloads ( GISMO ) Why develop GISMO?

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Generating Streaming Access Workload for Performance Evaluation

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  1. Generating Streaming Access Workloadfor Performance Evaluation Shudong Jin 3nd Year Ph.D. Student (Advisor: Azer Bestavros)

  2. Project Overview • This project aims to develop a Generator of Internet Streaming Media Object access workloads (GISMO) • Why develop GISMO? • Streaming access of emerging Internet streaming application (e.g., video/audio on Web) has unique characteristics: • High bandwidth requirement • Long duration (seconds to hours) • Variable bit-rate (VBR) burstiness • Timeliness and user-perceived quality are important • There is no streaming access workload generator • Workload generation is important for performance evaluation of Internet streaming content delivery techniques

  3. GISMO: Characteristics

  4. GISMO: Modeling • Modeling Request Arrival Process • Popularity distribution • Zipf-like distribution models the skewed request frequency of the streaming media objects. P ~ r-, 0<<1, where P is the access frequency, r is the rank of an object. • Temporal Correlation of Requests • Requests to the objects tend to arrive non-randomly. Pareto distribution models the correlated inter-arrival time. • Seasonal Patterns • Aggregated request arrival rate can exhibit seasonal patterns (hourly, daily, weekly etc). GISMO users can define such diurnal patterns.

  5. GISMO: Modeling • Modeling Individual Requests • Object Size Distribution • Streaming media objects have a wide range of length. We use a power law to model it. • Partial Access Patterns • User interactions involves in streaming access. We use Pareto distribution to model the stop time. • Variable Bit-Rate • The bit-rate of streaming media objects has high variability. We use Pareto distribution to model the tail of VBR marginal distribution, and Lognormal distribution for the body.

  6. GISMO: Modeling • VBR self-similarity • The bit-rate of streaming media objects (e.g., audio/video) exhibits long-range dependence. • The auto-correlation function decay slowly • Burstiness persists for long period, and implies the ineffectiveness of buffering • Generating self-similar process FGN • We use a random middle-point displacement algorithm • Transforming VBR marginal distribution • Gaussian  hybrid Lognormal/Pareto distribution

  7. GISMO: Functionality • GISMO generates • A set of bogus streaming media objects, installed in the servers which mimic real servers • Requests to these objects, initiated by the clients which mimic real users • GISMO can be used for many purposes • Evaluating the performance of streaming media servers, e.g., scheduling and I/O • Evaluating network protocols for streaming data transmission • Evaluating streaming data replication techniques (caching, pre-fetching, multicast merging, etc)

  8. GISMO: Architecture WWW Browser Requests Streaming Server TCP Media Player WWW Browser Objects Network RTSP Requests Media Player UDP Media Player Web Server WWW Browser Requests

  9. GISMO: Use Case • We have conducted a case performance study • Using GISMO to generate workloads • Evaluating proxy caching and server stream merging techniques • Showing that how the workload characteristics impact their effectiveness

  10. GISMO: Use Case How does popularity impact the effectiveness of proxy caching (left) and server merging (right)

  11. Future Directions • More client interactions in request streams, e.g., VCR functionality • More correlations in streaming media objects, e.g., Group-of-Picture GoP correlation • Using GISMO in evaluating streaming content delivery techniques • Using GISMO in evaluating network protocols for streaming data transmission

  12. Related Publications • Shudong Jin and Azer Bestavros. Generating Streaming Access Workloads for Performance Evaluation and A Case Study. BU CS Technical Report, April 2001. • Shudong Jin and Azer Bestavros. Temporal Locality in Web Request Streams: Sources, Characteristics, and Caching Implications. Short paper appeared in ACM SIGMETRICS’2000; full paper appeared in MASCOTS’2000. • Paul Barford and Mark Crovella. Generating Representative Web Workloads for Network and Server Performance Evaluation. ACM SIGMETRICS’1998.

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