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  1. 1. Generating Streaming Access Workload for Performance Evaluation Shudong Jin 3nd Year Ph.D. Student (Advisor: Azer Bestavros)
  2. 2. Project Overview <ul><li>This project aims to develop a G enerator of I nternet S treaming M edia O bject access workloads ( GISMO ) </li></ul><ul><li>Why develop GISMO? </li></ul><ul><ul><li>Streaming access of emerging Internet streaming application (e.g., video/audio on Web) has unique characteristics: </li></ul></ul><ul><ul><ul><li>High bandwidth requirement </li></ul></ul></ul><ul><ul><ul><li>Long duration (seconds to hours) </li></ul></ul></ul><ul><ul><ul><li>Variable bit-rate (VBR) burstiness </li></ul></ul></ul><ul><ul><ul><li>Timeliness and user-perceived quality are important </li></ul></ul></ul><ul><ul><li>There is no streaming access workload generator </li></ul></ul><ul><ul><ul><li>Workload generation is important for performance evaluation of Internet streaming content delivery techniques </li></ul></ul></ul>
  3. 3. GISMO: Characteristics
  4. 4. GISMO: Modeling <ul><li>Modeling Request Arrival Process </li></ul><ul><ul><li>Popularity distribution </li></ul></ul><ul><ul><ul><li>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. </li></ul></ul></ul><ul><ul><li>Temporal Correlation of Requests </li></ul></ul><ul><ul><ul><li>Requests to the objects tend to arrive non-randomly. Pareto distribution models the correlated inter-arrival time. </li></ul></ul></ul><ul><ul><li>Seasonal Patterns </li></ul></ul><ul><ul><ul><li>Aggregated request arrival rate can exhibit seasonal patterns (hourly, daily, weekly etc). GISMO users can define such diurnal patterns. </li></ul></ul></ul>
  5. 5. GISMO: Modeling <ul><li>Modeling Individual Requests </li></ul><ul><ul><li>Object Size Distribution </li></ul></ul><ul><ul><ul><li>Streaming media objects have a wide range of length. We use a power law to model it. </li></ul></ul></ul><ul><ul><li>Partial Access Patterns </li></ul></ul><ul><ul><ul><li>User interactions involves in streaming access. We use Pareto distribution to model the stop time. </li></ul></ul></ul><ul><ul><li>Variable Bit-Rate </li></ul></ul><ul><ul><ul><li>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. </li></ul></ul></ul>
  6. 6. GISMO: Modeling <ul><li>VBR self-similarity </li></ul><ul><ul><li>The bit-rate of streaming media objects (e.g., audio/video) exhibits long-range dependence. </li></ul></ul><ul><ul><li>The auto-correlation function decay slowly </li></ul></ul><ul><ul><li>Burstiness persists for long period, and implies the ineffectiveness of buffering </li></ul></ul><ul><li>Generating self-similar process FGN </li></ul><ul><ul><li>We use a random middle-point displacement algorithm </li></ul></ul><ul><li>Transforming VBR marginal distribution </li></ul><ul><ul><li>Gaussian  hybrid Lognormal/Pareto distribution </li></ul></ul>
  7. 7. GISMO: Functionality <ul><li>GISMO generates </li></ul><ul><ul><li>A set of bogus streaming media objects, installed in the servers which mimic real servers </li></ul></ul><ul><ul><li>Requests to these objects, initiated by the clients which mimic real users </li></ul></ul><ul><li>GISMO can be used for many purposes </li></ul><ul><ul><li>Evaluating the performance of streaming media servers, e.g., scheduling and I/O </li></ul></ul><ul><ul><li>Evaluating network protocols for streaming data transmission </li></ul></ul><ul><ul><li>Evaluating streaming data replication techniques (caching, pre-fetching, multicast merging, etc) </li></ul></ul>
  8. 8. GISMO: Architecture Network Media Player Media Player Media Player WWW Browser WWW Browser WWW Browser TCP RTSP UDP Web Server Streaming Server Requests Requests Requests Objects
  9. 9. GISMO: Use Case <ul><li>We have conducted a case performance study </li></ul><ul><ul><li>Using GISMO to generate workloads </li></ul></ul><ul><ul><li>Evaluating proxy caching and server stream merging techniques </li></ul></ul><ul><ul><li>Showing that how the workload characteristics impact their effectiveness </li></ul></ul>
  10. 10. GISMO: Use Case How does popularity impact the effectiveness of proxy caching (left) and server merging (right)
  11. 11. Future Directions <ul><ul><li>More client interactions in request streams, e.g., VCR functionality </li></ul></ul><ul><ul><li>More correlations in streaming media objects, e.g., Group-of-Picture GoP correlation </li></ul></ul><ul><ul><li>Using GISMO in evaluating streaming content delivery techniques </li></ul></ul><ul><ul><li>Using GISMO in evaluating network protocols for streaming data transmission </li></ul></ul>
  12. 12. Related Publications <ul><li>Shudong Jin and Azer Bestavros. Generating Streaming Access Workloads for Performance Evaluation and A Case Study . BU CS Technical Report, April 2001. </li></ul><ul><li>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. </li></ul><ul><li>Paul Barford and Mark Crovella. Generating Representative Web Workloads for Network and Server Performance Evaluation . ACM SIGMETRICS’1998. </li></ul>