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

2. Computer Science 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. Computer Science GISMO: Characteristics

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

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

11. Computer Science 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. Computer Science 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.