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Shudong_-_Poster.ppt Presentation Transcript

  • 1. Generating Streaming Access Workload for Performance Evaluation Shudong Jin 3nd Year Ph.D. Student (Advisor: Azer Bestavros)
  • 2. 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?
      • 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 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. 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.