Presentation on theme: "Computer Science Generating Streaming Access Workload for Performance Evaluation Shudong Jin 3nd Year Ph.D. Student (Advisor: Azer Bestavros)"— Presentation transcript:
Computer Science Generating Streaming Access Workload for Performance Evaluation Shudong Jin 3nd Year Ph.D. Student (Advisor: Azer Bestavros)
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
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.
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.
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
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)
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
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
Computer Science GISMO: Use Case How does popularity impact the effectiveness of proxy caching (left) and server merging (right)
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
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.