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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.
Aggregated request arrival rate can exhibit seasonal patterns (hourly, daily, weekly etc). GISMO users can define such diurnal patterns.
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.
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.
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
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)
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
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
GISMO: Use Case How does popularity impact the effectiveness of proxy caching (left) and server merging (right)
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
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.