A unified benchmarking and model-based framework for building QoS-aware streaming media services |
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Authors: | Ludmila Cherkasova Wenting Tang Amin Vahdat |
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Affiliation: | (1) Hewlett-Packard Laboratories, 1501 Page Mill Road, Palo Alto, CA 94303, USA;(2) Department of Computer Science and Engineering, University of California, San Diego, CA, USA;(3) Present address: VMware Inc., 3145 Porter Dr., Palo Alto, CA 94304, USA |
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Abstract: | A number of technology and workload trends motivate us to consider the appropriate resource allocation mechanisms and policies for streaming media services in shared cluster environments. We present MediaGuard – a model-based infrastructure for building streaming media services – that can efficiently determine the fraction of server resources required to support a particular client request over its expected lifetime. The proposed solution is based on a unified cost function that uses a single value to reflect overall resource requirements such as the CPU, disk, memory, and bandwidth necessary to support a particular media stream based on its bit rate and whether it is likely to be served from memory or disk. We design a novel, time-segment-based memory model of a media server to efficiently determine in linear time whether a request will incur memory or disk access when given the history of previous accesses and the behavior of the server's main memory file buffer cache. Using the MediaGuard framework, we design two media services: (1) an efficient and accurate admission control service for streaming media servers that accounts for the impact of the server's main memory file buffer cache, and (2) a shared streaming media hosting service that can efficiently allocate the predefined shares of server resources to the hosted media services, while providing performance isolation and QoS guarantees among the hosted services. Our evaluation shows that, relative to a pessimistic admission control policy that assumes that all content must be served from disk, MediaGuard (as well as services that are built using it) deliver a factor of two improvement in server throughput. |
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Keywords: | Streaming media servers Benchmarking Modeling Admission control System performance Analysis Shared streaming media hosting service |
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