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MediaGuard: a Model-Based Framework for Building QoS- aware Streaming Media Services
Cherkasova, Ludmila; Tang, Wenting; Vahdat, Amin
HPL-2004-25
Keyword(s): benchmarking; enterprise media servers; workload analysis; performance modeling; synthetic workload generator; admission control
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. First, workload measurements of existing media services indicate a "peak-to-mean" workload variance of more than one order of magnitude. It is difficult to overprovision service resources for such a highly variable workload, making the adaptive resource allocation and economies of scale of a shared hosting environment attractive for streaming media services. Second, in emerging workloads based on enterprise, news, and music content, a significant portion of the content is short and encoded at low bit rates. Additionally, media workloads display a strong temporal and spatial locality. This makes modem servers with gigabytes of main memory well suited to deliver most of the accesses to popular files from memory. Finally, end- point admission control for streaming services is more important than for traditional web services because a streaming media object delivered in the face of insufficient server resources is doubly bad, with wasted work at the server often resulting in aborted connection at the client. We present MediaGuard -a model-based infrastructure for building QoS-aware 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, segment-based memory model of a media server to efficiently determine 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 a novel, more accurate admission control policy for streaming media servers that accounts for the impact of the server's main memory file buffer cache. Our evaluation shows that, relative to a pessimistic admission control policy that assumes that all content must be served from disk, MediaGuard delivers a factor of two improvement in server throughput.
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