A number of technology and workload trends motivate us to consider a new request distribution and load balancing strategy for streaming media clusters. First, in emerging media workloads, 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 modern servers with gigabytes of main memory well suited to deliver a large fraction of accesses to popular files from memory. Second, a specific characteristic of streaming media workloads is that many clients do not finish playing an entire media file which results from the browsing nature of a large fraction of client accesses. In this paper, we propose and evaluate two new load-balancing strategies for media server clusters. The proposed strategies, FlexSplit and FlexSplitLard aim to efficiently utilize the combined cluster memory by exploiting specific media workload properties by "tuning" their behavior to media file popularity changes. The ability of the proposed policies to self-adapt to changing workloads across time while maintaining high performance makes these strategies an attractive choice for load balancing in media server clusters.
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, segment-based memory model of a media server to efficiently determine in liner 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 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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