FlexSplit: A Workload-Aware, Adaptive Load Balancing Strategy for Media Clusters

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.