ADuS: Adaptive resource allocation in cluster systems under heavy-tailed and bursty workloads

A large-scaled cluster system has been employed in various areas by offering pools of fundamental resources. How to effectively allocate the shared resources in a cluster system is a critical but challenging issue, which has been extensively studied in the past few years. Despite the fact that classic load balancing policies, such as Random, Join Shortest Queue and size-based polices, are widely implemented in actual systems due to their simplicity and efficiency, the performance benefits of these policies diminish when workloads are highly variable and heavily dependent. In this paper, we propose a new load balancing policy named ADuS, which attempts to partition jobs according to their sizes and to further rank the servers based on their loads. By dispatching jobs of similar size to the servers with the same ranking, ADuS can adaptively balance user traffic and system load in the system and thus achieve significant performance benefits. Extensive simulations show the effectiveness and the robustness of ADuS under many different environments.