Statistical workload shaping for storage systems

Data center computing is gaining popularity due to the cost efficiencies of consolidation, centralized management, and high reliability. The unpredictable bursty nature of typical workloads where the instantaneous arrival rates can significantly exceed the average long-term rate, requires the server to significantly over provision resources in order to meet response-time service level agreements (SLAs), resulting in low resource utilization and higher costs. In this paper we consider a statistical on-off model for bursty workloads to explore the relationship between burst size, frequency, capacity, and response time distribution. We analyze the performance of a workload shaping method to reduce capacity requirements by providing a graduated two-level SLA. Statistics of the request arrival to the overflow queue are characterized in terms of the underlying Markov chain passage times, and used to estimate its capacity.