Improve the Energy Efficiency of Datacenters With the Awareness of Workload Variability

Abstract

In modern datacenters, huge energy consumption is a significant problem that remains to be solved. Previous works reduce the system energy consumption by switching the idle servers to a low-power state. However, the workload demands on servers change dynamically and mainly depend on the real-time workload status. To maintain the system energy efficiency, when providing servers with some servers reserved, the status of workloads should be carefully considered. Generally, the status of workloads is characterized by some key factors sampled from the workloads. However, under dynamic workload demands, the accuracy of these sampled values varies. To accurately assess the status of server workloads, in this paper, we propose a Dynamic Time Scale based server Provision (DTSP) method, which takes the variability of workloads into consideration when providing servers for workload demands. To obtain accurate factor values indicating real-time workload, DTSP samples several key workload factors, including the coefficient of variation of arrival intervals, the request arrival rates of current workload and previous workload, and the mean service time of current requests, with a dynamic compatible rate. With these sampled factors, DTSP can accurately estimate the demands of workloads on servers and provide appropriate numbers of servers for the dynamic workloads. Extensive experiments demonstrate that taking the workload variability into consideration, DTSP can significantly promote the energy efficiency of a datacenter.