What is the future of disk drives, death or rebirth?
ACM Computing Surveys (CSUR)
Self-similarity: Behind workload reshaping and prediction
Future Generation Computer Systems
Computing the PUE of data centres by leveraging workload fluctuation
International Journal of High Performance Systems Architecture
| Hi-index | 0.00 |
Computer systems are now powerful enough to run multiple virtual machines (VMs), each one running a separate operating system (OS) instance. In such an environment, direct and centralized energy management employed by a single OS is unfeasible. Accurately predicting the idle intervals is one of the major approaches to save energy of disk drives. However, for the intensive workloads, it is difficult to find long idle intervals. Even if long idle intervals exist, it is very difficult for a predictor to catch the idle spikes in the workloads. This paper proposes to divide the workloads into buckets which are equal in time length, and predict the number of the forthcoming requests in each bucket instead of the length of the idle periods. By doing so, the bucket method makes the converted workload more predictable. The method also squeezes the executing time of each request to the end of its respective bucket, thus extending the idle length. By deliberately reshaping the workloads such that the crests and troughs of each workload become aligned, we can aggregate the peaks and the idle periods of the workloads. Due to the extended idle length caused by this aggregation, energy can be conserved. Furthermore, as a result of aligning the peaks, resource utilization is improved when the system is active. A trace driven simulator is designed to evaluate the idea. Three traces are employed to represent the workloads issued by three web servers residing in three VMs. The experimental results show that our method can save significant amounts of energy by sacrificing a small amount of quality of service.