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Cost-aware cooperative resource provisioning for heterogeneous workloads in data centers.
| Content Provider | CiteSeerX |
|---|---|
| Author | Zhan, Jianfeng Wang, Lei Li, Xiaona Shi, Weisong Weng, Chuliang Zhang, Wenyao Zang, Xiutao |
| Abstract | Abstract—Recent cost analysis shows that the server cost still dominates the total cost of high-scale data centers or cloud systems. In this paper, we argue for a new twist on the classical resource provisioning problem: heterogeneous workloads are a fact of life in large-scale data centers, and current resource provisioning solutions do not act upon this heterogeneity. Our contributions are threefold: first, we propose a cooperative resource provisioning solution, and take advantage of differences of heterogeneous workloads so as to decrease their peak resources consumption under competitive conditions; second, for four typical heterogeneous workloads: parallel batch jobs, Web servers, search engines, and MapReduce jobs, we build an agile system PhoenixCloud that enables cooperative resource provisioning; and third, we perform a comprehensive evaluation for both real and synthetic workload traces. Our experiments show that our solution could save the server cost aggressively with respect to the non-cooperative solutions that are widely used in state-of-the-practice hosting data centers or cloud systems: e.g., EC2, which leverages the statistical multiplexing technique, or RightScale, which roughly implements the elastic resource provisioning technique proposed in related state-of-the-art work. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Heterogeneous Workload Cost-aware Cooperative Resource Provisioning Data Center Cloud System Server Cost Cooperative Resource Agile System Phoenixcloud New Twist Search Engine Mapreduce Job Related State-of-the-art Work State-of-the-practice Hosting Data Center Typical Heterogeneous Workload Classical Resource Synthetic Workload Trace Statistical Multiplexing Technique Large-scale Data Center Cooperative Resource Provisioning Peak Resource Consumption Web Server Current Resource Provisioning Solution Abstract Recent Cost Analysis Comprehensive Evaluation Parallel Batch Job Elastic Resource Provisioning Technique Competitive Condition High-scale Data Center Non-cooperative Solution Total Cost |
| Content Type | Text |
