Integrating clustering and regression for workload estimation in the cloud
| Autor: | Peter Garraghan, Jie Xu, Farokh B. Bastani, Vasu Jindal, I-Ling Yen, Yongjia Yu |
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| Rok vydání: | 2020 |
| Předmět: |
Estimation
Scheme (programming language) Hardware_MEMORYSTRUCTURES Computer Networks and Communications business.industry Computer science 020206 networking & telecommunications Cloud computing Workload 02 engineering and technology Machine learning computer.software_genre Regression Computer Science Applications Theoretical Computer Science Task (computing) Computational Theory and Mathematics 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Cluster analysis business computer Software computer.programming_language |
| Zdroj: | Concurrency and Computation: Practice and Experience. 32 |
| ISSN: | 1532-0634 1532-0626 |
| Popis: | Workload prediction has been widely researched in the literature. However, existing techniques are per‐job based and useful for service‐like tasks whose workloads exhibit seasonality and trend. But cloud jobs have many different workload patterns and some do not exhibit recurring workload patterns. We consider job‐pool‐based workload estimation, which analyzes the characteristics of existing tasks' workloads to estimate the currently running tasks' workload. First cluster existing tasks based on their workloads. For a new task J, collect the initial workload of J and determine which cluster J may belong to, then use the cluster's characteristics to estimate J′s workload. Based on the Google dataset, the algorithm is experimentally evaluated and its effectiveness is confirmed. However, the workload patterns of some tasks do have seasonality and trend, and conventional per‐job‐based regression methods may yield better workload prediction results. Also, in some cases, some new tasks may not follow the workload patterns of existing tasks in the pool. Thus, develop an integrated scheme which combines clustering and regression and utilize the best of them for workload prediction. Experimental study shows that the combined approach can further improve the accuracy of workload prediction. |
| Databáze: | OpenAIRE |
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