Many-objective performance enhancement in computing clusters
| Autor: | Sriram Chellappan, A. B. M. Alim Al Islam, A. S. M. Rizvi, Tarik Reza Toha, Siddhartha Shankar Das |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Optimization problem
business.industry Computer science Cloud computing Symmetric multiprocessor system 0102 computer and information sciences 02 engineering and technology Energy consumption computer.software_genre 01 natural sciences 010201 computation theory & mathematics 0202 electrical engineering electronic engineering information engineering Cluster (physics) 020201 artificial intelligence & image processing Algorithm design Data mining Cluster analysis business Performance enhancement computer |
| Zdroj: | IPCCC |
| DOI: | 10.1109/pccc.2017.8280491 |
| Popis: | In a heterogeneous computing cluster, cluster objectives are conflicting to each other. Selecting a right combination of machines is necessary to enhance cluster performance, and to optimize all the cluster objectives. In this paper, we perform empirical performance analyses of a real cluster with our year-long collected data, formulate a new many-objective optimization problem for clusters, and integrate a greedy approach with the existing NSGA-III algorithm to solve this problem. From our experimental results, we find our approach performs better than existing optimization approaches. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|