Self-Adaptive Resource Management for Large-Scale Shared Clusters |
| |
| Authors: | Yan Li Feng-Hong Chen Xi Sun Ming-Hui Zhou Wen-Pin Jiao Dong-Gang Cao Hong Mei |
| |
| Affiliation: | 1.Key Laboratory of High Confidence Software Technologies, Ministry of Education, Institute of Software School of Electronics Engineering and Computer Science,Peking University,Beijing,China |
| |
| Abstract: | In a shared cluster, each application runs on a subset of nodes and these subsets can overlap with one another. Resource management
in such a cluster should adaptively change the application placement and workload assignment to satisfy the dynamic applications
workloads and optimize the resource usage. This becomes a challenging problem with the cluster scale and application amount
growing large. This paper proposes a novel self-adaptive resource management approach which is inspired from human market:
the nodes trade their shares of applications' requests with others via auction and bidding to decide its own resource allocation
and a global high-quality resource allocation is achieved as an emergent collective behavior of the market. Experimental results
show that the proposed approach can ensure quick responsiveness, high scalability, and application prioritization in addition
to managing the resources effectively. |
| |
| Keywords: | |
| 本文献已被 万方数据 SpringerLink 等数据库收录！ |
|
