Self-Adaptive Resource Management for Large-Scale Shared Clusters  

作　　者：李研 陈峰宏 孙熙 周明辉 焦文品 曹东刚 梅宏 

机构地区：[1]Key Laboratory of High Confidence Software Technologies,Ministry of Education,Institute of Software School of Electronics Engineering and Computer Science,Peking University [2]CCF

出　　处：《Journal of Computer Science & Technology》2010年第5期945-957,共13页计算机科学技术学报（英文版）

基　　金：Supported by the National Basic Research 973 Program of China under Grant No.2009CB320700;the National High Technology Research and Development 863 Program of China under Grant Nos.2007AA010301,2008AA01Z139,2009AA01Z1391;the National Natural Science Foundation of China under Grant Nos.60603038,60773151.

摘　　要：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.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.

关 键 词：distributed system resource management SELF-ADAPTATION shared cluster 

分 类 号：TP338.8[自动化与计算机技术—计算机系统结构]