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ALARM: Autonomic Load-Aware Resource Management for P2P Key-Value Stores in Cloud

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3 Author(s)
Can Zhang ; Sch. of Software, Shanghai Jiao Tong Univ., Shanghai, China ; Haopeng Chen ; Shuotao Gao

This paper presents ALARM, an autonomic load-aware resource management algorithm that can be used to manage physical machines or virtual machines in cloud, which participate in a P2P key-value store. A lot of existing key-value stores claim that they are elastic enough to scale up or down with no downtime or interruption to applications. However, the question that when the scaling up or down should take place has still not been resolved. The situation may get worse if the data store consists of hundreds of machines, for it's unrealistic for a system administrator to monitor the system and add/remove a machine manually. Fortunately, cloud computing and virtualization technology have enabled the real-time provision of virtual machines and a way of managing virtual machines without human interference. By supervising the utilization of multiple resources (CPU, memory, network IO, etc.) in virtual machines hosting the data store, our ALARM algorithm will take effect when some of the machines become overloaded or under loaded. The experiment result shows that ALARM helps the Open Chord data store, an open-source implementation of the Chord protocol, scale up and down according to the resource usage in the virtual machines.

Published in:

Dependable, Autonomic and Secure Computing (DASC), 2011 IEEE Ninth International Conference on

Date of Conference:

12-14 Dec. 2011