Energy-efficient dynamic clusters of servers

• Published in: The Journal of supercomputing Vol. 71; no. 5; pp. 1642 - 1656
• Main Authors: Duolikun, Dilawaer, Enokido, Tomoya, Aikebaier, Ailixier, Takizawa, Makoto
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-05-2015
• Subjects: Algorithms; Central processing units; Clusters; Compilers; Computation; Computer Science; Dynamics; Electric power; Interpreters; Power consumption; Processor Architectures; Programming Languages; Servers; Dynamic cluster; Energy-aware cluster; Storage- and computation-based power consumption (SCBPC) model; Power consumption mode; Storage- and computation-based processing (SCB) model
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-014-1261-3

Electric power consumed by servers has to be reduced in order to realize green societies. We consider computation (CP) and storage (ST) types of application processes performed on servers in this paper, where CPU and storage drives are mainly used, respectively. In the storage- and computation-based power consumption model proposed by the authors, the power consumption rate of a server depends on what types of processes are performed but is independent of how may processes are performed on the server. In the storage- and computation-based processing model, the execution time of an ST process depends on the number of concurrent CP and ST processes but the execution time of a CP process depends on only CP processes and is independent of ST processes. In our previous studies, the energy-aware algorithm is discussed to select a server in a cluster of servers for each request so that the total power consumption of the servers can be reduced. However, a server consumes electric power even if the server is idle, i.e. no process is performed. In this paper, we discuss a dynamic energy-aware (DEA) cluster which includes only active servers where at least one process is performed. A server for each request is selected in a dynamic cluster so that the total power consumption of servers in the cluster can be reduced. We evaluate the DEA algorithm in terms of the total power consumption and average execution time and show the total power consumption can be reduced.