Thermal-Aware File and Resource Allocation in Data Centers

Abstract

After addressing the issue of reducing power consumption by computing nodes in data centers, in recent years, computer scientists are focusing on reducing cooling cost of the data centers, thereby making the data centers thermal-aware. Due to the dramatic increase in power-density of data centers, thermal-management strategies are gaining more and more significance in the area of high performance computing data centers. Most of the previous studies towards achieving this goal focus on the nodes performing computation-intensive tasks, in which, the processors are the major consumer of the node’s power. However, there is a lack of study on the ways of thermal-aware data placement in storage clusters housing thousands of storage nodes, where the disk subsystems are the major power consumer.

In this dissertation, we propose thermal-aware file and resource allocation policies to reduce the cooling cost of data centers. We first propose a thermal-aware file assignment policy -TIGER- to make file assignments in distributed storage clusters, followed by out resource allocation policy- TASH, which addresses the issue of thermal management in specific framework (Hadoop). Our proposed thermal-aware policies utilize nodes’ contributions towards heat re-circulation in data centers while making file and resource allocation decisions. The proposed policies make use of cross-interference matrix to calculate node’s contribution in heat re-circulation. Our experimental results show that the proposed policies significantly reduce the cooling cost of data centers as compared to existing thermal-aware policies, and at the same time, maintains performance penalties within acceptable margin.