Abstract
In recent years, a large and growing body of literature has addressed the energy-efficient resource management problem in data centers. Due to the fact that cooling costs still remain the major portion of the total data center energy cost, thermal-aware resource management techniques have been employed to make additional energy savings. In this paper, we formulate the problem of minimizing the total energy consumption of a heterogeneous data center (MITEC) as a non-linear integer optimization problem. We consider both computing and cooling energy consumption and provide a thermal-aware Virtual Machine (VM) allocation heuristic based on the genetic algorithm. Experimental results show that, using the proposed formulation, up to 30 % energy saving is achieved compared to thermal-aware greedy algorithms and power-aware VM allocation heuristics.
Highlights
Cloud computing has emerged as a new and popular computing paradigm for providing on-demand hosted services over the Internet [1,2]
30% energy savings are achieved by our heuristics in higher utilizations in comparison with other greedy Virtual Machine (VM) allocation heuristics
And 4, MITEC-GA can efficiently reduce both computing and cooling energy. It can reduce more cooling energy especially when the utilization of servers is high (i.e., VM number = 620). This is because MITEC-GA turns on more chassis to obtain load balancing within the system, avoiding the creation of hotspots
Summary
Cloud computing has emerged as a new and popular computing paradigm for providing on-demand hosted services over the Internet [1,2]. It improves resource utilization, reduces the energy consumption through the consolidation of multiple workloads on fewer servers, and enables live migration [28,29,30] It can provide load balancing between servers by enabling Virtual Machine migration to eliminate thermal hotspots in data centers. Creating a formal definition of optimal thermal-aware VM allocation by considering both computing and cooling energy consumption and providing a novel heuristic based on a genetic algorithm to obtain a near-optimal solution in less computing time; Designing a trade-off between the power-aware consolidation techniques and thermal-aware load balancing approaches to obtain higher energy savings in Cloud data centers; An extensive simulation-based evaluation and performance analysis of the proposed algorithm.
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call