Cooling aware job migration for reducing cost in cloud environment

Naserian, Elahe; Ghoreyshi, Seyed Mohammad; Shafiei, Hossein; Mousavi, Payam; Khonsari, Ahmad

doi:10.1007/s11227-014-1349-9

Cited by 5 publications

(1 citation statement)

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“…A considerable amount of literature has been published on energy-efficient computing in data centers [10,11]. One of the most significant parts of the energy consumption in data centers appears to be the energy consumed in the cooling system [12,13]. Recent cooling technologies, such as in-rack, in-row, and container-based cooling, have been proposed to improve data centers' energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

Thermal-Aware Virtual Machine Allocation for Heterogeneous Cloud Data Centers

2020

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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.

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Section: Introductionmentioning

confidence: 99%