.png){kind=icon}
.png){kind=icon}
Return
An energy-efficient task scheduling algorithm for heterogeneous cloud computing systems
.png){kind=icon}
PRE
AI
DOI：10.1007/s10586-018-2858-8.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The massive growth of cloud computing leads to huge amounts of energy consumption and release of carbon footprints as data centers are housed by a large number of servers. Consequently, the cloud service providers are looking for eco-friendly solutions to reduce energy consumption and carbon emissions. As a result, task scheduling has drawn attention, in which efficient resource utilization and minimum energy consumption take into great consideration. This is an exigent issue, especially for the heterogeneous environment. In this work, we put forward an energy-efficient task scheduling algorithm (ETSA) to address the demerits associated with task consolidation and scheduling. The proposed algorithm ETSA takes into account the completion time and total utilization of a task on the resources, and follows a normalization procedure to make a scheduling decision. We evaluate the proposed algorithm ETSA to measure energy efficiency and makespan in the heterogeneous environment. The experimental results are compared with recent algorithms, namely random, round robin, dynamic cloud list scheduling, energy-aware task consolidation, energy-conscious task consolidation and MaxUtil. The proposed algorithm ETSA provides an elegant trade-off between energy efficiency and makespan than the existing algorithms.
Keywords:
Cloud computing
Data center
Virtual machine
Task scheduling
Energy consumption
Task consolidation
Makespan
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
C
IF:
4.1
Papers:
5.0K
Citations:
7.5K