Energy-efficient task scheduling algorithms for cloud data centers
TL;DR: ESF-ES algorithm is developed which focuses on minimizing energy consumption by minimizing the number of servers used, and the comparison is made with hybrid algorithms and most-efficient-server first scheme.
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Abstract: Cloud computing is a modern technology which contains a network of systems that form a cloud. Energy conservation is one of the major concern in cloud computing. Large amount of energy is wasted by the computers and other devices and the carbon dioxide gas is released into the atmosphere polluting the environment. Green computing is an emerging technology which focuses on preserving the environment by reducing various kinds of pollutions. Pollutions include excessive emission of greenhouse gas, disposal of e-waste and so on leading to greenhouse effect. So pollution needs to be reduced by lowering the energy usage. By doing this, utilization of resources should not be reduced. With less usage of energy, maximum resource utilization should be possible. For this purpose, many green task scheduling algorithms are used so that the energy consumption can be minimized in servers of cloud data centers. In this paper, ESF-ES algorithm is developed which focuses on minimizing energy consumption by minimizing the number of servers used. The comparison is made with hybrid algorithms and most-efficient-server first scheme.
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Citations
Energy-aware scheduling algorithm for time-constrained workflow tasks in DVFS-enabled cloud environment
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TL;DR: A new energy-aware scheduling algorithm for time-constrained workflow tasks is proposed using the DVFS method in which the host reduces the operating frequency using different voltage levels, which performs more efficiently when evaluating metrics such as energy utilization, average execution time, average resource utilization and average SLA violation.
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TL;DR: The proposed work ENNEGCC 3D gives a novel 3-Dimension Neural Network Predictor model to estimate the workload and avoids unwanted status change in server thus greatly reduces the power consumption.
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Energy-conscious resource scheduling in cloud computing environment: A pragmatic view
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References
A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems
Anton Beloglazov,Anton Beloglazov,Rajkumar Buyya,Rajkumar Buyya,Young Choon Lee,Young Choon Lee,Albert Y. Zomaya,Albert Y. Zomaya +7 more
TL;DR: This study discusses causes and problems of high power/energy consumption, and presents a taxonomy of energy-efficient design of computing systems covering the hardware, operating system, virtualization, and data center levels.
•Proceedings Article
Genetic Algorithms for Task Scheduling Problem.
Fatma A. Omara,Mona M. Arafa +1 more
- 01 Jan 2009
TL;DR: In this paper, two genetic algorithms have been developed and implemented, which are genetic algorithms with some heuristic principles that have been added to improve the performance according to the first developed genetic algorithm, two fitness functions have been applied one after the other.
259
Performance evaluation of a Green Scheduling Algorithm for energy savings in Cloud computing
Truong Vinh Truong Duy,Yukinori Sato,Yasushi Inoguchi +2 more
- 19 Apr 2010
TL;DR: A Green Scheduling Algorithm integrating a neural network predictor for optimizing server power consumption in Cloud computing by minimizing the energy use at the points of consumption to benefit all other levels is designed and implemented.
255
Energy-aware parallel task scheduling in a cluster
Lizhe Wang,Lizhe Wang,Samee U. Khan,Dan Chen,Joanna Kolodziej,Rajiv Ranjan,Cheng-Zhong Xu,Albert Y. Zomaya +7 more
TL;DR: Formal models are presented for precedence-constrained parallel tasks, DVFS-enabled clusters, and energy consumption, and proposed scheduling heuristics to reduce energy consumption of a tasks execution are developed.
213
Genetic algorithms for task scheduling problem
Fatma A. Omara,Mona M. Arafa +1 more
TL;DR: Two genetic algorithms are developed with some heuristic principles that have been added to improve the performance and it has been found that the developed algorithms always outperform the traditional algorithms.
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