An Integer Linear Programming model and Adaptive Genetic Algorithm approach to minimize energy consumption of Cloud computing data centers

Ibrahim, Huda; Aburukba, Raafat; El-Fakih, Khaled

doi:10.1016/j.compeleceng.2018.02.028

Cited by 51 publications

(19 citation statements)

References 6 publications

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“…According to [22], for reducing the problem which arises due to the consumption of huge energy in the data centre, there can be implemented an Integer Linear Programming model for developing an algorithm of dynamic task scheduling. He also focused on the presenting of an Adaptive Genetic Algorithm to depict dynamic characteristics which are related with Cloud platform.…”

Section: Literature Reviewmentioning

confidence: 99%

A detailed Study on Data Centre Energy Efficiency and Efficient Cooling Techniques

2020

IJATCSE

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In this era of modern technology, the cloud computing is considered as the most vibrant and indispensable part of the world of Information & Communication Technologies which is shaping the modern networking technology by introducing a new door with technical experiments for conserving the environment. It is empowered to rise the energy efficiency by reducing the carbon emissions that leads the transformation of cloud computing into green cloud computing. The buzzword term "Green cloud" is the combination of two terms "green" and "cloud" to symbolized the concept of ecofriendly environment with the Internet that is basically known as cloud computing. Even though the immense power of modern technology already gripped the environment and destroying the nature and environment that is not a renewable resource and it obvious has a great impact on life. Therefore it rises the awareness regarding environmental impact of the computing and so that green technology is gaining the importance accordingly. In this survey paper, we define a systematic approach to analyse different data centres' energy efficiency along with the concepts of diminishing the cost of the data centres by utilizing the cooling systems efficiently.

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Section: Literature Reviewmentioning

confidence: 99%

A detailed Study on Data Centre Energy Efficiency and Efficient Cooling Techniques

2020

IJATCSE

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“…Thus, a multi-objective ant colony system algorithm is designed to obtain the Pareto set. Ibrahim et al [17] presented an adaptive genetic algorithm to achieve energy efficient while considering the response time. Raju et al [18] proposed an algorithm called EAMOCA in the hybrid cloud, which aimed to minimize execution time and energy consumption while maximizing the resource utilization.…”

Section: Related Workmentioning

confidence: 99%

An Energy-Aware Algorithm for Virtual Machine Placement in Cloud Computing

Zhao

Zhou

2019

IEEE Access

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Virtualization technology, as a key technology in cloud computing, makes the virtual machine placement (VMP) play an important role in improving the energy efficiency of data centers. In this paper, an energy-aware algorithm named GATA is proposed for the VMP problem. It combines the genetic algorithm with the tabu search algorithm. The goal is to obtain an optimal VMP scheme to achieve energy efficiency while maximizing load balance among various resources. The algorithm is compared with two meta-heuristic algorithms and a newly proposed algorithm that is based on ant colony algorithm. The execution time of these algorithms is also discussed. The results show that the proposed algorithm is superior to those methods mentioned above. INDEX TERMS Genetic algorithm, power-aware, tabu search algorithm, virtual machine placement.

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“…An easy-to-conceived idea is that the entire application is divided into smaller subtasks, appropriate migration strategies are invoked, migration destinations are determined, and task assignments are completed [21]. The so-called good migration strategy means that the terminal energy consumption can be effectively reduced, and the task completion time also needs to meet the user delay expectation constraint.…”

Section: Energy Consumption Minimizationmentioning

confidence: 99%

An energy saving based on task migration for mobile edge computing

Wang

Zhu

Hei

et al. 2019

J Wireless Com Network

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Mobile edge computing (MEC), as the key technology to improve user experience in a 5G network, can effectively reduce network transmission delay. Task migration can migrate complex tasks to remote edge servers through wireless networks, solving the problems of insufficient computing capacity and limited battery capacity of mobile terminals. Therefore, in order to solve the problem of "how to realize low-energy migration of complex dependent applications," a subtask partitioning model with minimum energy consumption is constructed based on the relationship between the subtasks. Aiming at the problem of execution time constraints, the genetic algorithm is used to find the optimal solution, and the migration decision results of each subtask are obtained. In addition, an improved algorithm based on a genetic algorithm is proposed to dynamically adjust the optimal solution obtained by genetic algorithm by determining the proportion of task energy consumption and mobile phone residual power. According to the experimental results, it can be concluded that the fine-grained task migration strategy combines the advantages of mobile edge computing, not only satisfies the smooth execution of tasks, but also reduces the energy consumption of terminal mobile devices. In addition, experiments show that the improved algorithm is more in line with users' expectations. When the residual power of mobile devices is reduced to a certain value, tasks are migrated to MEC server to prolong standby time.

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An Integer Linear Programming model and Adaptive Genetic Algorithm approach to minimize energy consumption of Cloud computing data centers

Cited by 51 publications

References 6 publications

A detailed Study on Data Centre Energy Efficiency and Efficient Cooling Techniques

A detailed Study on Data Centre Energy Efficiency and Efficient Cooling Techniques

An Energy-Aware Algorithm for Virtual Machine Placement in Cloud Computing

An energy saving based on task migration for mobile edge computing

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