Journal Article10.1007/S10586-020-03053-X
Efficient dynamic resource allocation method for cloud computing environment
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TL;DR: A dynamic resource allocation model that can meet customer demand for resources with improved and faster responsiveness is presented and a multi-objective search algorithm called Spacing Multi-Objective Antlion algorithm (S-MOAL) is proposed to minimize both the makespan and the cost of using virtual machines.
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Abstract: The dynamic resource allocation is a good feature of the cloud computing environment. However, it faces serious problems in terms of service quality, fault tolerance, and energy consumption. It was necessary, then, to find an effective method that can effectively address these important issues and increase cloud performance. This paper presents a dynamic resource allocation model that can meet customer demand for resources with improved and faster responsiveness. It also proposes a multi-objective search algorithm called Spacing Multi-Objective Antlion algorithm (S-MOAL) to minimize both the makespan and the cost of using virtual machines. In addition, its impact on fault tolerance and energy consumption was studied. The simulation revealed that our method performed better than the PBACO, DCLCA, DSOS and MOGA algorithms, especially in terms of makespan.
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Citations
Heuristic initialization of PSO task scheduling algorithm in cloud computing
TL;DR: An improved initialization of particle swarm optimization (PSO) using heuristic algorithms using longest job to fastest processor (LJFP) and minimum completion time (MCT) algorithms is proposed and the performance of the proposed algorithms is compared with recent task scheduling methods.
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Managing overloaded hosts for energy-efficiency in cloud data centers
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Multi-objective workflow scheduling in cloud computing: trade-off between makespan and cost
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TL;DR: In this paper, the authors proposed a HEFT-ACO approach, which is based on the heterogeneous earliest end time (HEFT), and the ant colony algorithm (ACO) to minimize the makespan and the cost at the same time.
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HWACOA Scheduler: Hybrid Weighted Ant Colony Optimization Algorithm for Task Scheduling in Cloud Computing
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TL;DR: In this article , the authors proposed an ideal and optimal task scheduling algorithm that is tested and compared with other existing algorithms in terms of efficiency, makespan, and cost parameters, that is, they tried to explain and solve the task scheduling problem using an improved meta-heuristic algorithm called the Hybrid Weighted Ant Colony Optimization (HWACO) algorithm, which is an advanced form of the already present ant colony optimization Algorithm.
Towards Resilient Method: An exhaustive survey of fault tolerance methods in the cloud computing environment
Muhammad Asim Shahid,Muhammad Asim Shahid,Noman Islam,Noman Islam,Muhammad Alam,M. S. Mazliham,Shahrulniza Musa +6 more
TL;DR: This survey offers a comprehensive and detailed description of the various faults kinds, factors, & different methods to fault tolerance used in the cloud and offers a comparative study of the structures under the article.
50
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