Book Chapter10.1007/978-981-13-1921-1_36
Modified Ant Colony Optimization Algorithm for Task Scheduling in Cloud Computing Systems
G. Narendrababu Reddy,S. Phani Kumar +1 more
- 01 Jan 2019
- pp 357-365
13
TL;DR: MACO algorithm will improve performance of task scheduling by reducing makespan and degree of imbalance comparatively lower than basic ACO algorithm and to perform multi-objective task scheduling (MOTS) process.
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Abstract: Cloud computing is the development of distributed computing, parallel computing, and grid computing, or defined as commercial implementation of such computer science concepts. One among the day-to-day challenges in cloud computing environment is task scheduling (TS). TS is the process of allocating cloudlets to virtual machines (VM) in a cloud architecture with a concern of effective load balance and efficient utilization of resources. With the aim of facing challenges in cloud task scheduling, many non-deterministic polynomial time-hard optimization problem-solving techniques and many meta-heuristic (MH) algorithms have been proposed to solve it. A task scheduler should adapt its scheduling strategy to changing environment and variable tasks. This paper amends a cloud task scheduling policy based on modified ant colony optimization (MACO) algorithm. Main contribution of recommended scheme is to minimize makespan and to perform multi-objective task scheduling (MOTS) process. MACO algorithm will improve performance of task scheduling by reducing makespan and degree of imbalance comparatively lower than basic ACO algorithm.
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Citations
A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments
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318
Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing
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TL;DR: Wang et al. as mentioned in this paper designed an adaptive task scheduling algorithm for cloud computing based on ant colony algorithm, and a pheromone adaptive update adjustment mechanism was added to improve the convergence speed of the algorithm and effectively avoid the emergence of local optimal solutions.
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HATMOG: an enhanced hybrid task assignment algorithm based on AHP-TOPSIS and multi-objective genetic in cloud computing
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References
Cloud Task Scheduling Based on Load Balancing Ant Colony Optimization
Kun Li,Gaochao Xu,Guangyu Zhao,Yushuang Dong,Dan Wang +4 more
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TL;DR: A cloud task scheduling policy based on Load Balancing Ant Colony Optimization (LBACO) algorithm is proposed to balance the entire system load while trying to minimizing the make span of a given tasks set.
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A novel collaborative optimization algorithm in solving complex optimization problems
Wu Deng,Huimin Zhao,Huimin Zhao,Li Zou,Li Zou,Li Zou,Guangyu Li,Guangyu Li,Xinhua Yang,Daqing Wu,Daqing Wu +10 more
- 01 Aug 2017
TL;DR: The experiment results show that the proposed MGACACO algorithm can avoid falling into the local extremum, and takes on better search precision and faster convergence speed.
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A Multi-Objective Optimization Scheduling Method Based on the Ant Colony Algorithm in Cloud Computing
TL;DR: Experimental results show that based on these four metrics, a multi-objective optimization method is better than other similar methods, especially as it increased 56.6% in the best case scenario.
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A Hyper-Heuristic Scheduling Algorithm for Cloud
Chun-Wei Tsai,Wei-Cheng Huang,Meng-Hsiu Chiang,Ming-Chao Chiang,Chu-Sing Yang +4 more
- 07 Apr 2014
TL;DR: The results show that HHSA can significantly reduce the makespan of task scheduling compared with the other scheduling algorithms evaluated in this paper, on both CloudSim and Hadoop.
215
Task scheduling in Cloud computing
Abdul Razaque,Nikhileshwara Reddy Vennapusa,Nisargkumar Soni,Guna Sree Janapati,khilesh Reddy Vangala +4 more
- 29 Apr 2016
TL;DR: An efficient task-scheduling algorithm is introduced, which presents divisible task scheduling by considering network bandwidth, and uses a nonlinear programming model for divisibletask scheduling, which assigns the correct number of tasks to each virtual machine.
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