Multiobjective Virtual Machine Selection for Task Scheduling in Cloud Computing

2018 ◽  
pp. 319-331 ◽  
Author(s):  
Ketaki Naik ◽  
G. Meera Gandhi ◽  
S. H. Patil
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Task Scheduling ◽  
Machine Selection ◽  
Selection For

Optimal virtual machine selection for anomaly detection using a swarm intelligence approach

2019 ◽  
Vol 84 ◽  
pp. 105686 ◽  
Author(s):  
Aravinthkumar Selvaraj ◽  
Rizwan Patan ◽  
Amir H. Gandomi ◽  
Ganesh Gopal Deverajan ◽  
Manjula Pushparaj
Keyword(s):  
Anomaly Detection ◽  
Swarm Intelligence ◽  
Virtual Machine ◽  
Machine Selection ◽  
Selection For ◽  
Intelligence Approach

scholarly journals GCWOAS2: Multiobjective Task Scheduling Strategy Based on Gaussian Cloud-Whale Optimization in Cloud Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Lina Ni ◽  
Xiaoting Sun ◽  
Xincheng Li ◽  
Jinquan Zhang
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Task Scheduling ◽  
Completion Time ◽  
Cloud Model ◽  
Operating Cost ◽  
Optimal Scheduling ◽  
Search Range ◽  
Scheduling Strategy ◽  
Whale Optimization

An important challenge facing cloud computing is how to correctly and effectively handle and serve millions of users’ requests. Efficient task scheduling in cloud computing can intuitively affect the resource configuration and operating cost of the entire system. However, task and resource scheduling in a cloud computing environment is an NP-hard problem. In this paper, we propose a three-layer scheduling model based on whale-Gaussian cloud. In the second layer of the model, a whale optimization strategy based on the Gaussian cloud model (GCWOAS2) is used for multiobjective task scheduling in a cloud computing which is to minimize the completion time of the task via effectively utilizing the virtual machine resources and to keep the load balancing of each virtual machine, reducing the operating cost of the system. In the GCWOAS2 strategy, an opposition-based learning mechanism is first used to initialize the scheduling strategy to generate the optimal scheduling scheme. Then, an adaptive mobility factor is proposed to dynamically expand the search range. The whale optimization algorithm based on the Gaussian cloud model is proposed to enhance the randomness of search. Finally, a multiobjective task scheduling algorithm based on Gaussian whale-cloud optimization (GCWOA) is presented, so that the entire scheduling strategy can not only expand the search range but also jump out of the local maximum and obtain the global optimal scheduling strategy. Experimental results show that compared with other existing metaheuristic algorithms, our strategy can not only shorten the task completion time but also balance the load of virtual machine resources, and at the same time, it also has a better performance in resource utilization.

scholarly journals Cloud Computing Task Scheduling Model Based on Improved Whale Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
LiWei Jia ◽  
Kun Li ◽  
Xiaoming Shi
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Task Scheduling ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Virtual Machines ◽  
Whale Optimization Algorithm ◽  
Distribution Model ◽  
Particle Swarm Algorithm ◽  
Whale Optimization

The efficiency of task scheduling under cloud computing is related to the effectiveness of users. Aiming at the problems of long scheduling time, high cost consumption, and large virtual machine load in cloud computing task scheduling, an improved scheduling efficiency algorithm (called the improved whale optimization algorithm, referred to as IWC) is proposed. Firstly, a cloud computing task scheduling and distribution model with time, cost, and virtual machines as the main factors is constructed. Secondly, a feasible plan for each whale individual corresponding to cloud computing task scheduling is to find the best whale individual, which is the best feasible plan; in order to better find the optimal individual, we use the inertial weight strategy for the whale optimization algorithm to improve the local search ability and effectively prevent the algorithm from reaching premature convergence; we use the add operator and delete operator to screen individuals after each iteration which is completed and updated to improve the quality of understanding. In the simulation experiment, IWC was compared with the ant colony algorithm, particle swarm algorithm, and whale optimization algorithm under a different number of tasks. The results showed that the IWC algorithm has good results in terms of task scheduling time, scheduling cost, and virtual machine. The application is in cloud computing task scheduling.

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