Prioritized Energy Efficient Task Scheduling Algorithm in Cloud Computing Using Whale Optimization Algorithm

2021 ◽  
Author(s):  
Sudheer Mangalampalli ◽  
Sangram Keshari Swain ◽  
Vamsi Krishna Mangalampalli
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Optimization Algorithm ◽  
Energy Efficient ◽  
Scheduling Algorithm ◽  
Whale Optimization Algorithm ◽  
Task Scheduling Algorithm ◽  
Whale Optimization

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.

scholarly journals An efficient Task Scheduling Algorithm using Modified Whale Optimization Algorithm in Cloud Computing

2019 ◽  
Vol 9 (2) ◽  
pp. 2533-2537
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Optimization Problems ◽  
Scheduling Algorithm ◽  
Optimization Techniques ◽  
Algorithm Optimization ◽  
On Demand ◽  
Task Scheduling Algorithm ◽  
Whale Optimization ◽  
User Tasks

Cloud computing brings computing resources such as software and hardware, it serve service to the users through a network. Major concept of cloud computing is to share the marvellous storage section. In cloud computing, the user jobs are prepared and executed with appropriate resources to successfully deliver the services. There are large amount of task allocation techniques that are used to accomplish task planning. In order to improve the task scheduling technique, so we proposed method of efficient task scheduling algorithm. Optimization techniques are solving NP-hard problems is very famous. In this proposed technique, user tasks are stored in the order of queue methods. The priority is designed and allocated suitable resources for the task. New tasks are investigated and kept in the on-demand priority of queue. The output of the on-demand queue is given to the MWOA. It has been proved that this algorithm is capable to eliminate optimization problems and outperform the current algorithms. The method is proposed to the required more number of iterations is reduced. The proposed algorithm is compared with various scheduling algorithms such as, genetic algorithm, ant colony, standard grey wolf optimization and particle swarm optimization. The outcomes of tests indicate the better efficiency of the MWOA in expressions of makespan and energy consumption.

Task scheduling on cloud computing based on sea lion optimization algorithm

2021 ◽  
Vol 17 (2) ◽  
pp. 99-116
Author(s):  
Raja Masadeh ◽  
Nesreen Alsharman ◽  
Ahmad Sharieh ◽  
Basel A. Mahafzah ◽  
Arafat Abdulrahman
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Whale Optimization Algorithm ◽  
Sea Lion ◽  
Hunting Behavior ◽  
Content Type ◽  
Combinatorial Optimization Problems ◽  
Whale Optimization

Purpose Sea Lion Optimization (SLnO) algorithm involves the ability of exploration and exploitation phases, and it is able to solve combinatorial optimization problems. For these reasons, it is considered a global optimizer. The scheduling operation is completed by imitating the hunting behavior of sea lions. Design/methodology/approach Cloud computing (CC) is a type of distributed computing, contributory in a massive number of available resources and demands, and its goal is sharing the resources as services over the internet. Because of the optimal using of these services is everlasting challenge, the issue of task scheduling in CC is significant. In this paper, a task scheduling technique for CC based on SLnO and multiple-objective model are proposed. It enables decreasing in overall completion time, cost and power consumption; and maximizes the resources utilization. The simulation results on the tested data illustrated that the SLnO scheduler performed better performance than other state-of-the-art schedulers in terms of makespan, cost, energy consumption, resources utilization and degree of imbalance. Findings The performance of the SLnO, Vocalization of Whale Optimization Algorithm (VWOA), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO) and Round Robin (RR) algorithms for 100, 200, 300, 400 and 500 independent cloud tasks on 8, 16 and 32 VMs was evaluated. The results show that SLnO algorithm has better performance than VWOA, WOA, GWO and RR in terms of makespan and imbalance degree. In addition, SLnO exhausts less power than VWOA, WOA, GWO and RR. More precisely, SLnO conserves 5.6, 21.96, 22.7 and 73.98% energy compared to VWOA, WOA, GWO and RR mechanisms, respectively. On the other hand, SLnO algorithm shows better performance than the VWOA and other algorithms. The SLnO algorithm's overall execution cost of scheduling the cloud tasks is minimized by 20.62, 39.9, 42.44 and 46.9% compared with VWOA, WOA, GWO and RR algorithms, respectively. Finally, the SLnO algorithm's average resource utilization is increased by 6, 10, 11.8 and 31.8% compared with those of VWOA, WOA, GWO and RR mechanisms, respectively. Originality/value To the best of the authors’ knowledge, this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere.

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