An efficient approach to the map-reduce framework and genetic algorithm based whale optimization algorithm for task scheduling in cloud computing environment

Sanaj, M.S.; Prathap, P. M. Joe

doi:10.1016/j.matpr.2020.09.064

Cited by 40 publications

(26 citation statements)

References 19 publications

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“…The task scheduling problem in cloud computing is known as an NP-hard problem because of the large space of solutions. Therefore, we need a long time to discover an optimal solution [22]. It is possible to reach a near-optimal solution in a short time for such problems by using metaheuristic strategies [23].…”

Section: Meta-heuristic Algorithmmentioning

confidence: 99%

An Efficient Task Scheduling Based on Seagull Optimization Algorithm for Heterogeneous Cloud Computing Platforms

2022

IJE

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Cloud computing provides computing resources like software and hardware as a service by the network for several users. Task scheduling is one of the main problems to attain cost-effective execution. The main purpose of task scheduling is to allocate tasks to resources so that it can optimize one or more criteria. Since the problem of task scheduling is one of the Nondeterministic Polynomial-time (NP)-hard problems, meta-heuristic algorithms have been widely employed for solving task scheduling problems. One of the new bio-inspired meta-algorithms is Seagull Optimization Algorithm (SOA). In this paper, we proposed an energy-aware and cost-efficient SOA-based T ask Scheduling (SOAT S) algorithm. The aims of proposed algorithm to make a trade-off between five objectives (i.e., energy consumption, makespan, cost, waiting time, and load balancing) using a fewer number of iterations. The experiment results by comparing with several meta-heuristic algorithms (i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Whale Optimization Algorithm (WOA)) prove that the proposed technique performs better in solving task scheduling problem. Moreover, we compared the proposed algorithm with well-known scheduling methods: Cost -based Job Scheduling (CJS), Moth Search Algorithm based Differential Evolution (MSDE), and Fuzzy -GA (FUGE). In the heavily loaded environment, the SOAT S algorithm improved energy consumption and cost saving by 10 and 25%, respectively.

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Section: Meta-heuristic Algorithmmentioning

confidence: 99%

An Efficient Task Scheduling Based on Seagull Optimization Algorithm for Heterogeneous Cloud Computing Platforms

2022

IJE

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“…Ding et al [ 32 ] proposed a two-stage energy-saving cloud computing method based on Q-learning to reduce task response time and improve resource utilization. In [ 33 ], Sanaj and Joe Prathap proposed a MAP reduction framework and GA-WOA algorithm to schedule tasks in the cloud, and obtained a more efficient task scheduler. In [ 34 ], Dhinesh Babu and Venkata Krishna proposed a heuristic algorithm based on bee behavior to maximize throughput and achieve load balancing of virtual machines.…”

Section: Related Workmentioning

confidence: 99%

GCWOAS2: Multiobjective Task Scheduling Strategy Based on Gaussian Cloud‐Whale Optimization in Cloud Computing

Sun

et al. 2021

Computational Intelligence and Neuroscience

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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.

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“…Experiments show that this algorithm can help improve the efficiency of cloud computing scheduling. Literature [14] proposed a cloud computing task scheduling algorithm based on a mixture of ACO and WOA. Simulation experiments show that this algorithm is indeed better than ACO and WOA in cloud computing scheduling.…”

Section: Related Workmentioning

confidence: 99%

Cloud Computing Task Scheduling Model Based on Improved Whale Optimization Algorithm

Jia

Shi

2021

Wireless Communications and Mobile Computing

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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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An efficient approach to the map-reduce framework and genetic algorithm based whale optimization algorithm for task scheduling in cloud computing environment

Cited by 40 publications

References 19 publications

An Efficient Task Scheduling Based on Seagull Optimization Algorithm for Heterogeneous Cloud Computing Platforms

An Efficient Task Scheduling Based on Seagull Optimization Algorithm for Heterogeneous Cloud Computing Platforms

GCWOAS2: Multiobjective Task Scheduling Strategy Based on Gaussian Cloud‐Whale Optimization in Cloud Computing

Cloud Computing Task Scheduling Model Based on Improved Whale Optimization Algorithm

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