Hybrid swarm optimization algorithm based on task scheduling in a cloud environment

Summary Cloud computing is the current computing standard, which provides information technology (IT) services over the Internet on demand. In the cloud environment, a task is mapped with an available resource to attain a good result. Task scheduling is the technique that is used to allocate tasks o...

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Bibliographic Details
Published in:International journal of communication systems 2021-09, Vol.34 (13), p.n/a
Main Authors: Eldesokey, Heba M., Abd El‐atty, Saied M., El‐Shafai, Walid, Amoon, Mohammed, Abd El‐Samie, Fathi E.
Format: Article
Language:English
Subjects:
Algorithms
Cloud computing
Computing costs
Efficiency
Evolutionary algorithms
Genetic algorithms
Information technology
makespan
Multilayers
Optimization
Particle swarm optimization
regression
Resource utilization
salp swarm optimization
Schedules
Task scheduling
Time lag
Traffic delay
Virtual environments
Workload
Workloads
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Summary:Summary Cloud computing is the current computing standard, which provides information technology (IT) services over the Internet on demand. In the cloud environment, a task is mapped with an available resource to attain a good result. Task scheduling is the technique that is used to allocate tasks on virtual machines (VMs) of a server based on its capacity of workload. Tasks are scheduled to the server in such a way to minimize traffic and time delay. Particle swarm optimization (PSO) is the best existing algorithm used to schedule a task to an existing resource on the environment of the cloud. By PSO, the task is scheduled for an existing resource to reduce computational cost. In this paper, a hybrid swarm optimization (HSO) algorithm, which is the combination of PSO and salp swarm optimization (SSO), is proposed to resolve task scheduling issues in the cloud environment. The main goal of HSO is to schedule the task to the available resource in such a way to reduce the execution time and computation cost. Multilayer logistic regression (MLR) is an approach used to detect the overloaded VMs, so that a task can be scheduled to a VM according to its capacity of workload. The proposed HSO algorithm with MLR is simulated on the cloudsim toolkit, and the results reveal the efficiency of the proposed algorithm in terms of cost, execution time, and makespan. Compared to the existing algorithms such as the genetic algorithms (GAs), the improved efficiency evolution (IDEA), and the PSO, the proposed algorithm reveals superiority in terms of efficiency, resource utilization, and speed. In this paper, a hybrid swarm optimization (HSO) algorithm, which is a combination of PSO and salp swarm optimization (SSO), is proposed to resolve task scheduling issues in the cloud environment. The main goal of HSO is to schedule the task to the available resource in such a way to reduce the execution time and computation cost. Multilayer logistic regression (MLR) is an approach that is used to detect the overloaded virtual machines, so that a task can be scheduled to a VM according to its capacity of workload. The proposed HSO algorithm with the MLR is simulated in the cloudsim toolkit, and the results display the efficiency of the proposed algorithm in terms of cost, execution time, and makespan. Compared to the existing algorithms such as the genetic algorithms (GAs), the improved efficiency evolution (IDEA), and the PSO, the proposed algorithm reveals superiority in terms of ef
ISSN:1074-5351
1099-1131
DOI:10.1002/dac.4694