Binary PSOGSA for Load Balancing Task Scheduling in Cloud Environment

Alnusairi, Thanaa S.; Shahin, Ashraf A.; Daadaa, Yassine

doi:10.14569/ijacsa.2018.090535

Cited by 9 publications

(7 citation statements)

References 25 publications

(43 reference statements)

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“…Load balance is a very important task in scheduling tasks that directly affect cloud computing resources [27]. So this article is a nature-inspired method called Binary Load Balancing -Hybrid Particle Swarm Optimization and Gravitational Search Provides Algorithm (Bin-LB-PSOGSA).…”

Section: Related Workmentioning

confidence: 99%

Improving the Load Balancing and Dynamic Placement of Virtual Machines in Cloud Computing using Particle Swarm Optimization Algorithm

Yousefipour

Rahmani

Jahanshahi

2021

IJE

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Nowadays, maximizing profits, decreasing operating cost and scheduling tasks are the most important issues of cloud computing with its growing usage. In this regard, one of the challenges in cloud computing is to provide an efficient method to deploy virtual machines on physical machines with the aim of optimizing energy consumption, fair load distribution and task scheduling. The purpose of present study is to provide a method for improving task scheduling through an improved particle swarm optimization algorithm. In the proposed method of present study, selection of a proper objective function has led to balanced workload of virtual machines, decreased time of all tasks as well as maximum utilization of all resources and increased productivity in addition to dynamic placement of virtual machine on physical machine. The results of simulation showed that the proposed method has provided an optimized solution for scheduling tasks, equal allocation of tasks in virtual machines and placement on the appropriate physical machine and less time with an improvement of 0.02 has been spent on the process of outsourcing virtual machines.

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Section: Related Workmentioning

confidence: 99%

Improving the Load Balancing and Dynamic Placement of Virtual Machines in Cloud Computing using Particle Swarm Optimization Algorithm

Yousefipour

Rahmani

Jahanshahi

2021

IJE

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“…The experimental results summarized that the proposed ICSA algorithm reduced the makespan, waiting time, response time, and flow time as compared to FCFS and PSO methods. A Binary PSOGSA method was developed for the load balancing and task scheduling in the cloud in paper [11]. It is a bio-inspired load balancing algorithm used to manage the virtual machines for the load balancing issue.…”

Section: Related Workmentioning

confidence: 99%

“…For example, Ant Colony Optimization, Genetic Algorithm, Particle Swarm Optimization, Gravitation Search Algorithm, etc. [11]. In this research, various algorithms have been utilized and these are described as:…”

Section: Introductionmentioning

confidence: 99%

Independent Task Scheduling in Cloud Computing using Meta-Heuristic HC-CSO Algorithm

Bhagwan¹,

Kumar²

2021

IJACSA

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Cloud computing is a vital paradigm of emerging technologies. It provides hardware, software, and development platforms to end-users as per their demand. Task scheduling is an exciting job in the cloud computing environment. Tasks can be divided into two categories dependent and independent. Independent tasks are not connected to any type of parent-child concept. Various meta-heuristic algorithms have come into force to schedule the independent tasks. In this, paper a hybrid HC-CSO algorithm has been simulated using independent tasks. This hybrid algorithm has been designed by using the HEFT algorithm, Self-Motivated Inertia Weight factor, and standard Cat Swarm Optimization algorithm. The Crow Search algorithm has been applied to overcome the problem of premature convergence and to avoid the H-CSO algorithm getting stuck in the local fragment. The simulation was carried out using 500-1300 random lengths independent tasks and it was found that the H-CSO algorithm has beaten PSO, ACO, and CSO algorithms whereas the hybrid algorithm HC-CSO is working fine despite Cat Swarm Optimization, Particle Swarm Optimization, and H-CSO algorithm in the name of processing cost and makespan. For all scenarios, the HC-CSO algorithm is found overall 4.15% and 7.18% efficient than the H-CSO and standard CSO respectively in comparison to the makespan and in case of computation cost minimization, 9.60% and 14.59% than the H-CSO and the CSO, respectively.

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“…Weihua H et al proposed a load balancing implementation strategy based on fuzzy clustering of weight preference [7]. Alnusairi T S et al designed an optimization algorithm based on mixed particle swarm optimization, but this algorithm ignored the special case that the workload was dependent [8]. Puthal D et al proposed a new load balancing method from the perspective of improving the security of edge data centers, but the real-time network needs to be improved [9].…”

Section: Related Workmentioning

confidence: 99%

Edge cloud task scheduling model based on layered excellent gene replication strategy

Zhu

Zhao

Lin

et al. 2021

J. Phys.: Conf. Ser.

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With the development of the Internet of Things and 5G. Edge cloud technology has gradually become a research hotspot. When facing the massive and concurrent tasks of terminal users, reasonable resource scheduling strategy is a key technology. Because edge cloud needs to respond quickly to real-time tasks and ensure the stability of nodes at the same time, the optimal task scheduling strategy needs to be selected to meet the low latency requirements of edge users. In view of the above problems in resource allocation of edge cloud, this paper established a layered excellent gene replication strategy (HEGPSO model), in which the optimal replicator is added, and an evolutionary particle swarm optimization algorithm is proposed. In each iteration, the population is divided into three layers based on individual fitness. After that, the optimal replication factor is added to each layer of individuals to enhance the global search ability of the algorithm and ensure the good convergence of the algorithm. Finally, a balanced resource allocation model is established. Experiments show that the HEGPSO model proposed in this paper has high fitness and fast convergence speed, and is suitable for large-scale task access scenarios.

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Binary PSOGSA for Load Balancing Task Scheduling in Cloud Environment

Cited by 9 publications

References 25 publications

Improving the Load Balancing and Dynamic Placement of Virtual Machines in Cloud Computing using Particle Swarm Optimization Algorithm

Improving the Load Balancing and Dynamic Placement of Virtual Machines in Cloud Computing using Particle Swarm Optimization Algorithm

Independent Task Scheduling in Cloud Computing using Meta-Heuristic HC-CSO Algorithm

Edge cloud task scheduling model based on layered excellent gene replication strategy

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