Ranging and tuning based particle swarm optimization with bat algorithm for task scheduling in cloud computing

Valarmathi, R.; Sheela, T.

doi:10.1007/s10586-017-1534-8

Cited by 28 publications

(12 citation statements)

References 41 publications

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“…Owing to large solution space, scheduling is categorized as an NP-hard problem, and consequently, it needs time for finding an optimal answer. In general, the task scheduling problem could be regarded and modeled as a version of the Traveling Salesman Problem (TSP) [8,9] or Vehicle Routing Problem (VRP) [10,11]. In the TSP, a salesman must travel through all the cities and return to the city of origin, provided that it passes through the city once and travels the shortest distance [8].…”

Section: Introductionmentioning

confidence: 99%

“…In general, the task scheduling problem could be regarded and modeled as a version of the Traveling Salesman Problem (TSP) [8,9] or Vehicle Routing Problem (VRP) [10,11]. In the TSP, a salesman must travel through all the cities and return to the city of origin, provided that it passes through the city once and travels the shortest distance [8]. In the VRP, there is also a vehicle that must meet all customers or cities and meet their demand, provided that customer demand should not exceed the capacity of the vehicle and should also travel the shortest possible distance [10].…”

Section: Introductionmentioning

confidence: 99%

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Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

Pirozmand

Hosseinabadi

Farrokhzad

et al. 2021

Neural Comput & Applic

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The cloud computing systems are sorts of shared collateral structure which has been in demand from its inception. In these systems, clients are able to access existing services based on their needs and without knowing where the service is located and how it is delivered, and only pay for the service used. Like other systems, there are challenges in the cloud computing system. Because of a wide array of clients and the variety of services available in this system, it can be said that the issue of scheduling and, of course, energy consumption is essential challenge of this system. Therefore, it should be properly provided to users, which minimizes both the cost of the provider and consumer and the energy consumption, and this requires the use of an optimal scheduling algorithm. In this paper, we present a two-step hybrid method for scheduling tasks aware of energy and time called Genetic Algorithm and Energy-Conscious Scheduling Heuristic based on the Genetic Algorithm. The first step involves prioritizing tasks, and the second step consists of assigning tasks to the processor. We prioritized tasks and generated primary chromosomes, and used the Energy-Conscious Scheduling Heuristic model, which is an energy-conscious model, to assign tasks to the processor. As the simulation results show, these results demonstrate that the proposed algorithm has been able to outperform other methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

Pirozmand

Hosseinabadi

Farrokhzad

et al. 2021

Neural Comput & Applic

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“…Valarmathi et al [8] combined the cuckoo algorithm (CS) with the particle swarm optimization algorithm (PSO), resulting in a new intelligent optimization algorithm (CPSO). The combination of the two algorithms improved the overall population convergence rate and made the algorithm easy to implement.…”

Section: Related Workmentioning

confidence: 99%

“…The completion time (CT) is an important factor affecting the cloud computing's quality of service. The CT is equal to the sum of the task execution time (ET) and the task transfer time (TT), as shown in equation ( 7), while the task execution time is shown in equation (8).…”

Section: ) Task Completion Timementioning

confidence: 99%

Collaborative Optimization Scheduling of Cloud Service Resources Based on Improved Genetic Algorithm

Wang

2020

IEEE Access

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With the wide application of cloud computing technology, the services provided by cloud systems have become increasingly diverse, thus these systems are required to solve tasks of high variety and complexity, with a tremendously extensive amount of task data involved. That is why reasonable scheduling system resources are particularly important in cloud computing research. In this paper, a cloud computing system needs to take into account a wider range of cloud service resource types and collaborative optimization scheduling issues in order to solve the tasks at hand. Firstly, a new adaptive genetic algorithm (NAGA) was proposed. By improving the crossover mutation genetic operator, the algorithm was able to save excellent individuals as much as possible, enhance the algorithm's optimization ability, and greatly reduce the probability of the algorithm falling into the local optimal solution. Secondly, focusing on the main factors affecting service quality, such as task completion time, system load, and network bandwidth, an upgraded fitness operator method for the cloud resource collaborative optimization scheduling problem is set forth. Finally, an algorithm of cloud service resources based on an improved genetic algorithm (OSIG) is proposed. Experiments on the CloudSim cloud computing simulation platform demonstrate that the OSIG algorithm proposed in this paper can effectively optimize the resource scheduling strategy, shorten the task completion time, facilitate the system load balancing, and boost the system's service quality. The theoretical analysis was consistent with the experimental results.

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“…A study is also conducted in (18) for improving the task scheduling for that an enhanced PSO algorithm was proposed. In the existing PSO algorithm the problem of inertia weight assignment was solved by a tuning function based PSO (RTPSO).…”

Section: Literature Reviewmentioning

confidence: 99%

A QoS Aware FTOPSIS-WOA based task scheduling algorithm with load balancing technique for the cloud computing environment

Samriya¹,

Kumar²

2020

IJST

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Objectives: To perform task scheduling with minimising the makespan through implementing an effective load balancing approach. Methods: In this study, the Fuzzy Topsis algorithm (FTPOSIS) is used for the task scheduling and the makespan is minimised with the effective load balancing by modelling the whale optimization algorithm (WOA). Findings: This proposed model controls the admittance of the requests by achieving target QoS in terms of response time. Hence, the admittance is controlled so that the requests which are accepted do not face a delay greater than the time limit stated in the SLA. Using CloudSim tool the simulation is done and the results are exhibited. The effectiveness of the intended algorithm is compared with the existing methods. Novelty: The novelty of this study includes increasing the throughput of the cloud system by reducing the makespan of the cloud scheduling process. Reducing SLA violations and improving the QoS can efficiently give assurance to reduce the delay of transmission, packet loss rate of data. Attaining a balance between constrained resources and QoS.

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Ranging and tuning based particle swarm optimization with bat algorithm for task scheduling in cloud computing

Cited by 28 publications

References 41 publications

Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

Collaborative Optimization Scheduling of Cloud Service Resources Based on Improved Genetic Algorithm

A QoS Aware FTOPSIS-WOA based task scheduling algorithm with load balancing technique for the cloud computing environment

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