A Survey on QoS Requirements Based on Particle Swarm Optimization Scheduling Techniques for Workflow Scheduling in Cloud Computing

Farid, Mazen; Latip, Rohaya; Hussin, Masnida; Hamid, Nor Asilah Wati Abdul

doi:10.3390/sym12040551

Cited by 38 publications

(32 citation statements)

References 70 publications

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“…The services of cloud computing [28] that are provided to remote users who have different requirements have been described; in addition, they present a scheduling approach that showed which workloads consist of workflows or queries. On the other hand, several studies have focused on particle swarm workflows scheduling and optimization [29,30]. Different parallel job scheduling strategies have been proposed in [31,32] to optimize the mapping between jobs and resources.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Job Scheduling Strategy Using Jobs Characteristics in Cloud Computing

et al. 2020

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A crucial performance concern in distributed decentralized environments, like clouds, is how to guarantee that jobs complete their execution within the estimated completion times using the available resources’ bandwidth fairly and efficiently while considering the resource performance variations. Formerly, several models including reservation, migration, and replication heuristics have been implemented to solve this concern under a variety of scheduling techniques; however, they have some undetermined obstacles. This paper proposes a dynamic job scheduling model (DTSCA) that uses job characteristics to map them to resources with minimum execution time taking into account utilizing the available resources bandwidth fairly to satisfy the cloud users quality of service (QoS) requirements and utilize the providers’ resources efficiently. The scheduling algorithm makes use of job characteristics (length, expected execution time, expected bandwidth) with regards to available symmetrical and non-symmetrical resources characteristics (CPU, memory, and available bandwidth). This scheduling strategy is based on generating an expectation value for each job that is proportional to how these job’s characteristics are related to all other jobs in total. That should make their virtual machine choice closer to their expectation, thus fairer. It also builds a feedback method which deals with reallocation of failed jobs that do not meet the mapping criteria.

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Section: Related Workmentioning

confidence: 99%

Dynamic Job Scheduling Strategy Using Jobs Characteristics in Cloud Computing

et al. 2020

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“…The results of the proposed work are compared with some of the effective task scheduling algorithms. Surveys of Particle Swarm Optimization based scheduling algorithms are discussed in (19) . The aim is to assist the users to decide the suitable QoS parameters and mapping the resources for task in cloud environment.…”

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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“…Cloud computing uses virtualization technology based on user's needs to provide resources, which reduces energy consumption. One of the most popular ways to reduce energy consumption is to use metaheuristic techniques [3,4]. is technique could attract attention due to its simplicity of implementation.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we have dealt with the issue of virtualized reserve controlling of cloud computing records middles and, in particular, the load balancing among the servers, and we have done this by considering and implementing particle swarm optimization algorithms and genetic procedures [3,18]. e sequel displays that the combination of these two can be effective in reducing the cost of resource allocation and load balancing.…”

Section: Introductionmentioning

confidence: 99%

LBPSGORA: Create Load Balancing with Particle Swarm Genetic Optimization Algorithm to Improve Resource Allocation and Energy Consumption in Clouds Networks

Mirmohseni

Javadpour

Tang

2021

Mathematical Problems in Engineering

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Due to the purpose of this study that reducing power consumption in the cloud network is based on load balancing, the fitness function measures the load balance between cloud network and servers (the hosts). This technique is appropriate for handling the resource optimization challenges, due to the ability to convert the load balancing problem into an optimization problem (reducing imbalance cost). In this research, combining the results of the particle swarm genetic optimization (PSGO) algorithm and using a combination of advantages of these two algorithms lead to the improvement of the results and introducing a suitable solution for load balancing operation, because in the proposed approach (LBPSGORA), instead of randomly assigning the initial population in the genetic algorithm, the best result is procured by putting the initial population. The LBPSGORA method is compared with PSO, GA, and hybrid GA-PSO. The execution cost, load balancing, and makespan have been evaluated and our method has performed better than similar methods.

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A Survey on QoS Requirements Based on Particle Swarm Optimization Scheduling Techniques for Workflow Scheduling in Cloud Computing

Cited by 38 publications

References 70 publications

Dynamic Job Scheduling Strategy Using Jobs Characteristics in Cloud Computing

Dynamic Job Scheduling Strategy Using Jobs Characteristics in Cloud Computing

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

LBPSGORA: Create Load Balancing with Particle Swarm Genetic Optimization Algorithm to Improve Resource Allocation and Energy Consumption in Clouds Networks

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