Task-Based System Load Balancing in Cloud Computing Using Particle Swarm Optimization

Ramezani, Fahimeh; Lu, Jie; Hussain, Farookh Khadeer

doi:10.1007/s10766-013-0275-4

Cited by 220 publications

(90 citation statements)

References 26 publications

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“…Second, an evolutionary mechanism is designed that changes the multiplex strategy of the initial solutions of different customers. The overall result is that an appropriate solution can always be found [16]. Moreover, in another study, the dynamic load balancing used in cloud computing was studied by using multi-criteria distributed analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Load Balancing in Cloud Complex Systems using Adaptive Fuzzy Neural Systems

Sadeghi¹

2018

ijacsa

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Abstract-Load balancing, reliability, and traffic are among the service-oriented issues in software engineering, and cloud computing is no exception to this rule and has put many challenges ahead of experts in this field. Considering the importance of the load balancing process in cloud computing, the purpose of this paper is to provide an appropriate solution for load balancing load in complex cloud systems using an adaptive fuzzy neural system. This system consists of four layers, and a particular operation is performed on each layer. The results of the experiments show that the system has better performance in the criteria mentioned above (balancing, traffic and reliability).

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Section: Literature Reviewmentioning

confidence: 99%

Load Balancing in Cloud Complex Systems using Adaptive Fuzzy Neural Systems

Sadeghi¹

2018

ijacsa

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“…Experiments show that ACO-VMM reduce the number of migrations and SLA violations as compare to traditional techniques. Ramezani et al [7] have developed a TBSLB-PSO method that improves utilization of resources. VMM is been proposed for reducing the downtime for overloaded virtual machines, but this techniques still consume time, cost and large amount of memory while migration.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid G-Aco Approach on Load Balancing in Cloud Computing

Kaur¹

2017

ijarcs

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Cloud computing permits facilitating of various administrations on various datacenters where assets are designated to clients on request. It utilizes the concept of virtualization for working online, on the grounds that without virtualization cloud computing is inefficient and not adaptable. Load balancing is a method that distribute the workload among different nodes in the given environment such that it no node in the system is overloaded .The different conventional load balancing techniques not performed well and they doesn't consider SLA quality of service parameters while choosing virtual machine for relocation. Many other issues are likewise associated with migration process like number of migration, utilization of resources, response time and memory. So there is have to grow new approach for load balancing in data centers using VM algorithms that beat the issues in customary methodologies and enhance their execution. So the hybrid approach utilizing different strategies like Ant Colony Optimization (ACO) and Genetic algorithm (GA) is proposed in this paper. This paper defeat the issue of stagnation in ACO-VMM system. The outcomes are mimicked in cloudsim environment.

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“…This algorithm optimizes the response time and execution time parameter. F.Ramezani and F.Khadeer hussain [7] proposed an algorithm which is based on particle swarm optimization technique. This algorithm reduces the execution time and transfer time parameter.…”

Section: Fig1 Metascheduler Architecturementioning

confidence: 99%

A New Scheduling Algorithm for Job Scheduler in Cloud Environment Considering the Priority and Cost as Constraint

Kumar¹

2017

IJARCSSE

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Abstract-Cloud computing provides the different types of services (availability, scalability, on demand service etc.) to the user on the basis of pay per use basis over the internet. Number of users and application are increasing day by day in cloud environment due to which assignment of task to best virtual machine among the available virtual machine become the difficult problem because it is not easy to predict the task execution time at run time. Therefore cloud task (job) scheduler should be dynamic in nature and distribute the all the task among all the virtual machine in such a manner that no virtual machine should be in under loaded or overloaded. We proposed and developed a task scheduling algorithm considering the priority of jobs and cost as a constraint. Proposed algorithm shows that IBA minimize the idle time of resources but does not provide the guarantee for handling job priority and cost of the resource. So there is a need of job scheduling algorithm that considers job priority and recourse cost. Keywords-virtual machine, task scheduling, cloud job scheduler, metascheduler I. INTRODUCTIONCloud computing becoming the emerging technology in the field of IT within last few years. It provides the scalability, flexibility and virtualization type of services over the internet to the user. There are three type of basic service provided by the cloud computing, software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS). Cloud user uses the services of cloud on the pay per use basis and cloud service provider try to provide the satisfactory level of services to the user. Elasticity property of cloud computing, user can expand and contract his service on demand basis. If done correctly, it is less expensive than running to a regular hardware. Cloud provides the service to store and retrieve the data easy and flexible way without worrying the hardware configuration needed. As the number of user is increasing day-by-day on cloud, all the user need services from cloud infrastructure. User send request to cloud for service, there is a possibility that some node of cloud computing can be overloaded or under loaded condition. To overcome this type of problem, many algorithms have been proposed by researcher, but all algorithms have their advantages and limitation. So load balancing has been become a challenging problem for cloud service provider.There are two types of scheduler exist in cloud computing: first one is local scheduler that works with host i.e assigned the job to the virtual machine and monitor the status of virtual machine after a time interval. Second one is called global scheduler or meta scheduler. User submits all through the web interface in cloud environment after that all the jobs are checked by gatekeeper that request are coming from legitimate user or not [9]. If request are coming from legitimate user then job are directed to meta scheduler otherwise request is rejected. Meta scheduler contains the information about all the virtual machine. Meta sched...

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Task-Based System Load Balancing in Cloud Computing Using Particle Swarm Optimization

Cited by 220 publications

References 26 publications

Load Balancing in Cloud Complex Systems using Adaptive Fuzzy Neural Systems

Load Balancing in Cloud Complex Systems using Adaptive Fuzzy Neural Systems

A Hybrid G-Aco Approach on Load Balancing in Cloud Computing

A New Scheduling Algorithm for Job Scheduler in Cloud Environment Considering the Priority and Cost as Constraint

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