Workflow Scheduling Using Hybrid GA‐PSO Algorithm in Cloud Computing

Manasrah, Ahmad M.; Ali, Hanan Ba

doi:10.1155/2018/1934784

Cited by 180 publications

(117 citation statements)

References 39 publications

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“…The proposed algorithm is simulated by using synthetic workflows and it is compared against BHEFT and BTGA algorithms and the extended BPSO outperforms over the existing algorithms. Manasrah et.al [15], developed a new workflow scheduling algorithm i.e, GA-PSO to efficiently allocate the tasks to the resources. The main aim of this algorithm is to minimize the makespan and the execution cost and to fairly distribute the load among the resources.…”

Section: Ms Sudheer Drm Vamsi Krishna mentioning

confidence: 99%

Dynamic PSO for Task Scheduling Optimization in Cloud Computing

2019

IJRTE

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Task scheduling is still a challenge in cloud computing as no existing scheduling algorithms are not effectively provisioning and scheduling the resources in the cloud. Existing authors considered only metrics like makespan, execution time and turnaround time etc. and the previous authors concentrated only to optimize the above mentioned metrics. But no existing authors were considered about the effective provisioning of the resources in the cloud i.e, compute, storage and network capacities and still many resources in the cloud were underutilized. In this paper, we want to propose an algorithm which can effectively utilize the resources in the cloud by extending Particle Swarm Optimization by addressing the metrics Bandwidth utilization and Memory utilization particularly. We have simulated this algorithm by using cloudsim and compared the modified Dynamic PSO with the PSO algorithm and it outperforms in terms of Bandwidth and Memory utilization and the makespan is also optimized.

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Section: Ms Sudheer Drm Vamsi Krishna mentioning

confidence: 99%

Dynamic PSO for Task Scheduling Optimization in Cloud Computing

2019

IJRTE

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“…Ahmad M. Manasrah and Hanan Ba Ali proposed a Hybrid GA-PSO [9] algorithm for task allocation to the virtual machines efficiently. The experimental result shows that the proposed algorithm outperforms the existing Genetic Algorithm and PSO algorithm.…”

Section: Related Workmentioning

confidence: 99%

Design of Energy Aware Scheduling Algorithm for Executing Scientific Workflows in Cloud

Balamurugan¹,

Saraswathi²

2019

IJEAT

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The usage of cloud computing and its resources for the execution of scientific workflow is a rapidly increasing demand. The Scientific applications are generally large in scale; even a single scientific workflow includes more number of complex tasks. Execution of these tasks can be made successful only by deploying it in the cloud virtual machines, because only cloud environment can only provide very large number of computing assets. In cloud, every processing resource is given as Virtual Machine. Any scientific workflow deployed in the cloud needs large number of virtual machines so; huge amount of computational energy is spent by the virtual machines to execute multifaceted scientific workflows. Hence there arises the need to utilize the cloud resources in an energy efficient way. Also, if the virtual machines are planned to schedule in an energy efficient manner there is an increase of makepsan of the workflow which is going to be an important parameter for completing the workflow within the deadline. So, the need for executing scientific workflows in energy efficient way with reduced makespan becomes a major issue among the researchers. It also becomes very challenging task to executing a scientific workflow in within the given deadline of a task in the given workflow. To address these issues, a new Energy Aware workflow scheduling algorithm is proposed and designed with improved makespan for the execution of different scientific applications in cloud environment.

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“…A novel scheduling algorithm was designed in [15] for cloud computing using the driver of the dynamic essential path (DDEP) by considering actual computation cost and communication cost of task node in the scheduling process. A Hybrid GA-PSO algorithm was intended in [16] to decrease the makespan and the cost and balance the load of the dependent tasks over the heterogonous resources in the cloud.…”

Section: Related Workmentioning

confidence: 99%

Gene Optimized Deep Neural Round Robin Workflow Scheduling in Cloud

Shanmugasundaram¹,

Kumar²,

Kittur³

2019

IJACSA

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Workflow scheduling is a key problem to be solved in the cloud to increases the quality of services. Few research works have been designed for performing workflow scheduling using different techniques. But, scheduling performance of existing techniques was not effective when considering a larger number of user tasks. Besides, the makespan of workflow scheduling was higher. In order to solve such limitations, Gene Optimized Deep Neural Round Robin Scheduling (GODNRRS) Technique is proposed. The designed GODNRRS Technique contains three layers namely input, hidden and output layer to efficiently perform workflow scheduling in the cloud. The GODNRRS Technique initially gets the number of user tasks as input in the input layer and forwards it to the hidden layer. After taking input, GODNRRS Technique initializes gene population with the assist of virtual machines in Amazon cloud server at the first hidden layer. Next, GODNRRS Technique determines fitness function for each virtual machine using their energy, memory, CPU time, bandwidth capacity at the second hidden layer. Afterward, GODNRRS Technique defines a weight for each virtual machine at the third hidden layer depends on their fitness function estimation. Consequently, GODNRRS Technique distributes the user tasks to optimal virtual machines according to their weight value at the fourth hidden layer in a cyclic manner. At last, the output layer renders the scheduled tasks result. Thus, GODNRRS Technique handles workflows in the cloud with improved scheduling efficiency and lower energy and makespan. The GODNRRS Technique conduct the experimental evaluation using metrics such as scheduling efficiency, makespan, and energy consumption with respect to a different number of user tasks from LIGO , Montage and cybershake real-time applications. The experimental result show that the GODNRRS Technique is able to increases the efficiency and also reduces the makespan of workflows scheduling in the cloud as compared to state-of-the-art works. 1000 88 89 92 99 85 89 90 95 87 90 91 98

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Workflow Scheduling Using Hybrid GA‐PSO Algorithm in Cloud Computing

Cited by 180 publications

References 39 publications

Dynamic PSO for Task Scheduling Optimization in Cloud Computing

Dynamic PSO for Task Scheduling Optimization in Cloud Computing

Design of Energy Aware Scheduling Algorithm for Executing Scientific Workflows in Cloud

Gene Optimized Deep Neural Round Robin Workflow Scheduling in Cloud

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