An Evolutionary Study of Multi-Objective Workflow Scheduling in Cloud Computing

Shukla, Shashank; Gupta, Anil Kumar; Saxena, Sandeep; Upadhyay, S. K.

doi:10.5120/ijca2016908109

Cited by 9 publications

(6 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is a brand-new application mode produced by the workflow management system in the cloud computing environment, which is referred to as the cloud workflow for short. The cloud workflow management system uniformly manages the computation or storage of resources and realizes the orderly and efficient execution of cloud workflow task scheduling [27], thereby realizing the automation of business processes.…”

Section: Workflow Schedulingmentioning

confidence: 99%

Workflow Scheduling Based on Mobile Cloud Computing Machine Learning

Gong

2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

In recent years, cloud workflow task scheduling has always been an important research topic in the business world. Cloud workflow task scheduling means that the workflow tasks submitted by users are allocated to appropriate computing resources for execution, and the corresponding fees are paid in real time according to the usage of resources. For most ordinary users, they are mainly concerned with the two service quality indicators of workflow task completion time and execution cost. Therefore, how cloud service providers design a scheduling algorithm to optimize task completion time and cost is a very important issue. This paper proposes research on workflow scheduling based on mobile cloud computing machine learning, and this paper conducts research by using literature research methods, experimental analysis methods, and other methods. This article has deeply studied mobile cloud computing, machine learning, task scheduling, and other related theories, and a workflow task scheduling system model was established based on mobile cloud computing machine learning from different algorithms used in processing task completion time, task service costs, task scheduling, and resource usage The situation and the influence of different tasks on the experimental results are analyzed in many aspects. The algorithm in this paper speeds up the scheduling time by about 7% under a different number of tasks and reduces the scheduling cost by about 2% compared with other algorithms. The algorithm in this paper has been obviously optimized in time scheduling and task scheduling.

show abstract

Section: Workflow Schedulingmentioning

confidence: 99%

Workflow Scheduling Based on Mobile Cloud Computing Machine Learning

Gong

2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…If the resources in cloud environment are properly scheduled, it reduced the response time, total cost of cloud resources, energy consumption and enhance the performance of overall cloud environment. Proposed work comparative review of multi-objective workflow scheduling algorithms [6].…”

Section: Related Workmentioning

confidence: 99%

Modified Flower Pollination Based Task Scheduling in Cloud Environment using Virtual Machine Migration

Khurana¹,

Singh²

2019

IJITEE

View full text Add to dashboard Cite

Cloud computing allows users to use resources pay per use model by the help of internet. Users are able to do computation dynamically from different location by using internet resources. The major challenging task in cloud computing is efficient selection of resources for the tasks submitted by users. A number of heuristics and meta-heuristics algorithms are designed by different researchers. The most critical phase is the selection of appropriate resource and its management. The selection of resource include to identify list of authenticated available resources in the cloud for job submission and to choose the best resource. The best resource selection is done by the analysis of several factors like expected time to execute a task by user, access restriction to resources, and expected cost to use resources. In this paper, cloud architecture for resource selection is proposed which combines these factors and make the effective resource selection. In this paper a modified flower pollination algorithm is proposed to migrate the task on efficient virtual machine. The selection of the efficient virtual machine is calculated by the fitness function. By calculating the fitness function, the modified FPA algorithm is used to take the decision regarding VM migration is required to improve the resource efficiency or not. In this paper Virtual machine mapper maps the task as per knowledge base i.e. past history of the virtual machine, task type whether computational or communicational based. The results are compared with the existing meta-heuristic algorithms.

show abstract

“…The main limitation of this work is the time limit. Shukla et al have introduced multi‐objective workflow scheduling in cloud computing, which is mainly focused on providing a comparative review of multi‐objective workflow scheduling algorithms.…”

Section: Literature Surveymentioning

confidence: 99%

FFBAT: A security and cost‐aware workflow scheduling approach combining firefly and bat algorithms

Arunarani

Manjula

Sugumaran

2017

Concurrency and Computation

View full text Add to dashboard Cite

Summary Cloud computing is distributed computing on a large scale driven by practical and effective operations, in which a pay‐per‐use framework provides dynamic scaling in response to the needs of workflow applications. Many existing cloud computing environments do not effectively employ security measures to counter security threats in task scheduling. To improve the scheduling system, we include security service to the scheduling process. However, adding security services to applications inevitably causes overhead in terms of computation time. The tradeoff between achieving high computing performance and providing the desired level of security protection imposes a big challenge for task scheduling. To solve this problem, we propose a security and cost aware scheduling algorithm for heterogeneous tasks in scientific workflow executed in a cloud. Our proposed algorithm is based on the hybrid optimization approach, which combines Firefly and Bat algorithms. The coding strategy is to minimize the total execution cost while meeting the deadline and risk rate constraints. The proposed system uses a multi‐objective function, and the results indicate that our algorithm always outperforms the traditional algorithms.

show abstract

An Evolutionary Study of Multi-Objective Workflow Scheduling in Cloud Computing

Cited by 9 publications

References 13 publications

Workflow Scheduling Based on Mobile Cloud Computing Machine Learning

Workflow Scheduling Based on Mobile Cloud Computing Machine Learning

Modified Flower Pollination Based Task Scheduling in Cloud Environment using Virtual Machine Migration

FFBAT: A security and cost‐aware workflow scheduling approach combining firefly and bat algorithms

Contact Info

Product

Resources

About