A GSA based hybrid algorithm for bi-objective workflow scheduling in cloud computing

Choudhary, Anubhav; Gupta, Indrajeet; Singh, Vishakha; Jana, Prasanta K.

doi:10.1016/j.future.2018.01.005

Cited by 146 publications

(52 citation statements)

References 19 publications

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“…The Scheduling is an NP-complete problem, by looking at a lot of problems such as keeping cost and energy down and reducing waiting time, therefore, many research methods have been proposed by many researchers. Despite the abundance of these studies on this hot topic, but the door is still open behind the researchers because most they reached are not perfect and not close to the optimal solution [7]. Below are some recent and important studies that will be used by researchers when researching in this field.…”

Section: Figure 1 Cloud Computing Challenges From 2018mentioning

confidence: 99%

A cloud computing scheduling and its evolutionary approaches

Abdalkafor

Jihad

Allawi³

2021

IJEECS

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<span>Despite the increasing use of cloud computing technology because it offers unique features to serve its customers perfectly, exploiting the full potential is very difficult due to the many problems and challenges. Therefore, scheduling resources are one of these challenges. Researchers are still finding it difficult to determine which of the scheduling algorithms are appropriate and effective and that helps increases the performance of the system to accomplish these tasks. This paper provides a broad and detailed examination of resource scheduling algorithms in the environment of a cloud computing environment and highlights the advantages and disadvantages of some algorithms to help researchers in selecting the best algorithms to schedule a particular workload to get a satisfy a quality of service, guarantee good utilization of the cloud resources also minimizing the make-span.</span>

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Section: Figure 1 Cloud Computing Challenges From 2018mentioning

confidence: 99%

A cloud computing scheduling and its evolutionary approaches

Abdalkafor

Jihad

Allawi³

2021

IJEECS

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“…Execution cost and performance variation were considered in this algorithm. Choudhary et al [18] developed an efficient hybrid heuristic of HEFT and GSA (HGSA) for workflow scheduling to optimize makespan as well as cost. Besides, Zhang et al [19] proposed a bi-objective genetic algorithm (BOGA) to pursue low energy consumption and high system reliability for workflow scheduling in heteregenous computing system.…”

Section: A Workflow Scheduling With Static Resource Provisioningmentioning

confidence: 99%

Minimizing Monetary Costs for Deadline Constrained Workflows in Cloud Environments

Han

Chen

et al. 2020

IEEE Access

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As one of the latest market-oriented resource provisioning paradigms, cloud computing has been widely adopted by a growing number of consumers due to its powerful computing ability and storage ability. Although cloud computing can achieve effective cost reduction and convenience enhancement in the development of large-scale applications, it results in a complex cost optimization problem for data-dependent tasks represented by a workflow. All tasks in a workflow should be scheduled according to a proper strategy such that the cost is minimized and the precedence constraints and timing requirements are satisfied. In this paper, we study the cost optimization problem of deadline constrained workflows on cloud computing, and propose two list scheduling algorithms named Look-back Workflow Scheduling (LBWS) and Structure Aware Workflow Scheduling (SAWS) to solve the problem. LBWS distributes the deadline over the workflow as sub-deadlines to tasks in different levels, and schedules the tasks according to their priorities to the resources which meet their sub-deadlines and the best time-cost trade off requirements. Compared with LBWS, SAWS considers tasks allocated to the same level at a time and provisions resources with minimum cost to these tasks. Experiments on scientific workflow applications with different data and computational characteristics are conducted to show that, the proposed approaches can achieve better performance in terms of success rate and monetary cost. INDEX TERMS Cloud computing, workflow scheduling, deadline constraint, cost minimization.

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“…It should be able to adapt soft constraint and hard constraint. 23,24 As well as exact facts of arrival rates and service can also improve the e±ciency of the algorithm. 25 Metahuristic algorithms take more time and do not always guarantee of optimal solution.…”

Section: Literature Surveymentioning

confidence: 99%

Adaptive Workflow Scheduling Using Evolutionary Approach in Cloud Computing

Jaybhaye

Attar

2020

Vietnam J. Comp. Sci.

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Cloud services are used to achieve diverse computing needs such as cost, security, scalability, and availability. Acceleration evolution in the distributed and cloud domains is common for large and dynamic workflows deployment. Resources and task mapping depend on the user’s objectives such as reduction in cost or execution completion within the stipulated time in consideration with certain quality of services. Multiple virtual machine instances can be launched by defining different configurations such as operating system, server types, and applications. Though workflow scheduling is an NP-Hard problem, variety of decision-making techniques are available for optimum resource allocation. In this research paper, different algorithms are studied and compared with evolutionary approaches. Workflow scheduling using genetic algorithm is implemented and discussed. This paper aims to design a decision-making technique to optimize resources of cloud. It is an adaptive scheduling to maximize profit by reducing execution time. The approach implemented is useful to cloud service providers to maximize profit and resource efficiency in their services.

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A GSA based hybrid algorithm for bi-objective workflow scheduling in cloud computing

Cited by 146 publications

References 19 publications

A cloud computing scheduling and its evolutionary approaches

A cloud computing scheduling and its evolutionary approaches

Minimizing Monetary Costs for Deadline Constrained Workflows in Cloud Environments

Adaptive Workflow Scheduling Using Evolutionary Approach in Cloud Computing

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