Workflow scheduling in cloud: a survey

Wu, Fuhui; Wu, Qingbo; Tan, Yusong

doi:10.1007/s11227-015-1438-4

Cited by 228 publications

(113 citation statements)

References 180 publications

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“…For instance, [127] present a survey focusing on resource scheduling that also comprises several of the components proposed by [72], such as provisioning, allocation, and modeling. Three definitions were selected due to their clear definition of steps and components of resource management.…”

Section: Intercorrelation and Consolidationmentioning

confidence: 99%

“…These properties are fully or at least partially covered by the other taxonomies, such as [9], with cost, makespan, and reliability; [112], with unpredictability management (closely related to dynamic properties and reliability) and rescheduling; [127], with deadline, budget, reliability, and energy; and [108], with cost, time, and energy. In addition, the proposed taxonomy encompasses some of the attributes of interest to this work, such as hybrid and multicloud aspects, and workflow resource management.…”

Section: Proposed Taxonomymentioning

confidence: 99%

“…Consequently, cloud resource management for workflow execution is a topic of broad and current interest [127]. Moreover, there are few researches on scheduling workflows on real cloud environments, and much fewer cloud workflow management systems, which require even further academic study and industrial practice [127].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, there are few researches on scheduling workflows on real cloud environments, and much fewer cloud workflow management systems, which require even further academic study and industrial practice [127]. Workflow scheduling for commercial multicloud environments, for instance, still is an open issue to be addressed [32].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, data transfer between tasks is not directly considered in most existing studies, thus being assumed as part of task execution. However, this is not the case for data-intensive applications [127], especially ones from the big data era, wherein data movement can dominate both the execution time and cost.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Cloud resource management: towards efficient execution of large-scale scientific applications and workflows on complex infrastructures

2017

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Cloud computing evolved from the concept of utility computing, which is defined as the provision of computational and storage resources as a metered service. Another key characteristic of cloud computing is multitenancy, which enables resource and cost sharing among a large pool of users. Characteristics such as multitenancy and elasticity perfectly fit the requirements of modern data-intensive research and scientific endeavors. In parallel, as science relies on the analysis of very large data sets, data management and processing must be performed in a scalable and automated way. Workflows have emerged as a way to formalize and structure data analysis, thus becoming an increasingly popular paradigm for scientists to handle complex scientific processes. One of the key enablers of this conjunction of cloud computing and scientific workflows is resource management. However, several issues related to data-intensive loads, complex infrastructures such as hybrid and multicloud environments to support large-scale execution of workflows, performance fluctuations, and reliability, pose as challenges to truly position clouds as viable high-performance infrastructures for scientific computing. This paper presents a survey on cloud resource management that provides an extensive study of the field. A taxonomy is proposed to analyze the selected works and the analysis ultimately leads to the definition of gaps and future challenges to be addressed by research and development.

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Section: Intercorrelation and Consolidationmentioning

confidence: 99%

Section: Proposed Taxonomymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cloud resource management: towards efficient execution of large-scale scientific applications and workflows on complex infrastructures

2017

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Multi‐objective approach of energy efficient workflow scheduling in cloud environments

Rehman

Hussain

Rehman

et al. 2018

Concurrency and Computation

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Scheduling the tasks of a workflow to the cloud resources is a well-known N-P hard problem. The stakeholders involved in a cloud environment have different interests in scheduling problem. In addition to the traditional objectives like makespan, budget, and deadline, optimized in workflow scheduling, considering the green aspect of cloud, (ie, energy consumption) increase the problem complexity. Moreover, the interests of a cloud's stakeholders are conflicting, and satisfying all these interests simultaneously is a big problem. In this paper, we proposed a new Multi-Objective Genetic Algorithm(MOGA) for workflow scheduling in a cloud environment. MOGA considered the conflicting interest of the cloud stakeholders for optimization and provided a solution, which not only minimizes the makespan under the budget and deadline constraints but also provided an energy efficient solution using the dynamic voltage frequency scaling. We provided a gap search algorithm in this paper, which is used to optimize the resource utilization of the cloud's resources.We compared our results with genetic algorithms considering the budget, deadline, and energy efficiency individually. We also compared the performance of MOGA with Multi-objective Particle Swarm Optimization (MOPSO) with the same objectives as those of MOGA. To the best of our knowledge, there is no solution presented in the literature that considers the diverse objectives considered in this work. The results show that our proposed algorithm MOGA has significantly improved not only in terms of budget, deadline, and energy but also improved the utilization of cloud's resources as compared to the competitive algorithms of this work.

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Workflow scheduling using particle swarm optimization and gray wolf optimization algorithm in cloud computing

Arora

Banyal

2021

Concurrency and Computation

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Cloud computing is one of the emerging technologies in computer science in which services are provided through the internet on-demand. Workflow scheduling is considered to be an NP-hard problem and has a significant issue in the cloud environment.Finding the polynomial-time solutions for workflow scheduling problem is difficult with most of the existing algorithms designed for traditional computing platforms. Some existing meta-heuristics algorithms proposed for workflow scheduling problem are stuck in the local optimal solution and fails to give the global optimal solution. In this article, a hybrid of particle swarm optimization and gray wolf optimization, named the PSO-GWO algorithm, is proposed for workflow scheduling. The proposed algorithm was tested to reduce the total executing cost (TEC) and total execution time (TET) of the dependent tasks in the cloud computing environment. The proposed algorithm takes advantage of both the standard PSO and GWO algorithms and does not stick in the local optimal solution. The experiment results show that the PSO-GWO outperformed compared with the standard PSO and GWO algorithm in TEC and TET.

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Workflow scheduling in cloud: a survey

Cited by 228 publications

References 180 publications

Cloud resource management: towards efficient execution of large-scale scientific applications and workflows on complex infrastructures

Cloud resource management: towards efficient execution of large-scale scientific applications and workflows on complex infrastructures

Multi‐objective approach of energy efficient workflow scheduling in cloud environments

Workflow scheduling using particle swarm optimization and gray wolf optimization algorithm in cloud computing

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