QoS-Aware Scheduling of Workflows in Cloud Computing Environments

Bousselmi, Khadija; Brahmi, Zaki; Gammoudi, Mohamed Mohsen

doi:10.1109/aina.2016.72

Cited by 15 publications

(17 citation statements)

References 12 publications

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“…Much research on task scheduling and resource allocation are available for cloud and fog computing, attempting to resolve the abovementioned concerns. Bousselmi et al [12], focused on QoS-based workflow planning. Their scheduling algorithm leads to increase the quality of service (QoS) in cloud computing, based on certain metrics like resources availability, cost, and data transmission time.…”

Section: Related Workmentioning

confidence: 99%

TACRM: trust access control and resource management mechanism in fog computing

Daoud

Obaidat

Meddeb-Makhlouf

et al. 2019

Hum. Cent. Comput. Inf. Sci.

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Despite the fact that cloud computing is an effective solution for handling data in distributed environments, it is considered as an appropriate way to efficiently process the mass data generated by IoT devices [1]. It delivers centralized resources for data computation and storage, which can affect metrics like delay and bandwidth limitation [2, 3]. Inherently, Fog nodes are distributed within the proximity of users; a characteristic that reduces latency and establishes adjacent localized connections. Recently, the combination of cloud/fog, and IoT communication networks has received a great attention and widely emerged [4]. IoT exploits the fog computing capacities for virtualizing the tasks of IoT devices, but it still has restricted capability and acquires long delay [5]. Though the primary purpose of Fog paradigm is to achieve all tasks with high performance, the security features must be considered as part of the Fog system to guarantee

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Section: Related Workmentioning

confidence: 99%

TACRM: trust access control and resource management mechanism in fog computing

Daoud

Obaidat

Meddeb-Makhlouf

et al. 2019

Hum. Cent. Comput. Inf. Sci.

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“…The algorithm for scheduling is based onParallel Cat Swarm Optimization (PCSO). The scheduling is divided into two parts, one selects a storage which optimizes the storage of the workow to the best level and the second selects computing resources which optimizes the quality of the Workow[12].…”

mentioning

confidence: 99%

Prioritized task scheduling in fog computing

CHOUDHARI

Moh

2018

Proceedings of the ACMSE 2018 Conference

114

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“…In the Cloud Computing environment, the overall objective of task scheduling strategy is to guarantee the service-level agreements (SLAs) of allocated resources to make cost-efficient decisions for workload placement. Since task scheduling problem on Cloud Computing environments is NP-complete [19], several heuristics have been proposed to solve using Genetic Algorithms [38], Particle Swarm Optimization [39], Ant Colony Optimization [40], and Cat Swarm Optimization [5] [6]. However, the problem is still challenging due to the dynamic nature of the Cloud Computing environment and to the variability and confliction of the defined scheduling objectives.…”

mentioning

confidence: 99%

“…This paper is an extension of the works originally reported in [5] and [6]. We use our proposed partitioning algorithm in [6] for the partitioning of scientific workflows and our proposed scheduling algorithm in [5] for their scheduling in the cloud.…”

mentioning

confidence: 99%

DR-SWDF: A Dynamically Reconfigurable Framework for Scientific Workflows Deployment in the Cloud

Bousselmi¹,

Brahmi²,

Gammoudi³

2017

SCPE

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Workflows management systems (WfMS) are aimed for designing, scheduling, executing, reusing, and sharing workflows in distributed environments like the Cloud computing. With the emergence of e-science workflows, which are used in different domains like astronomy, life science, and physics, to model and execute vast series of dependents functionalities and a large amount of manipulated data, the workflow management systems are required to provide customizable programming environments to ease the programming effort required by scientists to orchestrate a computational science experiment. A key issue for e-science WfMS is how to deal with the change of the execution environment constraints and the variability and confliction of end users and cloud providers objectives for the execution of the same workflow or sub-workflow. They have to customize their management processes to insure the adaptability of the execution environment to the scientific workflows specificities, especially when dealing with large-scale (data, computing, I/O)-intensive workflows. In this paper, we propose a dynamically re-configurable framework for the deployment of scientific workflows in the Cloud (called DR-SWDF) that allows customizing the workflow deployment process according to a set of objectives and constraints of end users or cloud providers defined differently for the tasks or partitions of the same workflow. The DR-SWDF framework offers a K-means based algorithm that allows dynamically clustering the input workflows or sub-workflows in order to identify the most convenient techniques or algorithms to be applied for their scheduling and deployment in the cloud. The simulations results run on three examples large-scale scientific workflows show that our proposed framework can achieve better results than the use of a generic purpose approach.

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QoS-Aware Scheduling of Workflows in Cloud Computing Environments

Cited by 15 publications

References 12 publications

TACRM: trust access control and resource management mechanism in fog computing

TACRM: trust access control and resource management mechanism in fog computing

Prioritized task scheduling in fog computing

DR-SWDF: A Dynamically Reconfigurable Framework for Scientific Workflows Deployment in the Cloud

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