A Workflow Scheduling Technique for Task Distribution in Spot Instance-Based Cloud

Jung, Daeyong; Lim, Jong Beom; Yu, Heonchang; Gil, Joon‐Min; Lee, Eun Young

doi:10.1007/978-3-642-41671-2_52

Cited by 6 publications

(6 citation statements)

References 4 publications

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“…In this paper, using environment expands workflow scheduling scheme from our previous paper [5]. Our task distribution method determines the task size in order to allocate a task to a selected instance.…”

Section: Estimation Methodsmentioning

confidence: 99%

An Estimation-Based Task Load Balancing Scheduling in Spot Clouds

Jung¹,

Choi²,

Lee

et al. 2014

Advanced Information Systems Engineering

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Abstract. Cloud computing is a computing paradigm in which users can rent computing resources from service providers according to their requirements. Cloud computing based on the spot market helps a user to obtain resources at a lower cost. However, these resources may be unreliable. In this paper, we propose an estimation-based distributed task workflow scheduling scheme that reduces the estimated generation compared to Genetic Algorithm (GA). Moreover, our scheme executes a user's job within selected instances and stretches the user's cost. The simulation results, based on a before-and-after estimation comparison, reveal that the task size is determined based on the performance of each instance and the task is distributed among the different instances. Therefore, our proposed estimation-based task load balancing scheduling technique achieves the task load balancing according to the performance of instances.

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Section: Estimation Methodsmentioning

confidence: 99%

An Estimation-Based Task Load Balancing Scheduling in Spot Clouds

Jung¹,

Choi²,

Lee

et al. 2014

Advanced Information Systems Engineering

Self Cite

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“…Some authors have studied how to implement hybrid provisioning of resources between several cloud providers, or even between different computing infrastructures such as grids and clouds . The workflow model is widely used in many scientific computing areas, and there is a vast amount of studies regarding the feasibility and challenges of executing workflows in the cloud …”

Section: Related Workmentioning

confidence: 99%

Resource provisioning in Science Clouds: Requirements and challenges

García

Fernández-del-Castillo

Fernández

et al. 2017

Softw Pract Exp

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SummaryCloud computing has permeated into the IT industry in the last few years, and it is nowadays emerging in scientific environments. Science user communities are demanding a broad range of computing power to satisfy high-performance applications needs, such as local clusters, High Performance Computing (HPC) systems and computing grids. Different workloads need from different computational models, and the cloud is already considered as a promising paradigm.The scheduling and allocation of resources is always a challenging matter in any form of computation and clouds are not an exception. Science applications have unique features that differentiate their workloads, hence their requirements have to be taken into consideration to be fulfilled when building a Science Cloud. This paper will discuss what are the main scheduling and resource allocation challenges for any Infrastructure as a Service IaaS provider supporting scientific applications.

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“…Finally, the experiment demonstrates that the proposed algorithm is effectual and well-organized. Finally, Jung, Lim [35] have presented the workflow scheduling scheme that reduces the task waiting time when an instance occurs the out-of-bid situation.…”

Section:  2013mentioning

confidence: 99%

Multi-Objective Task Scheduling in the Cloud Computing based on the Patrice Swarm Optimization

Milani¹,

Navin²

2015

IJITCS

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Abstract-Cloud computing is the latest emerging trend in distributed computing, where shared resources are provided to end-users in an on demand fashion that brings many advantages, including data ubiquity, flexibility of access, high availability of resources, and flexibility. In this type of systems many challenges are existed that the task scheduling problem is one of them. The task scheduling problem in Cloud computing is an NP-hard problem. Therefore, many heuristics have been proposed, from low level execution of tasks in multiple processors to high level execution of tasks. In this paper, we propose a new algorithm based on PSO to schedule the tasks in the Cloud. The results demonstrated that the proposed algorithm has a better operation in terms of task execution time, waiting time and missed tasks in comparison of First Come First Served (FCFS), Shortest Process Next (SPN) and Highest Response Ratio Next (HRRN).

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A Workflow Scheduling Technique for Task Distribution in Spot Instance-Based Cloud

Cited by 6 publications

References 4 publications

An Estimation-Based Task Load Balancing Scheduling in Spot Clouds

An Estimation-Based Task Load Balancing Scheduling in Spot Clouds

Resource provisioning in Science Clouds: Requirements and challenges

Multi-Objective Task Scheduling in the Cloud Computing based on the Patrice Swarm Optimization

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