A Job Dispatch Optimization Method on Cluster and Cloud for Large-Scale High-Throughput Computing Service

Choi, Jieun; Kim, Seo Young; Adufu, Theodora; Hwang, Soonwook; Kim, Yoonhee

doi:10.1109/iccac.2015.42

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…There are various tasks for offloading from user devices, and tasks with distinct types have requirements of resources with various characteristics. Thus tasks with different types should be repectively managed by different methods to achieve the best result [107]. The task type is a useful differentiating factor for related literatures to understand the applicability of an offloading method to a task type.…”

Section: A Task Typementioning

confidence: 99%

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

et al. 2020

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Edge-cloud computing, combining the benefits of both edge computing and cloud computing, is one of the most promising ways to address the resource insufficiency of smart devices. Task offloading is an important challenge must be addressed for edge-cloud computing in practice, which decides the place and the time for performing each task. Even though there is existing research focusing on the task offloading in edge-cloud computing, a lot of problems should be solved before the application of these offloading technologies. Thus, in this article, we first propose a taxonomy of task offloading in edge-cloud environments to investigate and classify related research articles, and then summarize several challenges which have not been addressed for future research directions on this area to promote the development of edge-cloud market.

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Section: A Task Typementioning

confidence: 99%

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

et al. 2020

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“…GCSM which is consisting of a scheduler component named as Green Cloud Scheduler for task allocation and scheduling decisions by considering the energy aware capability of heterogeneous Cloud nodes. Jieun Choi et.al [20] proposed job scheduling optimization technique which focuses on the performance & QoS metrics in cluster and cloud environments. As a result, this technique controls the ratio of scheduling distribution for distributed environment with respect to characteristics of application and current workload.…”

Section: Related Workmentioning

confidence: 99%

A Novel Energy Efficient Resource Management System in Cloud Computing Environment.

Sonkar¹,

Kharat²

2018

IJET

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Primary target of cloud provider is to provide the maximum resource utilization and increase the revenue by reducing energy consumption and operative cost. In the service providers point of view, resource allocation, resource sharing, migration of resources on demand, memory management, storage management, load balancing, energy efficient resource usage, computational complexity handling in virtualization are some of the major tasks that has to be dealt with. The major issue focused in this paper is to reduce the energy consumption problem and management of computation capacity utilization. For the same, an energy efficient resource management method is proposed to grip the resource scheduling and to minimize the energy utilized by the cloud datacenters for the computational work. Here a novel resource allocation mechanism is proposed, based on the optimization techniques. Also a novel dynamic virtual machine (VM) allocation method is suggested to help dynamic virtual machine allocation and job rescheduling to improve the consolidation of resources to execute the jobs. Experimental results indicated that proposed strategy outperforms as compared to the existing systems.

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“…The creation and full usage of resources are also significant factors during scheduling [14]- [17]. Gang scheduling is used in distributed systems, and it is a very useful function [18]. The scheduling algorithm is necessary for assigning processors to the current activities in a distributed system.…”

Section: Introductionmentioning

confidence: 99%

Ganging of Resources via Fuzzy Manhattan Distance Similarity with Priority Tasks Scheduling in Cloud Computing

Priya¹,

Mehata²,

Banu³

2018

JTIT

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This paper proposes a fuzzy Manhattan distance-based similarity for gang formation of resources (FMDSGR) method with priority task scheduling in cloud computing. The proposed work decides which processor is to execute the current task in order to achieve efficient resource utilization and effective task scheduling. FMDSGR groups the resources into gangs which rely upon the similarity of resource characteristics in order to use the resources effectively. Then, the tasks are scheduled based on the priority in the gang of processors using gang-based priority scheduling (GPS). This reduces mainly the cost of deciding which processor is to execute the current task. Performance has been evaluated in terms of makespan, scheduling length ratio, speedup, efficiency and load balancing. CloudSim simulator is the toolkit used for simulation and for demonstrating experimental results in cloud computing environments.

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A Job Dispatch Optimization Method on Cluster and Cloud for Large-Scale High-Throughput Computing Service

Cited by 7 publications

References 7 publications

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

A Novel Energy Efficient Resource Management System in Cloud Computing Environment.

Ganging of Resources via Fuzzy Manhattan Distance Similarity with Priority Tasks Scheduling in Cloud Computing

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