A Group-Based Job Scheduling Method for Parallel Volunteer Computing

2015 ◽  
Author(s):  
Kaworu Ochi ◽  
Masaru Fukushi
Keyword(s):  
Job Scheduling ◽  
Volunteer Computing ◽  
Scheduling Method

scholarly journals Dynamic Job Scheduling Method Based on Expected Probability of Completion of Voting in Volunteer Computing

2015 ◽  
Vol E98.D (12) ◽  
pp. 2132-2140
Author(s):  
Yuto MIYAKOSHI ◽  
Shinya YASUDA ◽  
Kan WATANABE ◽  
Masaru FUKUSHI ◽  
Yasuyuki NOGAMI
Keyword(s):  
Job Scheduling ◽  
Volunteer Computing ◽  
Scheduling Method

scholarly journals ROA-CONS: Raccoon Optimization for Job Scheduling

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2270
Author(s):  
Sina Zangbari Koohi ◽  
Nor Asilah Wati Abdul Hamid ◽  
Mohamed Othman ◽  
Gafurjan Ibragimov
Keyword(s):  
User Satisfaction ◽  
High Performance ◽  
Job Scheduling ◽  
Response Times ◽  
Scheduling Algorithm ◽  
Desktop Computer ◽  
Network Heterogeneity ◽  
Parallel Job Scheduling ◽  
Scheduling Method ◽  
And Performance

High-performance computing comprises thousands of processing powers in order to deliver higher performance computation than a typical desktop computer or workstation in order to solve large problems in science, engineering, or business. The scheduling of these machines has an important impact on their performance. HPC’s job scheduling is intended to develop an operational strategy which utilises resources efficiently and avoids delays. An optimised schedule results in greater efficiency of the parallel machine. In addition, processes and network heterogeneity is another difficulty for the scheduling algorithm. Another problem for parallel job scheduling is user fairness. One of the issues in this field of study is providing a balanced schedule that enhances efficiency and user fairness. ROA-CONS is a new job scheduling method proposed in this paper. It describes a new scheduling approach, which is a combination of an updated conservative backfilling approach further optimised by the raccoon optimisation algorithm. This algorithm also proposes a technique of selection that combines job waiting and response time optimisation with user fairness. It contributes to the development of a symmetrical schedule that increases user satisfaction and performance. In comparison with other well-known job scheduling algorithms, the simulation assesses the effectiveness of the proposed method. The results demonstrate that the proposed strategy offers improved schedules that reduce the overall system’s job waiting and response times.

scholarly journals Multi-objective Prediction based Task Scheduling Method in Cloud Computing

2019 ◽  
Vol 8 (4) ◽  
pp. 9388-9394 ◽  
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Task Scheduling ◽  
Job Scheduling ◽  
Sigmoid Function ◽  
Service Model ◽  
Multi Objective ◽  
Computing Paradigm ◽  
Optimal Node ◽  
Scheduling Method

Cloud Computing is Internet based computing where one can store and access their personal resources from any computer through Internet. Cloud Computing is a simple pay-per-utilize consumer-provider service model. Cloud is nothing but large pool of easily accessible and usable virtual resources. Task (Job) scheduling is always a noteworthy issue in any computing paradigm. Due to the availability of finite resources and time variant nature of incoming tasks it is very challenging to schedule a new task accurately and assign requested resources to cloud user. Traditional task scheduling techniques are improper for cloud computing as cloud computing is based on virtualization technology with disseminated nature. Cloud computing brings in new challenges for task scheduling due to heterogeneity in hardware capabilities, on-demand service model, pay-per-utilize model and guarantee to meet Quality of Service (QoS). This has motivated us to generate multi-objective methods for task scheduling. In this research paper we have presented multi-objective prediction based task scheduling method in cloud computing to improve load balancing in order to satisfy cloud consumers dynamically changing needs and also to benefit cloud providers for effective resource management. Basically our method gives low probability value for not capable and overloaded nodes. To achieve the same we have used sigmoid function and Euclidean distance. Our major goal is to predict optimal node for task scheduling which satisfies objectives like resource utilization and load balancing with accuracy.

scholarly journals PENINGKATAN KAPASITAS PRODUKSI MELALUI PENERAPAN THEORY OF CONSTRAINT, PENJADWALAN MESIN PARALEL DAN BOTTLENECK SCHEDULING PADA PERUSAHAAN SHEET METAL WORK

2013 ◽  
Vol 3 (2) ◽  
Author(s):  
Sonia Nur Indah Suci ◽  
Nora Azmi ◽  
Sumiharni Batubara
Keyword(s):  
Processing Time ◽  
Parallel Machines ◽  
Job Scheduling ◽  
Production Capacity ◽  
Theory Of Constraints ◽  
Job Sequencing ◽  
Short Processing Time ◽  
Current Production ◽  
Scheduling Method ◽  
Theory Of Constraint

<p>This study aims to increase the production capacity of the server rack 08U Type Double ASeries<br />Wallmounted BRI on company X. Current production did not reached the expected target of<br />117 units / week because of the bottleneck workstation. The study began by identifying the bottleneck<br />workstation using the Theory of Constraints (TOC). The result of identification process showed two<br />bottleneck work stations, that are work station Punching and Ovencoating. The production capacity<br />was improved by performing the steps as follows: 1) Add 1 machine at punching station, 2) perform<br />job scheduling on parallel machines at punching station using Short Processing Time (SPT) criteria;<br />3) perform job sequencing on bending station using Weighted Shortest Processing Time (WSPT)<br />criteria; 4 ) adds overtime at ovencoating stations, and 5) implementing bottleneck scheduling method<br />to sequence all jobs. The Results of this study showed that makespan was reduced by 12.03% and<br />production capacity increased by 41.9% so that the production target of 117% can be achieved.</p>

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