Big data analysis for distributed computing job scheduling and reliability evaluation
| Autor: | Yung-Tsung Hou, Shiow-Luan Wang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Job scheduler
Computer science Distributed computing Big data 02 engineering and technology computer.software_genre 01 natural sciences Scheduling (computing) Computer cluster Server 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Safety Risk Reliability and Quality 010302 applied physics Job shop scheduling business.industry 020208 electrical & electronic engineering Approximation algorithm Condensed Matter Physics Atomic and Molecular Physics and Optics Surfaces Coatings and Films Electronic Optical and Magnetic Materials Batch processing business computer |
| Zdroj: | Microelectronics Reliability. 94:41-45 |
| ISSN: | 0026-2714 |
| DOI: | 10.1016/j.microrel.2019.01.010 |
| Popis: | The aim of this study is to present a distributed batch job scheduling problem that is practical in big data analysis. When a computer cluster receives computing jobs from different sources, different jobs can be combined into a time slot for batch processing. The cluster has multiple servers for batch processing, and a computing job can be split into different time slots on a server. The objective of the scheduling is to minimize the total completion time of all jobs. We show that this problem is NP hard, and this paper also proposes a ( 2 − 1 m ) approximation algorithm for this batch scheduling problem, where m is the number of servers, and follows up the reliability evaluation to make the system more stable and reliable. |
| Databáze: | OpenAIRE |
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