Bandwidth-Aware Rescheduling Mechanism in SDN-Based Data Center Networks

Autor: Chao-Lin Chen, Ming-Chin Chuang, Chiajui Hung
Rok vydání: 2021
Předmět:
Scheme (programming language)
TK7800-8360
Computer Networks and Communications
Computer science
Distributed computing
0211 other engineering and technologies
Cloud computing
02 engineering and technology
Task (project management)
Green computing
0202 electrical engineering
electronic engineering
information engineering

Bandwidth (computing)
Network performance
Electrical and Electronic Engineering
computer.programming_language
020203 distributed computing
Network architecture
021103 operations research
business.industry
Network packet
rescheduling
bandwidth-aware
Process (computing)
Network monitoring
Service provider
Hadoop
Hardware and Architecture
Control and Systems Engineering
Signal Processing
Data center
software-defined network
Electronics
business
Software-defined networking
computer
Computer network
Zdroj: Electronics, Vol 10, Iss 1774, p 1774 (2021)
Electronics
Volume 10
Issue 15
iThings/GreenCom/CPSCom/SmartData/Cybermatics
ISSN: 2079-9292
DOI: 10.3390/electronics10151774
Popis: Recently, with the increase in network bandwidth, various cloud computing applications have become popular. A large number of network data packets will be generated in such a network. However, most existing network architectures cannot effectively handle big data, thereby necessitating an efficient mechanism to reduce task completion time when large amounts of data are processed in data center networks. Unfortunately, achieving the minimum task completion time in the Hadoop system is an NP-complete problem. Although many studies have proposed schemes for improving network performance, they have shortcomings that degrade their performance. For this reason, in this study, we propose a centralized solution, called the bandwidth-aware rescheduling (BARE) mechanism for software-defined network (SDN)-based data center networks. BARE improves network performance by employing a prefetching mechanism and a centralized network monitor to collect global information, sorting out the locality data process, splitting tasks, and executing a rescheduling mechanism with a scheduler to reduce task completion time. Finally, we used simulations to demonstrate our scheme’s effectiveness. Simulation results show that our scheme outperforms other existing schemes in terms of task completion time and the ratio of data locality.
Databáze: OpenAIRE
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