Bandwidth-Aware Rescheduling Mechanism in SDN-Based Data Center Networks
Autor: | Chao-Lin Chen, Ming-Chin Chuang, Chiajui Hung |
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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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