Intelligently modeling, detecting, and scheduling elephant flows in software defined energy cloud: A survey

Ling Xia Liao, Han Chieh Chao, Mu Yen Chen

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Elephant flows (elephants) refer to the sequences of packets that contribute only 10% of the total volume but consume over 90% of the network bandwidth. They often cause network congestion and should be efficiently managed. Present cloud data centers often involve host- and switch-based approaches to detect and schedule elephants, but suffer (1) each host and switch in the network needs to be customized, and (2) dynamic models and advanced policies are difficult to be applied. Software Defined Cloud (SDC) addresses these issues by enabling controller-based approaches. With the aid of Machine Learning (ML) technologies, SDC can achieve learning-based models, flexible deployment, and early detection and schedule of elephants for the optimization of network performance and energy usage in a dynamic and intelligent manner. On this purpose, this article emphases the significance of models describing elephants, surveys the mechanisms that may apply to model, detect, and schedule elephants for SDC to optimize the network performance and energy usage. To the best of our knowledge, this work is the first effort that reviews the techniques in all these related subtopics simultaneously in the context of energy cloud.

Original languageEnglish
Pages (from-to)64-78
Number of pages15
JournalJournal of Parallel and Distributed Computing
Volume146
DOIs
Publication statusPublished - 2020 Dec

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence

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