Data center traffic engineering using Markov approximation
Data center traffic engineering using Markov approximation
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DOI：
10.1109/icoin.2017.7899499
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
K. Hirata;M. Yamamoto
中科院分区：
文献类型：
--
作者：
K. Hirata;M. Yamamoto
本文提出一种采用马尔可夫近似的数据中心流量工程方案。马尔可夫近似是一种分布式优化框架。它通过用户的独立行为来优化网络，基于网络中最少的必要信息构建时间可逆的连续时间马尔可夫链。所提出的方案旨在通过马尔可夫近似来最小化数据中心网络中的最大链路利用率。所提出的方案提供了两种具有不同搜索空间的策略。通过仿真实验，我们表明所提出的方案不仅在静态情况下而且在数据中心网络中流量需求动态变化的动态情况下都有效地降低了最大链路利用率。
This paper proposes a data center traffic engineering scheme with Markov approximation. Markov approximation is a distributed optimization framework. It optimizes networks by independent behaviors of users that construct a time-reversible continuous-time Markov chain based on minimum necessary information in the networks. The proposed scheme aims at minimizing the maximum link utilization in data center networks by means of Markov approximation. The proposed scheme provides two strategies with different search space. Through simulation experiments, we show that the proposed scheme efficiently reduces the maximum link utilization under not only static situations but also dynamic situations where traffic demands dynamically change in data center networks.