Redundancy-Guaranteed and Receiving-Constrained Disaster Backup in Cloud Data Center Network

Abstract

For disaster backup in cloud data center network, existing researches have not jointly considered sufficient data redundancy and limited receiving capacity, likely to result in underutilization of network transmission capability, unfair distribution of backup load, or even lacks of disaster resistance. In this paper, we propose a new strategy to realize bandwidth-efficient and load-fair disaster backup under redundancy and capacity constraints using customized bandwidth allocation and flexible flow scheduling in software-defined networking. Based on many-to-many relationship in disaster backup, we formulate a new redundancy-guaranteed and receiving-constrained capacitated multi-commodity flow problem. By constructing flow-ratio-constrained backup transmission model, we specify flow allocation ratio among backup data centers with limited receiving capacity. Then we present a basic ratio-aware ant colony optimization algorithm satisfying backup flow constraint and rate requirement constraint. Furthermore, to obtain higher performance in redundancy guarantee and enhance bandwidth allocation fairness among massive backup transfers, we propose a fair-rotating and ratio-aware ant colony optimization (FRRA-ACO) algorithm. Especially, we use rotary routing search for multiple concurrent flows based on backup requirement cloning to approximate the upper bound of bandwidth allocation, adjust ratio of bandwidth allocation for multiple backup transfers with different requirements, and further improve flow rate according to the maximum link utilization on links if possible. Experiments demonstrate that FRRA-ACO outperforms state-of-the-art algorithms with less backup completion time, fairer backup load distribution, and higher network utilization.