计算机科学与探索 ›› 2020, Vol. 14 ›› Issue (9): 1490-1500.DOI: 10.3778/j.issn.1673-9418.1908062

• 学术研究 • 上一篇    下一篇

基于多目标分解策略的副本布局算法研究

邵必林,贺金能,边根庆   

  1. 1. 西安建筑科技大学 管理学院,西安 710055
    2. 西安建筑科技大学 信息与控制工程学院,西安 710055
  • 出版日期:2020-09-01 发布日期:2020-09-07

Research on Replica Layout Algorithm Based on Multi-objective Decomposition Strategy

SHAO Bilin, HE Jinneng, BIAN Genqing   

  1. 1. School of Management, Xi'an University of Architecture and Technology, Xi'an 710055, China
    2. School of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China
  • Online:2020-09-01 Published:2020-09-07

摘要:

高效的副本布局是分布式存储系统可靠性的重要保障。数据副本既可以增加系统数据的可用性,又能够提高系统的负载均衡能力,但同时也会带来能量消耗问题。针对副本带来的效能提升和能耗之间的冲突,提出了一种基于多目标分解策略的副本布局算法(MDSRL)。将平均文件不可用性、负载均衡、能耗作为三个优化对象,并将多目标优化问题分解成多个标量子问题同时进行优化,每一个子问题的优化都借助相邻的若干个子问题的信息,经过多次迭代优化后,试图找出一组能够在这三个目标上都有良好表现的折衷解。实验表明MDSRL算法所求出的解在平均文件不可用性和能耗上比多目标进化算法(MOE)减少了3.11个百分点和2.3个百分点,在平均文件不可用性和负载变化上比多目标副本管理算法(MORM)减少了68.1个百分点和0.2个百分点,且解的分布性和收敛性更好。

关键词: 分布式存储, 多目标优化, 分解策略, 副本布局

Abstract:

The efficient replica layout is an important guarantee for distributed storage systems reliability. Data replica can increase system data availability, as well as improve the system load balance ability, but it also produces energy consumption problems simultaneously. Aiming at the conflicts between efficiency improvement and energy consumption replicas bring, replica layout algorithm based on a multi-objective decomposition strategy (MDSRL) is proposed. It regards the mean file unavailability, load balance and energy consumption as optimization objectives,  decomposes multi-objective problems into multiple scalar problems and optimizes them simultaneously. The optimization of each scalar problem is based on the information of several adjacent scalar problems. After multiple iterations of optimization, this paper tries to find a set of tradeoff solutions that can perform well on all three objectives. Experiments demonstrate that the solutions obtained by the MDSRL algorithm are 3.11 and 2.3 percentage points less than the multi-objective evolutionary (MOE) algorithm in mean file unavailability and energy consumption, and are 68.1 and 0.2 percentage points less than the multi-objective replica management (MORM) algorithm in the mean file unavailability and load variance, and the solutions distribution and convergence are better.

Key words: distributed storage, multi-objective optimization, decomposition strategy, replica layout