NewBalance: Efficient data space management and algorithmic optimization for cloud storage
Fragmentation usually occurs when data space of original storage nodes has to be reallocated to new added storage nodes during the scale-out evolution of the large-scale storage system.It greatly influences the performance of the large-scale storage system.In this paper,we present an efficient space management framework,called NewBalance,to reduce fragmentation with the minimum data movement while keeping the storage system load balance.The space management framework has two phases including the collection phase and the allocation phase.For the collection phase,we propose a novel algorithm,called the greedy bi-direction collector,which collects enough space for the new storage nodes.For the allocation phase,we formally represent it as one variant of the bin packing problem and then utilize some bin packing heuristics including the first fitting and the best fitting to allocate collected intervals to new added storage nodes.The experimental results show that the amount of intervals can be reduced by 20%~55%and our algorithmic optimization improves the data lookup performance by at least 10%and the scale-out performance by 2×~3×.
Guangping Xu Sheng Lin Xing Guo Kai Shi Hua Zhang
Tianjin Key Lab. of Intelligence Computing and New Software Technology,Tianjin University of Technology,Tianjin,China 300384
国内会议
第10届全国计算机支持的协同工作学术会议暨中国计算机学会协同计算专委年度工作会议
太原
英文
474-484
2015-08-28（万方平台首次上网日期，不代表论文的发表时间）