An Ant Colony Optimization Based Data Update Scheme for Distributed Erasure-Coded Storage Systems

Owing to the high availability and space-efficiency of erasure codes, they have become the de facto standard to provide data durability in large scale distributed storage systems. The update-intensive workloads of erasure codes lead to a large amount of data transmission and I/O. As a result, it becomes a major challenge to reduce the amount of data transmission and optimize the use of existing network resources so that the update efficiency of the erasure codes could be improved. However, very little research has been done to optimize the update efficiency of the erasure codes under multiple QoS metrics. In this paper, our proposed update scheme, the Ant Colony Optimization based multiple data nodes Update Scheme (ACOUS) employs a two-stage rendezvous data update procedure to optimize the multiple data nodes updates. Specifically, the two-stage rendezvous data update procedure performs the data delta collection and the parity delta distribution based on a multi-objective update tree which is built by the ant colony optimization routing algorithm. Under typical data center network topologies, extensive experimental results show that, compared to the traditional TA-Update scheme, our scheme is able to achieve a 26% to 37% reduction of update delay with convergence guarantee at the cost of negligible computation overhead.