关键词: 云存储, 异构文件, 联合延迟, 尾概率, 凸优化

Abstract:

With the popularity of erasure code technology, high data reliability and high spatial efficiency storage performance are gradually realized in distributed storage, but reducing tail delay is still a problem to be solved. Therefore, this paper proposes an algorithm framework to quantify and optimize the tail delay of erasure coding storage system. For any service time distribution and heterogeneous files, the upper bound of tail delay is derived. Then, an optimization model is proposed to minimize the weighted delay tail probability of all files placed on the server and the server selection of access request files, and its nonconvex characteristics are proved, so that an efficient alternative optimization algorithm can be used. In addition, by describing the asymptotic behavior of the tail of delay distribution, the tail index of service delay of arbitrary erasure code storage is mathematically quantified in closed form, and it is proved that the algorithm based on probability scheduling is（asymptotically） optimal. The experimental results show that the tail delay of erasure coding storage system is significantly reduced under the actual workload.

Key words: cloud storage, heterogeneous files, joint delay, tail probability, convex optimization