Reducing Insertion Time in LRC-based Cloud Storage Systems

Abstract

In traditional erasure codes, all the redundant data are created and uploaded to the different storage nodes by a unique source node. However, such a source node may have limited communication and computation capabilities, which constrain the storage process throughput. In-network redundancy generation can improve data insertion throughput through distributing the data insertion load among the source and storage nodes. But it's hard to schedule the generation process. Many works have proposed some heuristic scheduling algorithms to improve data insertion throughput. We propose a new method combined global and local optimization to schedule the process. Experimental results show that our method reduce insertion time up to 18% compared with the best heuristic scheduling algorithm.