Locally Minimum Storage Regenerating Codes in Distributed Cloud Storage Systems

In distributed cloud storage systems, inevitably there exist multiple node failures at the same time. The existing methods of regenerating codes, including minimum storage regenerating(MSR) codes and minimum bandwidth regenerating(MBR) codes, are mainly to repair one single or several failed nodes, unable to meet the repair need of distributed cloud storage systems. In this paper, we present locally minimum storage regenerating(LMSR) codes to recover multiple failed nodes at the same time. Specifically, the nodes in distributed cloud storage systems are divided into multiple local groups, and in each local group(4, 2) or(5, 3) MSR codes are constructed. Moreover, the grouping method of storage nodes and the repairing process of failed nodes in local groups are studied. Theoretical analysis shows that LMSR codes can achieve the same storage overhead as MSR codes. Furthermore, we verify by means of simulation that, compared with MSR codes, LMSR codes can reduce the repair bandwidth and disk I/O overhead effectively.

Erasure Coding for Cloud Storage Systems: A Survey

In the current era of cloud computing, data stored in the cloud is being generated at a tremendous speed, and thus the cloud storage system has become one of the key components in cloud computing. By storing a substantial amount of data in commodity disks inside the data center that hosts the cloud, the cloud storage system must consider one question very carefully： how do we store data reliably with a high efficiency in terms of both storage overhead and data integrity？ Though it is easy to store replicated data to tolerate a certain amount of data losses, it suffers from a very low storage efficiency. Conventional erasure coding techniques, such as Reed-Solomon codes, are able to achieve a much lower storage cost with the same level of tolerance against disk failures. However, it incurs much higher repair costs, not to mention an even higher access latency. In this sense, designing new coding techniques for cloud storage systems has gained a significant amount of attention in both academia and the industry. In this paper, we examine the existing results of coding techniques for cloud storage systems. Specifically, we present these coding techniques into two categories： regenerating codes and locally repairable codes. These two kinds of codes meet the requirements of cloud storage along two different axes： optimizing bandwidth and I/O overhead. We present an overview of recent advances in these two categories of coding techniques. Moreover, we introduce the main ideas of some specific coding techniques at a high level, and discuss their motivations and performance.