RepECDuet: Ensure High Reliability and Performance for Deduplicated and DeltaCompressed Storage Systems Data deduplication is a widely deployed technique to remove duplicate content to save storage space, which is however incapable of eliminating the redundancy between nonidentical but similar data blocks. To achieve further space savings in deduplicated storage systems, delta compression is employed to compress postdeduplication data. Both deduplication and delta compression introduce content references among blocks, which inevitably undermines the reliability of deduplicated and delta compressed storage systems. To ensure better reliability, existing approaches utilize either replication or erasure codes to redundantly distribute data across multiple nodes. In deduplicated and delta compressed storage systems, we observe that delta compressed chunks (DCCs) are far smaller than regular chunks called nonDCCs. Motivated by this observation, we suggest a straightforward approach in which replication is used to protect DCCs and erasure code is deployed to protect nonDCCs. However, we need to address two critical challenges to ensure this solution effective. First, the random placement of DCCs replicas destroys cache locality. Second, the separate and individual recovery and restore cache could cause storage containers to be accessed repeatedly. To address these two challenges, in this paper, we propose RepECDuet which employs both replication and erasure codes to ensure high reliability and performance for deduplicated and deltacompressed storage systems. RepECDuet introduces a deltautilizationaware filter to select and replicate containers based on the percentage of DCCs in the containers to maintain cache locality. Moreover, to avoid unnecessary container reads, we design a cooperative cache scheme that is aware of both failure recovery and regular restore cache. Our experimental results based on three realworld datasets demonstrate that RepECDuet significantly improves the restore performance by 26%59%, and reduces the storage overhead by 54%98% than the existing approaches.

StorageReliabilityRepair TradeOffs in Distributed Storage System For reliability purpose, data redundancy is anessential in distributed storage system. In the evolving of data redundancy schemes, storagereliabilityrepair tradeoffs always exist. From replication to erasure codes, higher reliability achieves with lower storage cost, but the higher repair penalty can not be ignored. Optimized data redundancy schemes like regenerating codes or basic pyramid codes are proposed to relieve the high repair penalty. In order to study the storagereliabilityrepair tradeoffs, we propose a new model based on the standard Markov Chain, it takes the irregular fault tolerance, latent sector error and correlated failures into account, and measures the storage and repair cost from system perspective. Through interscheme and intrascheme discussions, we find some principles about choosing data redundancy schemes and corresponding parameters to achieve better storagereliabilityrepair tradeoffs. Our goal is to provide system designers and administrators with concrete information to help them achieve proper storagereliabilityrepair tradeoffs.