For the sake of cost-effectiveness, it is a conventional wisdom to employ (k + r,k) erasure codes to archive rarely accessed replicas, i.e. erasure-coded data archival. Existing researches on erasure-coded data archival optimizations are mainly aimed to reduce archival traffic within storage clusters. Apart from archival traffic, both non-sequential reads and imbalanced loads can deteriorate archival performance. Traditional distributed archival schemes (DArch for short) for randomly distributed replicas tend to suffer from two problems: (i) non-sequential reads because underlying file systems split a data block into multiple smaller data chunks and (ii) imbalanced loads since archival tasks are assigned according to data locality of replicas. To overcome such drawbacks, we incorporate both prefetching mechanism and balancing strategy into erasure-coded archival for replica-based storage clusters, and propose three new archival schemes: a prefetching-enabled archival scheme (i.e. P-DArch), a balancing-enabled archival scheme (i.e. B-DArch) and a prefetching-and-balancing-enabled archival scheme (i.e. PB-DArch). We implement a proof-of-concept prototype, where all the four archival schemes are deployed and quantitatively evaluated. The experimental results show that both the prefetching mechanism and balancing strategy can effectively optimize archival performance of a replica-based storage cluster exhibiting a random data layout. In a (12,9) RS-coded archival scenario, P-DArch, B-DArch and PB-DArch outperform DArch by a factor of 2.95, 1.72 and 3.85, respectively.

In-memory store has become a key component for an increasing number of data-intensive applications like OLTP and OLAP. To be resilient to data loss incurred by transient failures, redundancy strategies are incorporated into in-memory stores. In-memory datasets are characterized by skewed popularity, because they exhibit varied access frequencies (a.k.a., number of accesses). Therefore, it is prudent to apply customized redundancy schemes with dynamic memory efficiency and access parallelisms to different in-memory datasets. In this work, we propose an adaptive redundancy scheme-PaRS-for in-memory datasets. PaRS relies on a re-stripe or replication mechanism to transform involved redundancy groups according to their workload popularity growth. With PaRS in place, a memory-efficient redundancy layout is deployed for data blocks with low access frequencies; a redundancy layout exhibiting high access parallelism is adopted for highly-accessed data blocks. Compared with existing redundancy schemes that employ simple replication or erasure coding, PaRS facilitates a configurable tradeoff between memory efficiency and access parallelism for in-memory data blocks. Quantitative evaluations using YCSB show that PaRS enables in-memory stores to exhibit higher access performance and memory efficiency than the replication scheme. Furthermore, PaRS achieves better load balancing than the erasure coding, while sustaining superb access performance and memory efficiency. In particular, under a double-fault-tolerant in-memory store of limited memory, PaRS improves access latency by 15.1 to 31.5 percent compared to 3-way replication, and PaRS enhances load balancing by more than 3.9× relative to Reed-Solomon coding.