Erasure codes(EC) have been widely adopted to provide high data reliability with low storage costs in clusters. Due to the deletion and out-of-place update operations, some data blocks are invalid, which unfortunately arouses the tediousgarbage collection(GC) problem. Several limitations still plague existing designs: substantial network traffic, unbalanced traffic load, and low read/write performance after GC. This paper proposes FastGC, a fast garbage collection method that merges the old stripes into a new stripe and reclaims invalid blocks. FastGC quickly generates an efficient merge solution by stripe grouping and bit sequences operations to minimize network traffic and maintains data block distributions of the same stripe to ensure read performance. It carefully allocates the storage space for new stripes during merging to eliminate the discontinuous free spaces that affect write performance. Furthermore, to accelerate the parity updates after merging, FastGC greedily schedules the transmission links for multi-stripe updates to balance the traffic load across nodes and adopts a maximum flow algorithm to saturate the bandwidth utilization. Comprehensive evaluation results show via simulations and Alibaba ECS experiments that FastGC can significantly reduce 10.36%-81.22% of the network traffic and 34.25%-72.36% of the GC time while maintaining read/write performance after GC.

A significant drawback of erasure-coding is suffering from the expensive update traffic. The analysis of real-world-production traces shows that partial updates, including partial-block-updates and partial-stripe-updates, are both common. Existing schemes cannot work adequately for partial updates. Raid-based scheme coordinates multiple updated entire blocks to update parity, yet it incurs significant network traffic for partial-block-updates. Delta-based scheme transmits the updated parts and independently updates parity, yet it cannot share computed-delta parts for partial-stripe-updates. We propose CoRD, which optimally combines Raid-based and Delta-based schemes to minimize the update traffic. It exploits the offset address intersections between multiple updated blocks and only transmits the updated parts to coordinate in parity updates. CoRD further address cross-block update scenarios by flipping some dedicated blocks to improve the performance. Comprehensive evaluations verify the effectiveness of CoRD for the latest traces, with the update traffic reduction of 37.02%-87.19% and the performance improvement of 36.54%-231.92% compared to state-of-the-art.

Modern clustered storage systems have commonly used erasure coding to maintain data durability against failures, yet it introduces significant update overhead for partial updates (e.g., only part of a block is updated). Recent studies propose the append-commit and buffer-logging techniques, which append multiple updated data to buffer-log and cache the corresponding old data from disk into memory, to reduce the update costs when committing the updates to parity. However, caching the entire old data block will introduce additional disk reads and memory overhead because caching the old part that not be updated, caching the partial old data block will incur the significant disk seeks for frequent partial updates. Our real-cloud experiments show that the unbalanced disk I/O may cause the bottleneck for updating, which is unfortunately overlooked by existing studies.This paper proposes LASC, a locality-aware speculative cache scheme for partial updates. LASC perceives the update locality of a data block from an update request stream within a period and speculatively caches the old data from the disk. It caches the entire old data block with high update locality to reduce the disk seeks. For a series of update requests to the same data block, LASC only performs one disk seek. Otherwise, it caches the old partial data to migrate the disk reads and memory overhead. We evaluate LASC via trace-driven simulations and Alibaba ECS experiments for two of the largest and latest public block-level I/O traces and show that LASC can effectively balance the disk I/O and improve the update performance while keeping memory overhead low.