Distributed transaction systems can leverage memory disaggregation for efficient resource scaling, yet they experience significant performance degradation under high-contention workloads. We present CREST, a disaggregated transaction system that efficiently manages high-contention transaction workloads in disaggregated memory architectures via three key techniques: (i) cell-level concurrency control, which achieves more fine-grained transaction concurrency than existing record-level approaches and reduces remote access latencies using a metadata-aggregated record structure; (ii) localized execution, which allows compute nodes to operate on local uncommitted results to reduce blocking time; and (iii) parallel commits, which parallelize commit operations under transaction dependencies. Evaluation shows that CREST achieves a throughput gain of up to 1.92× over state-of-the-art systems under high-contention workloads.

Consensus protocols like Paxos and Raft provide data consistency and fault tolerance for distributed services. Log replication in these protocols can be supported by erasure coding, which incurs lower redundancy than full-copy replication and significantly saves network and storage costs for overall performance improvements. However, existing consensus protocols with erasure coding cannot achieve the minimum network and storage costs during log replication. We propose FlexRaft, which dynamically varies the coding scheme used in Raft based on the server status to always achieve the theoretically minimum redundancy ratio, while maintaining the same liveness as in Raft. To address the issue of an inconsistent coding scheme between the leader and its followers, we specify the prerequisite of overwriting a log entry and also allow the leader and its followers to exactly track the coding scheme being used. We further extend FlexRaft into FlexRaft+, which provides a different storage layout to vary the coding scheme through a novel technique called re-encoding-free replication, so as to enable fast server recovery. We prove that both FlexRaft and FlexRaft+ maintain Raft safety. We implement a prototype of FlexRaft and FlexRaft+, atop which we build a distributed key-value store to show its efficacy. Experiments on Alibaba Cloud show that FlexRaft achieves the theoretically minimum network and storage costs in practice, and reduces the commit latency by 44.51% and 19.37% compared with state-of-the-art CRaft and HRaft, respectively. FlexRaft+ further reduces the commit latency when the coding scheme is being varied and improves the server recovery performance.

Consensus protocols like Paxos and Raft provide data consistency and fault tolerance for upper-layer distributed services. Log replication in these protocols can be supported by erasure coding, which incurs a lower redundancy ratio than full-copy replication and hence significantly saves network and storage costs for overall performance improvements. However, existing consensus protocols with erasure coding cannot achieve the minimum network and storage costs during log replication. Our observation is that the optimal coding scheme varies with the number of healthy servers in a group, such that the coding scheme with the lowest redundancy ratio in normal cases incurs more network traffic and storage overhead for log replication in the presence of server failures. To this end, we propose FlexRaft, which dynamically adjusts the coding scheme used in Raft based on the server status to always achieve the theoretically minimum redundancy ratio, while maintaining the same liveness as in the original Raft. To address the issue of an inconsistent coding scheme between the leader and its followers, we specify the prerequisite of overwriting a log entry, and also allow the leader and its followers to exactly track the coding scheme used. We further consider how to handle server failures and prove the safety of FlexRaft. We implement a prototype of FlexRaft, atop which we build a distributed key-value store to show its efficacy. Experiments on Alibaba Cloud show that FlexRaft achieves the theoretically minimum network and storage costs in practice, and reduces the commit latency by 44.51% and 19.37% compared with state-of-the-art CRaft and HRaft, respectively.

RDMA (Remote Direct Memory Access) is widely studied in building key-value stores to achieve ultra-low latency. In RDMA-based key-value stores, the indexing time takes a large fraction of the overall operation latency as RDMA enables fast data access. However, the single index structure used in existing RDMA-based key-value stores, either hash-based or sorted index, fails to support range queries efficiently while achieving high performance for singlepoint operations. In this paper, we explore the adoption of a hybrid index in the key-value stores based on RDMA, especially under the memory disaggregation architecture, to combine the benefits of a hash table and a sorted index. We propose HStore, an RDMA-based key-value store that uses a hash table for single-point lookups and leverages a skiplist for range queries to index the values stored in the memory pool. Guided by previous work on using RDMA for key-value services, HStore dedicatedly chooses different RDMA verbs to optimize the read and write performance. To efficiently keep the index structures within a hybrid index consistent, HStore asynchronously applies the updates to the sorted index by shipping the update log via two-sided verbs. Compared to state-of-the-art Sherman and Clover, HStore improves the throughput by up to 54.5% and 38.5% respectively under the YCSB benchmark.

Stragglers (i.e., nodes with slow performance) are prevalent and incur performance instability in large-scale storage systems, yet it is challenging to detect stragglers in practice. We make a case by showing how erasure-coded caching provides robust straggler tolerance without relying on timely and accurate straggler detection, while incurring limited redundancy overhead in caching. We first analytically motivate that caching only parity blocks can achieve effective straggler tolerance. To this end, we present POCache, a parity-only caching design that provides robust straggler tolerance. To limit the erasure coding overhead, POCache slices blocks into smaller subblocks and parallelizes the coding operations at the subblock level. Also, it leverages a straggler-aware cache algorithm that takes into account both file access popularity and straggler estimation to decide which parity blocks should be cached. We implement a POCache prototype atop Hadoop 3.1 HDFS, while preserving the performance and functionalities of normal HDFS operations. Our extensive experiments on both local and Amazon EC2 clusters show that in the presence of stragglers, POCache can reduce the read latency by up to 87.9% compared to vanilla HDFS.

Modern data centers employ erasure coding to protect data storage against failures. Given the hierarchical nature of data centers, characterizing the effects of erasure coding and redundancy placement on the reliability of erasure-coded data centers is critical yet unexplored. This paper presents a discrete-event simulator called SimEDC, which enables us to conduct a comprehensive simulation analysis of reliability on erasure-coded data centers. SimEDC reports reliability metrics of an erasure-coded data center based on the configurable inputs of the data center topology, erasure codes, redundancy placement, and failure/repair patterns of different subsystems obtained from statistical models or production traces. It can further accelerate the simulation analysis via importance sampling. Our simulation analysis based on SimEDC shows that placing erasure-coded data in fewer racks generally improves reliability by reducing cross-rack repair traffic, even though it sacrifices rack-level fault tolerance in the face of correlated failures.

Erasure coding has been widely adopted to protect data storage against failures in production data centers. Given the hierarchical nature of data centers, characterizing the effects of erasure coding and redundancy placement on the reliability of erasure-coded data centers is critical yet largely unexplored. This paper presents a comprehensive simulation analysis of reliability on erasure-coded data centers. We conduct the analysis by building a discrete-event simulator called SIMEDC, which reports reliability metrics of an erasure-coded data center based on the configurable inputs of the data center topology, erasure codes, redundancy placement, and failure/repair patterns of different subsystems obtained from statistical models or production traces. Our simulation results show that placing erasure-coded data in fewer racks generally improves reliability by reducing cross-rack repair traffic, even though it sacrifices rack-level fault tolerance in the face of correlated failures.

Repair performance in hierarchical data centers is often bottlenecked by cross-rack network transfer. Recent theoretical results show that the cross-rack repair traffic can be minimized through repair layering, whose idea is to partition a repair operation into inner-rack and cross-rack layers. However, how repair layering should be implemented and deployed in practice remains an open issue. In this article, we address this issue by proposing a practical repair layering framework called DoubleR . We design two families of practical double regenerating codes (DRC), which not only minimize the cross-rack repair traffic but also have several practical properties that improve state-of-the-art regenerating codes. We implement and deploy DoubleR atop the Hadoop Distributed File System (HDFS) and show that DoubleR maintains the theoretical guarantees of DRC and improves the repair performance of regenerating codes in both node recovery and degraded read operations.