Efficient lock management is critical in distributed systems with shared resources, especially in the context of enhanced computational power and reduced processing time. While numerous studies have leveraged Remote Direct Memory Access (RDMA) for distributed locking, these network-based approaches suffer from high network latency and jitter. The emergence of the Compute Express Link (CXL) protocol provides high-speed, low-latency connections between host processors and memory devices, presenting a compelling alternative for the lock system design. This paper introduces CXLock, the industry’s first distributed lock management system based on CXL 2.0 switches. CXLock decouples lock management into client-side queuing and manager-side arbitration, resolving race conditions of multiple clients without using remote atomics. To handle the lack of hardware-enforced coherency in CXL 2.0, CXLock employs a software-based coherency model that enables memory sharing across multiple hosts with cacheable and write-back attributes. CXLock is implemented and evaluated on a real rack-scale hardware platform containing a CXL switch and multiple host nodes. The results show that CXLock delivers an average throughput improvement of 3.7× over the RDMA-based solutions. Moreover, CXLock reduces the lock grant latency by up to 76% in both contention-free and contended scenarios.

Emerging memory-semantic fabrics such as CXL and UB enable direct load/store access to remote memory at byte granularity, opening new opportunities for cluster-wide memory pooling and sharing. However, building a high-performance, fault-tolerant memory pool atop such fabrics remains challenging. Systems must coordinate application transparency with heterogeneous memory topologies, ensure safe memory reuse across machines, and handle instruction-level memory failures that manifest as hardware exceptions in user code. We present Oak, a resilient, high-performance memory pool service that enables transparent and efficient memory pooling and sharing across machines via memory-semantic interconnects. Oak decouples memory metadata from control logic via a stateless global memory manager, which is backed by a distributed KV store enabling scalable, fault-tolerant orchestration. To tolerate memory faults ranging from device loss to single-page uncorrectable errors, Oak provides a lightweight kernel-user cooperative recovery mechanism that intercepts memory failures in the kernel, performs microsecond-scale recovery, and defers metadata updates to user-space asynchronously. We demonstrate Oak's practicality by building Oak-KV, a fault-tolerant, zero-copy key-value store that runs entirely on Oak-managed shared memory. Evaluations show that Oak-KV delivers higher throughput than representative baselines under both normal and failure conditions.

GPU Direct Storage (GDS) plays a vital role in GPU-based training and inference systems, leveraging Peer-to-Peer Direct Memory Access (P2P-DMA) to establish a direct data transfer path between the GPU and the storage device. The direct I/O path reduces GPU storage access latency and CPU overhead, thus improving the efficiency of data transfer. Currently, however, GDS employs a phony buffer in the host memory to interact with the Linux kernel, which results in suboptimal I/O performance, extra resource consumption, and high deployment complexity.

In the past decade, cloud databases have experienced rapid development and growth. PolarDB, Alibaba's cloud-native OLTP database, has evolved significantly to meet the increasing demand for cloud-native architectures and now serves hundreds of thousands of customers across various industries. This paper presents PolarDB's evolution over the past eight years, with a focus on scalability, performance, and cost-efficiency. Initially, PolarDB adopted a primary-replica architecture based on disaggregated storage, with an emphasis on enhancing single-node performance for scale-up in modern many-core systems. To achieve this, we co-designed PolarDB with cutting-edge hardware, including RDMA, to improve performance. Meanwhile, we refined the internal architecture, including improvements to B+ tree concurrency control and transaction management, ensuring high scalability in scale-up scenarios. More recently, our focus has shifted to scaling out PolarDB to meet the performance and scalability needs of ultra-large-scale applications. By leveraging RDMA, we optimized distributed transaction processing, transforming PolarDB into a high-performance, high-scalability and cost-effective distributed database. In the TPC-C benchmark, PolarDB scaled out to 2340 nodes and achieved over 2 billion tpmC, with a jitter rate of no more than 0.16% during the 8-hour stress test. Compared to the second- and third-highest-performing databases in public TPC-C results, PolarDB's tpmC is 2.52× and 2.91× higher, respectively. In terms of cost-effectiveness, PolarDB's per-tpmC cost is 37% and 79.5% lower than that of the other two systems, respectively.

The serverless database has recently attracted increasing attention both in industry and academia due to its high elasticity and the “pay-as-you-go” model. This paper delivers a thorough review of current shared-storage-based commercial serverless databases, pinpointing two major challenges: (1) they either experience difficulties with instance migration during scaling up or restrict the resource usage within a single physical host to avoid potential migration. (2) they lack the ability to scale out secondary nodes due to the absence of strong consistency support in secondary nodes. Based on our experience in building serverless databases, this paper proposes two fundamental requirements to address these two issues: seamless and instant migration and read scale-out. The former allows for instance migration when there are insufficient resources on the resident host during scaling up without application disruption, whereas the latter necessitates strong consistency on secondary nodes to process read requests. To fulfill these fundamental requirements, we propose PolarDB Serverless, a shared-storage-based serverless database achieving seamless scale-up and read scale-out. It supports read scale-out by inheriting the strong consistency feature from PolarDB, making it possible to process strongly consistent reads on secondary nodes. In the pursuit of achieving seamless migration, PolarDB Serverless introduces a transaction migration policy. It ensures there is no interruption to the application during migrations, allowing transactions to continue on the new instance without any disruptions. It also minimizes the overhead of migration, achieving a fast migration. In our evaluation, especially in the context of database migration scenarios, it's noteworthy that the migration of a database instance takes just half a second without causing any exceptions for applications. PolarDB Serverless is the first shared-storage-based serverless database supporting both seamless scale-up and read scale-out and is already commercially available at Alibaba Cloud.

Transaction performance in geo-replicated databases heavily relies on the request location: when not issued by the primary region, transactions are forced to involve costly wide-area communication. While existing systems distribute primary roles across regions, such assignment typically occurs at the shard level, making it difficult to align with geographically dispersed access to individual records. This paper introduces PolyBase, a pioneering architecture to address such misalignment, leveraging the widely adopted Paxos-based log replication mechanisms. It enables flexible row-level consensus group affiliation , which runs on an unchanged Paxos protocol , but dynamically re-assigns database rows between Paxos log replication groups, whose leaders become the primary region, enjoying faster writes and up-to-date versions for reads. With carefully designed data structures and protocols, PolyBase significantly reduces wide-area RTTs without compromising transaction or log replication consistency or reliability guarantees. We implemented PolyBase with optimized re-assignment policies and integrated it into two popular databases (RocksDB and MySQL). Our evaluation on AWS, using a production e-commerce workload and microbench-marks confirms that PolyBase offers significantly higher transaction throughput and lower average/tail latency compared to baselines.

The recent emergence of commodity persistent memory (PM) hardware has altered the landscape of the storage hierarchy. It brings multi-fold benefits to database systems, with its large capacity, low latency, byte addressability, and persistence. However, PM has not been incorporated into the popular disaggregated architecture of cloud-native databases.

A classic design of cloud-native databases adopts an architecture that consists of one read/write (RW) node and one or more read-only (RO) nodes. In such a design, the propagation of write-ahead logs (WALs) from the RW node to the RO node(s) is typically performed asynchronously. Consequently, system designers either have to accept a loose consistency guarantee, where a read from the RO node may return stale data, or tolerate significant performance degradation in terms of read latency, as it then needs to wait for the log to be propagated and applied. Most commercial cloud-native databases, such as Amazon Aurora, choose performance over strong consistency. As a result, it makes RO nodes useless for many applications requiring read-after-write consistency (a form of strong consistency), and the support for serverless databases (i.e., allowing the RO nodes to be scaled out automatically) is impossible as they require a single endpoint. This paper proposes PolarDB-SCC (PolarDB-Strongly Consistent Cluster), a cloud-native database architecture that guarantees strongly consistent reads with very low latency. The core idea is to eliminate unnecessary waits and reduce the necessary wait time on RO nodes while still supporting strong consistency. To achieve this, it tracks the RW node's modification timestamp at three progressively finer-grained levels. We further design a Linear Lamport timestamp to reduce the RO node's timestamp fetching operations and leverage the RDMA network for all the data transferring ( e.g. , timestamp fetching and log shipment) to minimize network overhead and extra CPU usage. Our evaluation shows that PolarDB-SCC does not incur any noticeable overhead for ensuring strongly consistent reads compared with the eventually consistent (stale) read policy. To the best of our knowledge, PolarDB-SCC is the first "read-write splitting" cloud-native database that supports strongly consistent read with negligible overhead. Compared with a straightforward read-wait design, PolarDB-SCC improves throughput by up to 4.51× and reduces median latency by up to 3.66× in SysBench's read-write workload. PolarDB-SCC is already commercially available at Alibaba Cloud.

In modern solid-state drives (SSDs), a flash translation layer (FTL) is the core engine performing the logical-to-physical address translations. However, the increasing capacity of SSDs requires a large DRAM space to hold the mapping table in a page-level FTL. Due to the limited size of built-in DRAM, existing FTL schemes selectively cache some active mapping entries in DRAM, while store the entire mapping table on flash. However, the low cache hit ratio introduced by the traditional mapping entry eviction policy degrades the access performance of SSDs. In this article, we propose a novel FTL, hot-clusterity FTL (HCFTL), to improve the hit ratio of cached mapping table (CMT) of SSDs with limited cache space. HCFTL clusters mapping entries, which are newly evicted from the cache, into dynamic translation pages (DTPs). Reading entries from DTPs will increase the CMT hit ratio due to spatial and temporal localities. Because the logical page numbers (LPNs) in a DTP are not consecutive, we introduce two different efficient index structures to speedup the lookup of mapping entries in DTPs. Furthermore, to make the DTP index highly accurate, we additionally allocate an auxiliary cache to buffer the mapping entries newly evicted from CMT, and produce dynamic pages with minimum difference between the maximal and the minimal LPNs of entries in those pages. Our experiments show that HCFTL can improve the CMT hit ratio by up to 41.1% and decrease the system response time by up to 33.3%, compared to baseline FTL schemes.

Summary Cache management policy plays a key role in offering low latency access to flash‐based SSDs. Most existing solutions including LRU and its successors only focus on improving the cache hit ratio, but rarely consider to reduce the waiting time of the eviction operation in the page‐level mapping FTLs. As the workloads spreading across internal chips of modern flash‐based SSDs are often highly imbalanced when workloads are write‐intensive, the time cost of evicting a dirty page from cache varies in a wide range. In this paper, we propose a novel eviction‐cost‐aware cache management policy, called ECR, to minimize the eviction cost in write‐dominant applications. ECR gives a higher probability to evict a page, which causes the shortest waiting time in the corresponding chip queue. To achieve this, we introduce a monitor module to keep track of states of all chip queues, and a multi‐LRU list structure to accelerate the selection of a victim chip and a target page in cache to perform an eviction. Our experimental results show that ECR can significantly reduce the average response time by as much as 59.55% and 44.84% compared to LRU and GCaR‐CFLRU, respectively, where GCaR‐CFLRU is the combination of state‐of‐the‐art algorithm GCaR and CFLRU.

Key-value (KV) stores support many crucial applications and services. They perform fast in-memory processing but are still often limited by I/O performance. The recent emergence of high-speed commodity non-volatile memory express solid-state drives (NVMe SSDs) has propelled new KV system designs that take advantage of their ultra-low latency and high bandwidth. Meanwhile, to switch to entirely new data layouts and scale up entire databases to high-end SSDs requires considerable investment. As a compromise, we propose SpanDB, an LSM-tree-based KV store that adapts the popular RocksDB system to utilize selective deployment of high-speed SSDs . SpanDB allows users to host the bulk of their data on cheaper and larger SSDs (and even hard disc drives with certain workloads), while relocating write-ahead logs (WAL) and the top levels of the LSM-tree to a much smaller and faster NVMe SSD. To better utilize this fast disk, SpanDB provides high-speed, parallel WAL writes via SPDK, and enables asynchronous request processing to mitigate inter-thread synchronization overhead and work efficiently with polling-based I/O. To ease the live data migration between fast and slow disks, we introduce TopFS, a stripped-down file system providing familiar file interface wrappers on top of SPDK I/O. Our evaluation shows that SpanDB simultaneously improves RocksDB's throughput by up to 8.8 \times and reduces its latency by 9.5–58.3%. Compared with KVell, a system designed for high-end SSDs, SpanDB achieves 96–140% of its throughput, with a 2.3–21.6 \times lower latency, at a cheaper storage configuration.

Traffic simulators are important tools for tasks such as urban planning and transportation management. Microscopic simulators allow per-vehicle movement simulation, but require longer simulation time. The simulation overhead is exacerbated when there is traffic congestion and most vehicles move slowly. This in particular hurts the productivity of emerging urban computing studies based on reinforcement learning, where traffic simulations are heavily and repeatedly used for designing policies to optimize traffic related tasks.

The increasing capacity of SSDs requires a large amount of built-in DRAM to hold the mapping information of logical-to-physical address translation. Due to the limited size of DRAM, existing FTL schemes selectively keep some active mapping entries in a Cached Mapping Table (CMT) in DRAM, while storing the entire mapping table on flash. To improve the CMT hit ratio with limited cache space on SSDs, in this paper, we propose a novel FTL, a hot-clusterity FTL (HCFTL) that clusters mapping entries recently evicted from the cache into dynamic translation pages (DTPs). Given the temporal localities that those hot entries are likely to be visited in near future, loading DTPs will increase the CMT hit ratio and thus improve the FTL performance. Furthermore, we introduce an index structure to speedup the lookup of mapping entries in DTPs. Our experiments show that HCFTL can improve the CMT hit ratio by up to 41.1% and decrease the system response time by up to 33.3%, compared to state-of-the-art FTL schemes.

Flash-based SSDs are usually equipped with an onboard cache to further improve system performance by smoothing the gap between the upper-level applications and lower-level flash chips. Since modern SSDs are usually composed of multiple flash chips, and the load of flash chips are significantly different, it is very meaningful to be aware of the chip load condition when designing a cache replacement algorithm. Nevertheless, existing cache replacement algorithms only consider to reduce the cache miss ratio so as to reduce the I/O requests to the underlying flash memory as much as possible, none of them considers the load condition of flash chips. In this paper, we propose a Load- aware Cache Replacement algorithm, called LCR, to improve the performance of flash-based SSDs. The basic idea is to give a higher priority to cache the blocks on overloaded flash chips. We evaluate the performance of our scheme by using a trace- driven simulator with multiple real-world workloads, and results show that compared with the most common algorithm LRU and the state-of-the-art algorithm GCaR, LCR reduces the average response time by as much as 39.2% and 12.3%, respectively.