arXiv subjects

Weixing Zhou

Publications and source records attributed to Weixing Zhou.

6 recordsLinked to original sources

arXiv2026

Full-gap kinetic limitation of thermionic-electron transport for electron transpiration cooling

Electron transpiration cooling (ETC) can reduce aerothermal loads on sharp hypersonic leading edges, but its performance is governed by whether thermionically emitted electrons escape the hot surface or return as cathode-directed backflow. Here, a one-dimensional-in-space, three-dimensional-in-velocity electrostatic particle-in-cell/Monte Carlo collision model is developed for a full cathode--anode plasma diode, resolving thermionic emission, collisional plasma transport, emitted-electron backflow, and downstream collection. A helium benchmark is used to examine emitted-electron transport and backflow-limited current flow. With increasing imposed emission, the diode first remains in a weak-backflow regime, where net emitted-electron transport and downstream collection both increase with emission. Further increasing the emission produces a sharp transition to backflow-limited transport between $7.0\times10^{19}$ and $7.5\times10^{19},\mathrm{m^{-2},s^{-1}}$. At $7.25\times10^{19},\mathrm{m^{-2},s^{-1}}$, the backflow ratio reaches $54.03%$, while the net transport and downstream collection efficiencies fall to about $46%$. Above this transition, added backflow overcompensates the imposed emission increase, reducing useful emitted-electron transport rather than causing saturation. Boundary energy diagnostics show that stronger emission may still increase the nominal cathode-side cooling metric, but after transition this metric no longer indicates improved emitted-electron escape or full-gap transport. These results show that the present PIC-MCC framework captures the key kinetic processes governing ETC-relevant emitted-electron escape and backflow limitation.

physics.plasm-ph↗

arXiv2026

ATCC: Adaptive Concurrency Control for Unforeseen Agentic Transactions

Data agents, empowered by Large Language Models (LLMs), introduce a new paradigm in transaction processing. Unlike traditional applications with fixed patterns, data agents run online-generated workflows that repeatedly issue SQL statements, reason over intermediate results, and revise subsequent plans. To ensure data consistency, these SQL statements issued by an agent should be integrated into a transaction, referred to as agentic transactions. Agentic transactions exhibit unforeseen characteristics, including long execution times, irregular execution intervals, and non-deterministic access patterns, breaking the assumptions underlying concurrency control (CC) (e.g., short-lived, predefined). Traditional CC schemes, which rely on fixed policies, fail to capture such dynamic behavior, resulting in inadequate performance. This paper introduces ATCC, an adaptive Concurrency Control for Agentic Transactions. ATCC continuously monitors and interprets the runtime behavior of each agentic transaction, evaluates its interactive phases, and dynamically adapts optimistic or pessimistic execution for each transaction. To ensure precise timing for adaptive switches, ATCC employs a reinforcement learning-based policy to balance immediate blocking against future abort costs. Additionally, to mitigate contention-induced tail latency and wasted reasoning cost caused by abort, a cost-aware priority-based lock scheduling is integrated to prioritize expensive or latency-sensitive transactions. Experimental results under agentic-like YCSB and TPC-C workloads demonstrate that ATCC improves the throughput of agentic transactions by up to four orders of magnitude and reduces tail latency by up to 90% compared to state-of-the-art CC schemes.

cs.DB↗

arXiv2026

Concurrency Control as a Service

Existing disaggregated databases separate execution and storage layers, enabling independent and elastic scaling of resources. In most cases, this design makes transaction concurrency control (CC) a critical bottleneck, which demands significant computing resources for concurrent conflict management and struggles to scale due to the coordination overhead for concurrent conflict resolution. Coupling CC with execution or storage limits performance and elasticity, as CC's resource needs do not align with the free scaling of the transaction execution layer or the storage-bound data layer. This paper proposes Concurrency Control as a Service (CCaaS), which decouples CC from databases, building an execution-CC-storage three-layer decoupled database, allowing independent scaling and upgrades for improved elasticity, resource utilization, and development agility. However, adding a new layer increases latency due to the shift in communication from hardware to network. To address this, we propose a Sharded Multi-Write OCC (SM-OCC) algorithm with an asynchronous log push-down mechanism to minimize network communications overhead and transaction latency. Additionally, we implement a multi-write architecture with a deterministic conflict resolution method to reduce coordination overhead in the CC layer, thereby improving scalability. CCaaS is designed to be connected by a variety of execution and storage engines. Existing disaggregated databases can be revolutionized with CCaaS to achieve high elasticity, scalability, and high performance. Results show that CCaaS achieves 1.02-3.11X higher throughput and 1.11-2.75X lower latency than SoTA disaggregated databases.

cs.DB↗

arXiv2025

Performant Synchronization in Geo-Distributed Databases

The deployment of databases across geographically distributed regions has become increasingly critical for ensuring data reliability and scalability. Recent studies indicate that distributed databases exhibit significantly higher latency than single-node databases, primarily due to consensus protocols maintaining data consistency across multiple nodes. We argue that synchronization cost constitutes the primary bottleneck for distributed databases, which is particularly pronounced in wide-area networks (WAN). Fortunately, we identify opportunities to optimize synchronization costs in real production environments: (1) network clustering phenomena, (2) triangle inequality violations in transmission, and (3) redundant data transfers. Based on these observations, we propose GeoCoCo, a synchronization acceleration framework for cross-region distributed databases. First, GeoCoCo presents a group rescheduling strategy that adapts to real-time network conditions to maximize WAN transmission efficiency. Second, GeoCoCo introduces a task-preserving data filtering method that reduces data volume transmitted over the WAN. Finally, GeoCoCo develops a consistency-guaranteed transmission framework integrating grouping and pruning. Extensive evaluations in both trace-driven simulations and real-world deployments demonstrate that GeoCoCo reduces synchronization cost-primarily by lowering WAN bandwidth usage-by up to 40.3%, and increases system throughput by up to 14.1% in GeoGauss.

cs.DB↗

arXiv2023

Towards Transaction as a Service

This paper argues for decoupling transaction processing from existing two-layer cloud-native databases and making transaction processing as an independent service. By building a transaction as a service (TaaS) layer, the transaction processing can be independently scaled for high resource utilization and can be independently upgraded for development agility. Accordingly, we architect an execution-transaction-storage three-layer cloud-native database. By connecting to TaaS, 1) the AP engines can be empowered with ACID TP capability, 2) multiple standalone TP engine instances can be incorporated to support multi-master distributed TP for horizontal scalability, 3) multiple execution engines with different data models can be integrated to support multi-model transactions, and 4) high performance TP is achieved through extensive TaaS optimizations and consistent evolution. Cloud-native databases deserve better architecture: we believe that TaaS provides a path forward to better cloud-native databases.

cs.DB↗

arXiv2023

GeoGauss: Strongly Consistent and Light-Coordinated OLTP for Geo-Replicated SQL Database

Multinational enterprises conduct global business that has a demand for geo-distributed transactional databases. Existing state-of-the-art databases adopt a sharded master-follower replication architecture. However, the single-master serving mode incurs massive cross-region writes from clients, and the sharded architecture requires multiple round-trip acknowledgments (e.g., 2PC) to ensure atomicity for cross-shard transactions. These limitations drive us to seek yet another design choice. In this paper, we propose a strongly consistent OLTP database GeoGauss with full replica multi-master architecture. To efficiently merge the updates from different master nodes, we propose a multi-master OCC that unifies data replication and concurrent transaction processing. By leveraging an epoch-based delta state merge rule and the optimistic asynchronous execution, GeoGauss ensures strong consistency with light-coordinated protocol and allows more concurrency with weak isolation, which are sufficient to meet our needs. Our geo-distributed experimental results show that GeoGauss achieves 7.06X higher throughput and 17.41X lower latency than the state-of-the-art geo-distributed database CockroachDB on the TPC-C benchmark.

cs.DB↗