arXiv subjects

Jinxin Yang

Publications and source records attributed to Jinxin Yang.

4 recordsLinked to original sources

arXiv2026

Status Updating in Two-Way Delay Systems with Preemption

We consider a status update system consisting of a sampler, a sink, and a controller located at the sink. The controller sends requests to the sampler to generate and transmit status updates. Packet transmissions from the controller to the sampler (reverse link) and from the sampler to the sink (forward link) experience random delays. The reverse and forward links are modeled as servers with geometric service times, referred to as the controller and sampler servers, respectively. Each server is equipped with a single buffer that stores an arriving packet when the server is busy. We adopt a preemption-in-waiting policy on both links, whereby an arriving packet replaces the packet in the buffer whenever the buffer is full. Our main goal is to determine the optimal generation times of request packets at the controller in order to minimize the long-term average age of information (AoI) at the sink. We formulate the problem as a Markov decision process (MDP) and derive the optimal stationary deterministic policy using the relative value iteration (RVI) algorithm. We prove the convergence of the algorithm. Numerical results show that the proposed system consistently outperforms baseline policies from prior work and reveal a threshold-based structure for the optimal policy.

cs.IT↗

arXiv2026

Bala-Join: An Adaptive Hash Join for Balancing Communication and Computation in Geo-Distributed SQL Databases

Shared-nothing geo-distributed SQL databases, such as CockroachDB, are increasingly vital for enterprise applications requiring data resilience and locality. However, we encountered significant performance degradation at the customer side, especially when their deployments span multiple data centers over a Wide Area Network (WAN). Our investigation identifies the bottleneck in the performance of the Distributed Hash Join (Dist-HJ) algorithm, which is contingent upon a crucial balance between communication overhead and computational load. This balance is severely disrupted when processing skewed data from real-world customer workloads, leading to the observed performance decline. To tackle this challenge, we introduce Bala-Join, an adaptive solution to balance the computation and network load in Dist-HJ execution. Our approach consists of the Balanced Partition and Partial Replication (BPPR) algorithm and a distributed online skewed join key detector. The former achieves balanced redistribution of skewed data through a multicast mechanism to improve computational performance and reduce network overhead. The latter provides real-time skewed join key information tailored to BPPR. Furthermore, an Active-Signaling and Asynchronous-Pulling (ASAP) mechanism is incorporated to enable efficient, real-time synchronization between the detector and the redistribution process with minimal overhead. Empirical study shows that Bala-Join outperforms the popular Dist-HJ solutions, increasing throughput by 25%-61%.

cs.DB↗

arXiv2026

Anatomy of Agentic Memory: Taxonomy and Empirical Analysis of Evaluation and System Limitations

Agentic memory systems enable large language model (LLM) agents to maintain state across long interactions, supporting long-horizon reasoning and personalization beyond fixed context windows. Despite rapid architectural development, the empirical foundations of these systems remain fragile: existing benchmarks are often underscaled, evaluation metrics are misaligned with semantic utility, performance varies significantly across backbone models, and system-level costs are frequently overlooked. This survey presents a structured analysis of agentic memory from both architectural and system perspectives. We first introduce a concise taxonomy of MAG systems based on four memory structures. Then, we analyze key pain points limiting current systems, including benchmark saturation effects, metric validity and judge sensitivity, backbone-dependent accuracy, and the latency and throughput overhead introduced by memory maintenance. By connecting the memory structure to empirical limitations, this survey clarifies why current agentic memory systems often underperform their theoretical promise and outlines directions for more reliable evaluation and scalable system design.

cs.CL↗

arXiv2023

One Size Cannot Fit All: a Self-Adaptive Dispatcher for Skewed Hash Join in Shared-nothing RDBMSs

Shared-nothing architecture has been widely adopted in various commercial distributed RDBMSs. Thanks to the architecture, query can be processed in parallel and accelerated by scaling up the cluster horizontally on demand. In spite of that, load balancing has been a challenging issue in all distributed RDBMSs, including shared-nothing ones, which suffers much from skewed data distribution. In this work, we focus on one of the representative operator, namely Hash Join, and investigate how skewness among the nodes of a cluster will affect the load balance and eventual efficiency of an arbitrary query in shared-nothing RDBMSs. We found that existing Distributed Hash Join (Dist-HJ) solutions may not provide satisfactory performance when a value is skewed in both the probe and build tables. To address that, we propose a novel Dist-HJ solution, namely Partition and Replication (PnR). Although PnR provide the best efficiency in some skewness scenario, our exhaustive experiments over a group of shared-nothing RDBMSs show that there is not a single Dist-HJ solution that wins in all (data skew) scenarios. To this end, we further propose a self-adaptive Dist-HJ solution with a builtin sub-operator cost model that dynamically select the best Dist-HJ implementation strategy at runtime according to the data skew of the target query. We implement the solution in our commercial shared-nothing RDBMSs, namely KaiwuDB (former name ZNBase) and empirical study justifies that the self-adaptive model achieves the best performance comparing to a series of solution adopted in many existing RDBMSs.

cs.DB↗