arXiv subjects

Juncheng Fang

Publications and source records attributed to Juncheng Fang.

7 recordsLinked to original sources

arXiv2025

Brook-2PL: Tolerating High Contention Workloads with A Deadlock-Free Two-Phase Locking Protocol

The problem of hotspots remains a critical challenge in high-contention workloads for concurrency control (CC) protocols. Traditional concurrency control approaches encounter significant difficulties under high contention, resulting in excessive transaction aborts and deadlocks. In this paper, we propose Brook-2PL, a novel two-phase locking (2PL) protocol that (1) introduces SLW-Graph for deadlock-free transaction execution, and (2) proposes partial transaction chopping for early lock release. Previous methods suffer from transaction aborts that lead to wasted work and can further burden the system due to their cascading effects. Brook-2PL addresses this limitation by statically analyzing a new graph-based dependency structure called SLW-Graph, enabling deadlock-free two-phase locking through predetermined lock acquisition. Brook-2PL also reduces contention by enabling early lock release using partial transaction chopping and static transaction analysis. We overcome the inherent limitations of traditional transaction chopping by providing a more flexible chopping method. Evaluation using both our synthetic online game store workload and the TPC-C benchmark shows that Brook-2PL significantly outperforms state-of-the-art CC protocols. Brook-2PL achieves an average speed-up of 2.86x while reducing tail latency (p95) by 48% in the TPC-C benchmark.

cs.DB↗

arXiv2025

Physically interpretable diffractive optical networks for high-dimensional vortex mode sorting

Despite the significant progress achieved by diffractive optical networks in diverse computing tasks, such as mode multiplexing and demultiplexing, investigations into the physical meanings behind complex diffractive networks at the layer level have been quite limited. Here, for highdimensional vortex mode sorting tasks, we show how various physical transformation rules for each layer within trained diffractive networks can be revealed under properly defined input/output mode relations. An intriguing physical transformation division phenomenon, associated with the saturated sorting performance of the system, has been observed with an increasing number of masks. In addition, we have also demonstrated the use of physical interpretation for efficiently designing parameter-varying networks with high performance. These physically interpretable optical networks resolve the contradiction between rigorous physical theorems and operationally vague network structures, paving the way for designing and understanding systems for various mode conversion tasks, and inspiring further interpretation of diffractive networks in advanced tasks and other network structures.

physics.optics↗

arXiv2025

DeViNE: A Decentralized Virtual Network Embedding Algorithm

Virtual Network Embedding (VNE) is a technique for mapping virtual networks onto a physical network infrastructure, enabling multiple virtual networks to coexist on a shared physical network. Previous works focused on implementing centralized VNE algorithms, which suffer from lack of scalability and robustness. This project aims to implement a decentralized virtual network embedding algorithm that addresses the challenges of network virtualization, such as scalability, single point of failure, and DoS attacks. The proposed approach involves selecting L leaders from the physical nodes and embedding a virtual network request (VNR) in the local network of each leader using a simple algorithm like BFS. The algorithm then uses a leader-election mechanism for determining the node with the lowest cost and highest revenue and propagates the embedding to other leaders. By utilizing decentralization, we improve the scalability and robustness of the solution. Additionally, we evaluate the effectiveness of our fully decentralized algorithm by comparing it with existing approaches. Our algorithm performs $12\%$ better in terms of acceptance rate and improves the revenue-to-cost ratio by roughly $21\%$ to compared approaches.

cs.NI↗

arXiv2024

PoneglyphDB: Efficient Non-interactive Zero-Knowledge Proofs for Arbitrary SQL-Query Verification

In database applications involving sensitive data, the dual imperatives of data confidentiality and provable query processing are important. This paper introduces PoneglyphDB, a database system that leverages non-interactive zero-knowledge proofs (ZKP) to support both confidentiality and provability. Unlike traditional databases, PoneglyphDB enhances confidentiality by ensuring that raw data remains exclusively with the host, while also enabling verification of the correctness of query responses by providing proofs to clients. The main innovation in this paper is proposing efficient ZKP designs (called circuits) for basic operations in SQL query processing. These basic operation circuits are then combined to form ZKP circuits for larger, more complex queries. PoneglyphDB's circuits are carefully designed to be efficient by utilizing advances in cryptography such as PLONKish-based circuits, recursive proof composition techniques, and designs with low-order polynomial constraints. We demonstrate the performance of PoneglyphDB with the standard TPC-H benchmark. Our experimental results show that PoneglyphDB can efficiently achieve both confidentiality and provability, outperforming existing state-of-the-art ZKP methods.

cs.DB↗

arXiv2024

Beyond the Known: Novel Class Discovery for Open-world Graph Learning

Node classification on graphs is of great importance in many applications. Due to the limited labeling capability and evolution in real-world open scenarios, novel classes can emerge on unlabeled testing nodes. However, little attention has been paid to novel class discovery on graphs. Discovering novel classes is challenging as novel and known class nodes are correlated by edges, which makes their representations indistinguishable when applying message passing GNNs. Furthermore, the novel classes lack labeling information to guide the learning process. In this paper, we propose a novel method Open-world gRAph neuraL network (ORAL) to tackle these challenges. ORAL first detects correlations between classes through semi-supervised prototypical learning. Inter-class correlations are subsequently eliminated by the prototypical attention network, leading to distinctive representations for different classes. Furthermore, to fully explore multi-scale graph features for alleviating label deficiencies, ORAL generates pseudo-labels by aligning and ensembling label estimations from multiple stacked prototypical attention networks. Extensive experiments on several benchmark datasets show the effectiveness of our proposed method.

cs.LG↗

arXiv2021

G-Tran: Making Distributed Graph Transactions Fast

Graph transaction processing raises many unique challenges such as random data access due to the irregularity of graph structures, low throughput and high abort rate due to the relatively large read/write sets in graph transactions. To address these challenges, we present G-Tran -- an RDMA-enabled distributed in-memory graph database with serializable and snapshot isolation support. First, we propose a graph-native data store to achieve good data locality and fast data access for transactional updates and queries. Second, G-Tran adopts a fully decentralized architecture that leverages RDMA to process distributed transactions with the MPP model, which can achieve high performance by utilizing all computing resources. In addition, we propose a new MV-OCC implementation with two optimizations to address the issue of large read/write sets in graph transactions. Extensive experiments show that G-Tran achieves competitive performance compared with other popular graph databases on benchmark workloads.

cs.DB↗

arXiv2020

Multiple-image encryption and hiding with an optical diffractive neural network

A cascaded phase-only mask architecture (or an optical diffractive neural network) can be employed for different optical information processing tasks such as pattern recognition, orbital angular momentum (OAM) mode conversion, image salience detection and image encryption. However, for optical encryption and watermarking applications, such a system usually cannot process multiple pairs of input images and output images simultaneously. In our proposed scheme, multiple input images can be simultaneously fed to an optical diffractive neural network (DNN) system and each corresponding output image will be displayed in a non-overlap sub-region in the output imaging plane. Each input image undergoes a different optical transform in an independent channel within the same system. The multiple cascaded phase masks in the system can be effectively optimized by a wavefront matching algorithm. Similar to recent optical pattern recognition and mode conversion works, the orthogonality property is employed to design a multiplexed DNN.

cs.CV↗