High-contention Online Transaction Processing (OLTP) workloads pose a critical challenge for concurrency control protocols, particularly due to hotspots leading to excessive transaction aborts and deadlocks. Traditional approaches, including Two-Phase Locking (2PL) [1], struggle under such conditions, suffering from performance degradation due to wasted work from aborts and the harmful effects of transaction retrials. This proposal introduces B2PL, a novel deadlockfree 2PL protocol designed to tolerate high contention. B2PL leverages static analysis of transactions using a new graph-based structure, SLWGraph, to predetermine lock acquisitions and enforce an ordering that eliminates deadlocks within the 2PL framework. Furthermore, it enhances parallelism by enabling early lock release through a flexible mechanism called partial transaction chopping. By avoiding deadlocks and subsequent retries, B2PL aims to significantly improve throughput and latency in demanding OLTP environments. Our evaluations show promising results, indicating substantial performance gains over state-of-the-art 2PL protocols [2].

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.

Metastable failure is a recent abstraction of a pattern of failures that occurs frequently in real-world distributed storage systems. In this paper, we propose a formal analysis and modeling of metastable failures in replicated storage systems. We focus on a foundational problem in distributed systems—the problem of consensus—to have an impact on a large class of systems. Our main contribution is the development of a queuing-based analytical model, MSF-Model, that can be used to characterize and predict metastable failures. MSF-Model integrates novel modeling concepts that allow modeling metastable failures, which was intractable to model prior to our work. We also perform real experiments to reproduce and validate our model. Our real experiments show that MSF-Model predicts metastable failures with high accuracy by comparing the real experiment with the predictions from the queuing-based model.

Virtual Network Embedding (VNE) is an essential component of network virtualization technology. Prior works on VNE mainly focused on resource efficiency and did not address the scalability as a first-grade objective. Consequently, the ever-increasing demand and size render them less-practical. The few existing designs for mitigating this problem either do not extend to multi-resource settings or do not consider the physical servers and network simultaneously. In this work, we develop GraphViNE, a parallelizable VNE solution based on spatial Graph Neural Networks (GNN) that clusters the servers to guide the embedding process towards an improved runtime and performance. Our experiments using simulations show that the parallelism of GraphViNE reduces its runtime by a factor of 8. Also, GraphViNE improves the revenue-to-cost ratio by about 18%, compared to other simulated algorithms.