Efficient container image distribution is crucial for enabling machine learning inference at the network edge, where resource limitations and dynamic network conditions create significant challenges. In this paper, we presentPeerSync, a decentralized P2P-based system designed to optimize image distribution in edge environments.PeerSyncemploys a popularity- and network-aware download engine that dynamically adapts to content popularity and real-time network conditions.PeerSyncfurther integrates automated tracker election for rapid peer discovery and dynamic cache management for efficient storage utilization. We implementPeerSyncwith 8000+ lines of Rust code and test its performance extensively on both large-scale Docker-based emulations and physical edge devices. Experimental results show thatPeerSyncdelivers a remarkable speed increase of 2.72×, 1.79×, and 1.28× compared to the Baseline solution, Dragonfly, and Kraken, respectively, while significantly reducing cross-network traffic by 90.72% under congested and varying network conditions.

The online caching problem aims to minimize cache misses when serving a sequence of requests under a limited cache size. While naive learning-augmented caching algorithms achieve ideal $1$-consistency, they lack robustness guarantees. Existing robustification methods either sacrifice $1$-consistency or introduce excessive computational overhead. In this paper, we introduce Guard, a lightweight robustification framework that enhances the robustness of a broad class of learning-augmented caching algorithms to $2H_{k-1} + 2$, while preserving their $1$-consistency. Guard achieves the current best-known trade-off between consistency and robustness, with only $\mathcal{O}(1)$ additional per-request overhead, thereby maintaining the original time complexity of the base algorithm. Extensive experiments across multiple real-world datasets and prediction models validate the effectiveness of Guard in practice.

This paper addresses the data-locality-aware task assignment and scheduling problem for distributed job executions. Our goal is to minimize job completion times without prior knowledge of future job arrivals. We propose an Optimal Balanced Task Assignment algorithm (OBTA), which achieves minimal job completion times while significantly reducing computational overhead through efficient narrowing of the solution search space. To balance performance and efficiency, we extend the approximate Water-Filling (WF) algorithm, providing a rigorous proof that its approximation factor equals the number of task groups in a job. We also introduce a novel heuristic, Replica-Deletion (RD), which outperforms WF by leveraging global optimization techniques. To further enhance scheduling efficiency, we incorporate job ordering strategies based on a shortest-estimated-time-first policy, reducing average job completion times across workloads. Extensive trace-driven evaluations validate the effectiveness and scalability of the proposed algorithms.

Computing ecosystems are shifting toward a computing continuum paradigm designed to handle the diverse and dynamic nature of computing resources spread across various locations. It demonstrates significant potential in providing high-bandwidth and low-latency services for users. However, as a large number of users request services from distributed computing continuum systems, it is critical to schedule numerous delay-sensitive, fractional workloads and maximum parallelism-bound jobs to appropriate backend resources,e.g., cloud container instances. In addition, the scheduling strategy also needs to maximize the social welfare that incorporates the utilities of jobs and the revenue of service providers. However, current workload scheduling algorithms are based on simple heuristics and lack performance guarantees. Due to the unpredictability of online requests, the distribution of requests should not be assumed. Therefore, designing an online workload scheduling strategy without assumptions on request distributions is essential for balancing the online workload. This work first establishes a spatiotemporal integrated resource pool to reflect the computational resources provided by distributed computing continuum systems. Then, several pseudo-social welfare functions and marginal cost functions are constructed, where the latter is used to estimate the marginal cost of provisioning services to each newly arrived job based on the current resource surplus. We propose an online workload scheduling strategy namedOnSocMaxto solve the above problems. It operates by following the solutions to several convex pseudo-social welfare maximization problems and is proven to be$\alpha$-competitive for some$\alpha$with a value of at least 2. The evaluation results demonstrate thatOnSocMaxoutperforms several benchmark strategies in maximizing social welfare.

In this paper, we study learning-augmented algorithms for the Bahncard problem. The Bahncard problem is a generalization of the ski-rental problem, where a traveler needs to irrevocably and repeatedly decide between a cheap short-term solution and an expensive long-term one with an unknown future. Even though the problem is canonical, only a primal-dual-based learning-augmented algorithm was explicitly designed for it. We develop a new learning-augmented algorithm, named PFSUM, that incorporates both history and short-term future to improve online decision making. We derive the competitive ratio of PFSUM as a function of the prediction error and conduct extensive experiments to show that PFSUM outperforms the primal-dual-based algorithm.

When using the belief rule base (BRB) methodology to deal with the nonlinear causal inference problems, combinatorial explosion often occurs due to overnumbered antecedent attributes, resulting in poor performance. Therefore, this paper proposes a novel nonlinear causal inference approach based on vector-based BRB. In the modeling process of BRB, the original attributes are ranked by contribution rate and transformed into attribute vectors. Meanwhile, combined with the k-means method, appropriate referential vectors are obtained. Thereby a vector-based BRB can be established. In the inference process of BRB, the idea of full activation of vector-based rules is presented. By calculating the spatial matching degree of the testing sample and the referential vectors, activation weights of the rules which are used in the evidential reasoning algorithm are acquired. Experimental results of a nonlinear function with four-dimensional input and the pipeline leakage detection data show the effectiveness and superiority of the proposed approach.