The convergence of networking and computing has become a critical architectural paradigm due to the increasing demand for services offering high availability and ultra-low latency, including immersive video and wide-area high-performance computing. However, increasing heterogeneity in computing resources and tighter coupling with algorithms have exacerbated the mismatch between resource supply and service demands. The imperative necessity for service-aware and resource-adaptive routing solutions is underscored by the fact that traditional location-based routing mechanisms neglect service semantics. Against this backdrop, the Computing Power Network (CPN) has emerged, integrating network and computing to enable dynamic, SLA-driven task orchestration. Although substantial progress has been achieved in task scheduling and orchestration in the existing work, the critical issue of service-level routing has not been completely addressed. To bridge this gap, we propose OSAF, a lightweight and backward-compatible service routing mechanism for CPN. OSAF introduced Service Tag (STag) as location-independent semantic identifier, which decouple service requests from host addresses. This enables users to access integrated services without being aware of resource locations. Our design of Service-Aware Routing (SAR) is based on OSAF. This routing algorithm utilizes reinforcement learning and multi-layer graph attention to model fine-grained resource dependencies and adapt to real-time service dynamics. Extensive evaluations of large-scale, dynamic service scenarios under CPN have shown that OSAF outperforms the current routing mechanisms in terms of SLA fulfillment, scalability, and load balancing.
Process models represent valuable experiential knowledge accumulated by enterprises throughout their development. The reuse of such knowledge can save managerial time and enhance the efficiency of process designers. Traditional process recommendation methods mostly focus on matching between process models themselves while neglecting the cross-modal matching problem between process texts and process models. Furthermore, enterprises accumulate process models in diverse forms, yet existing methods can only recommend a single form of process model (e.g., BPMN or Petri nets) and are unable to recommend multiple forms simultaneously. To address these challenges, this paper proposes a novel process recommendation network, referred to as ProcNet, which integrates large language models (LLMs) and graph convolutional networks (GCNs) for process recommendation. Specifically, (1) ProcNet uses LLMs to extract process activities and their relationships from requirement texts, constructs a directed graph, and semantically aligns it with the process activities in the database. Meanwhile, the process models in the database are also transformed into directed graphs; (2) ProcNet employs GCNs to obtain high-dimensional feature representations of each directed graph; (3) ProcNet calculates the top-k process models in the database most similar to the requirements using Euclidean distance and recommends them to the user. Extensive experiments demonstrate that ProcNet outperforms existing baselines, achieving remarkable results in recall, precision, F1-score, and mean average precision (MAP).
Cold starts are a big challenge in Function-as-a-Service (FaaS). While most solutions focus on optimizing the runtime environment and FaaS scheduling, they often overlook the impact of FaaS implementation on cold-start latency. Dynamic languages like Python, with their extensive dependency imports and dynamic building processes, can particularly suffer from longer startup times. We propose LightLoader, an approach to accelerating Python-based FaaS (i.e., PyFaaS) cold start by debloating them and optimizing their dynamic build. LightLoader rewrites PyFaaS by converting potentially unused functions to on-demand dynamic loading, thereby reducing loading time. It also delays third-party package loading until its first use instead of importing these all at the beginning. Notably, to ensure that these rewrites do not compromise the original PyFaaS’s functionalities, we utilize a large language model (LLM) to test and repair the optimized PyFaaS iteratively. We implemented a prototype on the popular open-source serverless platform OpenFaaS and conducted an extensive evaluation with PyFaaS of various sizes and functionalities. The experimental results show that LightLoader effectively accelerates PyFaaS cold starts, reducing end-to-end latency by 11.44
In service-oriented industrial supply chains, enterprises collaborate to share information while maintaining strict privacy requirements that prohibit raw financial data exchange between corporate groups. We propose a novel Granular Fuzzy Rule-Based Model based on Density Peaks Clustering (DPC-GFRBM) for privacy-preserving revenue prediction in industrial supply chain networks. DPC-GFRBM performs local clustering within each enterprise group to identify revenue patterns, then aggregates these results through collaborative analysis without revealing individual data. A fuzzy rule-based model maps the granulated features to revenue predictions, balancing privacy protection with predictive accuracy. Experimental results on public and real-world enterprise revenue datasets show that DPC-GFRBM consistently outperforms traditional fuzzy rule-based methods, achieving over 20
Optimizing route planning for Mobility-as-a-Service platforms, such as carpooling, involves solving combinatorial problems under precedence and multi-candidate constraints. Traditional methods struggle to balance real-time requirements and solution quality due to computational complexity. We introduce DeepQUBO, a quantum-compatible framework that reformulates the Precedence-Constrained Generalized Traveling Salesman Problem (GTSP-PC) into a sparse Quadratic Unconstrained Binary Optimization (QUBO) model. The framework employs auxiliary variables that decouple complex constraints, maintaining optimality while reducing QUBO elements by 51 × faster convergence than conventional approaches. Frustration analysis confirms DeepQUBO’s enhanced suitability for quantum optimization, achieving lower constraint-frustration ratios across benchmark datasets. Our work bridges service orchestration with quantum-ready optimization, offering practical value for large-scale transportation systems.
