Multimedia content offers additional context for recommender systems to better understand user interests. Existing studies on multimodal recommendation primarily focus on constructing item-item semantic graphs. However, most of these methods capture only shallow semantic structures based on feature similarity and struggle to model more complex or cross-entity semantic relationships (e.g., user-item). Moreover, in these methods, collaborative signals often dominate and suppress semantic knowledge, which limits its role in representation learning. To address these issues, we propose SCALE, a novel framework that combines subspace-aware graph construction and contrastive alignment for multimodal recommendation with large language models. Specifically, we first use large language models and encoders to extract user and item features. Following the subspace clustering assumption, we apply the Orthogonal Matching Pursuit algorithm to mine complex semantic structures within the item-item, user-user, and user-item spaces, and integrate them into a unified semantic graph. We then perform graph convolution on both the semantic and interaction graphs, and aggregate the results for recommendation. Furthermore, contrastive losses are employed to enhance semantic fusion and alignment. Extensive experiments on five real-world datasets demonstrate that SCALE significantly outperforms state-of-the-art multimodal recommendation models, highlighting its effectiveness in modeling complex relationships and integrating semantic knowledge with collaborative signals.

Web APIs have become a fundamental paradigm in the Web 4.0 era, with mashup services emerging as a transformative technology that combines multiple APIs to create comprehensive services. However, existing approaches exhibit two significant limitations: overlooking the quality and completeness of recommendation contexts of new mashup requirements, and failing to effectively extract high-quality collaborative features from multi-perspective service relationships. To address these limitations, we propose LMSR, a novelLLM-enhancedMulti-perspectiveService Feature Learning framework for Web APIRecommendation. LMSR first leverages general-purpose LLM to refine and encode the original requirement descriptions, and employs a Mixture of Service Experts (MoSE)-based context prediction model to precisely predict service information relevant to new requirements, establishing a comprehensive and high-quality recommendation context for mashup requirements. Furthermore, by integrating the predicted recommendation contexts into the LLM through fine-tuning, LMSR effectively extracts collaborative features from multi-perspective service relationships between mashup requirements and APIs, ultimately achieving precise Web API recommendation. Comprehensive experiments on real-world datasets demonstrate that LMSR significantly outperforms 11 baseline approaches across precision, recall, F1-score, and NDCG, validating its effectiveness in Web API recommendation. The codes are available athttps://scdm-shu.github.io/codes/LMSR.zip.

Edge service deployment has attracted significant attention in recent years, aiming to optimize service placement on edge servers while satisfying diverse requirements. However, existing approaches often overlook the influence of geographical contexts on service demands, where user needs vary significantly across regions with distinct characteristics. They also fail to account for differences between direct responses and multi-hop forwarding in edge network topology, leading to unsatisfactory edge service deployment strategies. To this end, we formulate the Points of Interest-Based Edge Service Deployment (POI-ESD) problem with topology-aware optimization, integrating POI attributes and spatial distributions while incorporating edge network topology to enhance service placement. By proving the$\mathcal{N}P$-hardness of POI-ESD problem, we propose a novel graph-encoded genetic algorithm, MTGA, to efficiently generate high-quality deployment strategies. It ensures strategic placement of edge services in regions that best match user demands, improving the service utilization and satisfiability for edge users. Extensive experiments on a real-world dataset combining Shanghai Telecom and Baidu Maps POI data demonstrate that MTGA significantly outperforms existing competing approaches, achieving superior performance of edge service deployment.

As a dominant paradigm in modern web applications, cloud computing has seen a surge in adoption. The deployment of vast and various workloads encapsulated within containers has become ubiquitous across cloud platforms, imposing substantial demands on the supporting infrastructure. However, Kubernetes (k8s), the de facto standard for container orchestration, struggles with low scheduling throughput and high latency in large-scale clusters. The primary challenges are identified as excessive load from read requests and resource contention between co-located components. In this paper, we present MerKury, a general and lightweight framework designed to enhance the Kubernetes performance for large-scale clusters. MerKury employs a dual strategy: first, it preprocesses specific requests to alleviate excessive load; second, it introduces an adaptive resource allocation algorithm to mitigate resource contention. Evaluations across various cluster scales demonstrate that MerKury notably augments node capacity by up to 4.5×, increases scheduling throughput by up to 7.3×, and reduces request latency by 5.6%-57.7%, outperforming vanilla Kubernetes and baseline resource allocation methods.

The Intelligent Transportation System (ITS) serves as a pivotal element within urban networks, offering decision support to users and connected automated vehicles through comprehensive information gathering, sensing, device control, and data processing. Presently, ITS predominantly relies on sensors embedded in fixed infrastructure, notably Roadside Units (RSUs). However, RSUs are confined by coverage limitations and may encounter challenges in prompt emergency responses. On-demand resources, such as drones, present a viable option to supplement these deficiencies effectively. This article introduces an approach where Software-Defined Networking and Mobile Edge Computing technologies are integrated to formulate a high-availability drone swarm control and communication infrastructure framework comprising the cloud layer, edge layer, and device layer. Drones confront limitations in flight duration attributed to battery limitations, posing a challenge in sustaining continuous monitoring of road conditions over extended periods. Effective drone scheduling stands as a promising solution to overcome these constraints. To tackle this issue, we initially utilized Graph WaveNet, a specialized graph neural network structure tailored for spatial-temporal graph modeling, for training a congestion prediction model using real-world dataset inputs. Building upon this, we further propose an algorithm for drone scheduling based on congestion prediction. Our simulation experiments using real-world data demonstrate that, compared to the baseline method, the proposed scheduling algorithm not only yielded superior scheduling gains but also mitigated drone idle rates.

Web service-oriented transactions have become an integral part of the Internet economy, with the mainstream transaction patterns relying primarily on cloud service markets. However, traditional service transaction methods have deficiencies in terms of both trust and scalability. Distrust between service provider (SP) and consumer (SC), particularly around online payments and data security, impedes the further growth of service transactions. Although blockchain-based transaction mechanisms have made notable progress in addressing trust issues, they still face performance bottlenecks. To tackle these challenges, this paper introduces TrustPay, a service transaction framework that leverages a dual-layer blockchain structure consisting of a parent chain and multiple subchains. The framework partitions the subchain network based on business, with each subchain dedicated to storing service invocation records generated within a specific business unit. The smart contract deployed on the parent chain will settle the invocation records in all subchain networks as transaction records and facilitate automatic transfers among blockchain accounts. This design leverages blockchain's inherent reliability while improving its scalability in large-scale scenarios. Additionally, a novel consensus protocol, REFEREE, is introduced and applied to the subchain network, ensuring efficient recording of invocation data and trusted verification among participants, further enhancing both trust and performance. Comparative experiments and analysis show that TrustPay's dual-layer blockchain structure and REFEREE protocol are not only reliable but also outperform baseline methods in terms of efficiency.

Recent years have witnessed remarkable advances in graph representation learning using Graph Neural Networks (GNNs). To fully exploit the unlabeled graphs, researchers pre-train GNNs on large-scale graph databases and then fine-tune these pre-trained G raph M odels (GMs) for better performance in downstream tasks. Because different GMs are developed with diverse pre-training tasks or datasets, they can be complementary to each other for a more complete knowledge base. Naturally, a compelling question is emerging: How can we exploit the diverse knowledge captured by different GMs simultaneously in downstream tasks? In this paper, we make one of the first attempts to exploit multiple GMs to advance the performance in the downstream tasks. More specifically, for homogeneous GMs that share the same model architecture but are obtained with different pre-training tasks or datasets, we align each layer of these GMs and then aggregate them adaptively on a per-sample basis with a tailored Recurrent Aggregation Policy Network (RAPNet). For heterogeneous GMs with different model architectures, we design an alignment module to align the output of diverse GMs and a meta-learner to decide the importance of each GM conditioned on each sample automatically before aggregating the GMs. Extensive experiments in various downstream tasks from 3 domains reveal our dominance over each single GM. Additionally, our methods (UniGM) can achieve better performance with moderate computational overhead compared to alternative approaches including ensemble and model fusion. Also, we verify that our methods are not limited to graph data but could be flexibly applied to multiple modalities. The codes are available at https://github.com/monica309673/UniGM.

Innovation and prosperity of the Modern Service Industry bring convenience and efficiency to our society. However, the service governance capability lags behind the development of the industry, causing problems such as service violations and poor service quality. Existing works towards service regulation for service design time such as business process compliance checking are restricted to certain types of rules. When services or policies undergo evolution, rapid iteration becomes challenging. Motivated by this, we propose a service regulation analysis framework for service design time. It includes three phases: the service regulation modeling phase, which realizes modeling of regulation requirements; the service violation recognition phase, proposing an automatic detection algorithm based on process semantics; and the violation trace-back phase, which supports rapid localization of violations. Based on previous work, we construct the Enhanced-LPD4VR, a dataset with a broader range of processes and more nuanced annotations. Furthermore, we introduce an Internet healthcare service case study that illustrates the effectiveness of our framework through comparative experiments.

The growth of service ecosystems, which include diversified services such as cloud and edge services, has led to a thriving service transaction market. However, service pricing remains a major obstacle to further progress. Without proper pricing guidance, service providers tend to formulate pricing strategies solely based on their own interests, which frequently hinders the maximization of overall market benefits. This problem is even more challenging in edge computing scenarios as different Edge Service Providers (ESPs) are located in distributed regions and influenced by multiple factors, making it difficult to formulate a single pricing model. This paper proposes a multi-participant stochastic game model to formalize the multi-edge service pricing problem. An incentive mechanism based on Pareto improvement is then proposed to drive the game to the Pareto optimal direction with optimal profits. Finally, an improved PSO algorithm is proposed to solve the game model and analyze the equilibrium states under different evolutionary mechanisms. Experimental results indicate that the proposed pricing incentive mechanism can promote a more effective and reasonable pricing allocation, avoiding 21.6% anarchism loss of the overall profits, while showcasing the effectiveness of our algorithm in solving the game.

The deep integration and innovation of cross-over services across the boundaries of different industries, organizations and individuals will provide developers with multidimensional, high-quality and valuable cross-over services, which has become an important innovation approach for the development of modern service industry. With the further development of this trend, services in the form of processes play an increasingly important role in the field of service computing research. However, with the rise of digitization and the escalation of cross-industry, cross-over participants face two tough questions: (l) What kind of service process can solve the complex business we face.(2) How can we access these service processes quickly and conveniently. In this paper, we propose the concept of reference service process to solve the problems above. The reference service process is a normalized cross-over service collaboration paradigm. On the one hand, reference service process of different functional topics can solve most complex businesses, eliminating the need for developers to design service processes. On the other hand, each reference service process establishes a mapping relationship with multiple general service processes, which solves the problem of selection by automatically selecting the optimal service process for developers.

The increasing number of available open services demands distributed architectures to promote scalability to enable their precise and efficient retrieval. As web services proliferate, it is more and more difficult to find suitable services and further compose them into complex applications automatically. In this paper, a novel service infrastructure named Service Network(ServNet), which is constructed on the basis of service routers and service switches, is proposed to deploy and manage large-scale services. Furthermore, the zone routing algorithm (ZRA) combines the advantages of the proactive and reactive approaches by maintaining an up-to-date topological map of a zone centered on each service node. Experimental evaluations confirmed the efficiency in terms of accuracy of query and network traffic.