In data-intensive microservice-based systems, frequent and large-scale inter-service communication poses a critical performance bottleneck, degrading throughput and escalating latency. Existing solutions exhibit notable limitations: microservice merging sacrifices loose coupling and system evolvability; resource-aware scheduling enhances communication efficiency but fails to reduce data volume; dynamic deployment reduces network distances while introducing compute-resource contention; and unnecessary data transfer elimination remains ineffective under massive data loads. Hence, to overcome these challenges, we propose iRUC, an approach forinter-service data communicationReduction viaUnifiedComputation. In particular, we first designGraphQL+, an executable declarative language that extends GraphQL with service-interaction semantics to achieve unified, cross-language modeling of data processing and transmission across microservices. Second, we develop anLLM-based multi-agent systemleveraging Claude 4.5 Sonnet and Gemini 2.5 Pro to automatically parse microservice code and synthesize corresponding GraphQL+ models. Third, we implement the unifiedexecution enginefor GraphQL+ models, including the database gateway that preserves microservice database autonomy while enabling cross-database queries. This design enables iRUC to perform the unified modeling and execution of data processing and transmission across microservices, thereby significantly reducing inter-service data transfer while maintaining microservice independence. Experimental evaluation on nine GitHub open-source microservice projects deployed on Huawei Cloud demonstrates iRUC’s effectiveness: compared with the unnecessary transfer elimination, dynamic deployment, and serverless computing approaches, iRUC improves throughput by 5.57×, 1.52×, and 1.87×, respectively, while reducing latency to 7.7%, 40.7%, and 37.4% of those approaches. These results show that iRUC achieves significant performance improvements in large-scale data processing scenarios.

Microservice architecture is favored for its loose coupling, reusability, and scalability. However, in data-intensive systems where data is the primary and permanent assets, the dynamic inter-microservice communication leads to a large amount of inter-microservice data transfer. This leads to a significant reduction in throughput and an increase in latency. While existing strategies, including microservice decomposition, deployment and resource optimization, show promise, they overlook the unnecessary inter-microservice data communication in practice, which includes the excessive data exposure and carryover data. This motivates us to develop iRUC, an integrated approach to remove the unnecessary inter-microservice data communications, through integrating the Excessive Data Transmission Removal, Carryover Microservice Upgrade, and Query Language (QL) Statement Composition. By implementing a microservice based data intensive system and deploying it on the public cloud environment, the experimental results confirm the effectiveness of our approach, achieving on average 4.2 × to 5.3 × throughput improvement and 78.6% to 85.9% latency reduction.

In this research, we have proposed the parallel GPU-accelerated algorithms to compute the Jacobi moments defined in a rectangular region with substantially improved computational efficiency and highly satisfied accuracy. In our algorithms, the parallel 3-D matrix multiplications are adopted to increase the computational efficiency, while the techniques of coalesced memory access, shared memory and heterogeneous computation are utilized to optimize the computing performance on a GPU platform. Our new GPU-accelerated system can provide any required moment computational accuracy without additional computing time. To verify the performance of our new parallel GPU-accelerated algorithms, we conducted a series of 2D and 3-D image analysis tests via the Jacobi moments with encouraging outcome, while the computing times for all these experimental tasks are in the level of milliseconds. It is expected that our new parallel GPU-accelerated algorithms will expedite the research in 3-D image analysis via the moment methods in the real-time range.

In edge computing, applications are usually delivered as services, each of which runs independently and cooperates to construct complicated applications. QoS (Quality of Service) monitoring is an important way to detect and locate faulty services. In a decentralized environment, QoS monitoring will face trust problem because it is difficult to guarantee the trustworthiness of monitoring results. This article builds a blockchain system for QoS monitoring. However, there are two challenges. First, although the blockchain consensus ensures the consistency of on-chain data among nodes, there is no guarantee that the monitoring data collected in the decentralized environment are authentic, because malicious nodes may report falsified data. Second, in order to handle service faults in time, the real-time query is usually required for obtaining monitoring data. But, blockchains suffer from inefficient querying, because the sequential data storage of blockchain is designed for write intensive applications at the expense of some read performance. To address these challenges, this article proposes a clustering-based algorithm for validating the authenticity of monitoring data collected in the decentralized environment, and proposes a probabilistic threshold query over blockchain, which supports efficient querying and guarantees the probability that the query results are correct is not less than a given threshold. This article implements the proposed blockchain system based on the blockchain platformHyperledger Fabricand the edge computing platformKubeEdge. The experiment results demonstrate the proposed blockchain system provides high-throughput and low-latency monitoring ability, and can efficiently obtain monitoring results close to real QoS data.

Blockchain technologies pave a promising way for implementing the inter-organizational processes. Most of the current research works translate the execution logic in the process models into the smart contracts, which can run independently on the blockchain without the outside process engine. However, the works usually suffer from the execution and storage costs, since the translation needs to be done when the processes are deployed. In this paper, we customize a process engine for executing the inter-organizational business processes via a blockchain-style procedure, i.e., checking the validity of transactions, adding the valid transactions into the blockchain through the consensus mechanism, and then updating the process states according to the committed transactions. And then, we build a blockchain system by embedding the customized process engine into the blockchain nodes. Moreover, in order to realize the interactions between the inter-organizational processes running on blockchain and the services outside blockchain, we propose a blockchain-based approach for service registration, binding and invocation, and design a lease-based concurrency control protocol to logically isolate transactions from each other when invoking the services simultaneously. Finally, we implement a prototype system based on a permissioned blockchain platform Hyperledger Fabric and a process engine Activiti. The experimental results show the proposed blockchain system can execute the inter-organizational processes correctly and efficiently.

Smart contracts (SCs) run on the distributed ledger technology (DLT) platform and can implement agreements between participants without a trusted third party. This paper uses the DLT and SC techniques to build distributed applications composed of existing services. In practice, there are many functionally-equivalent services on the Internet. To beat their competitors, the service providers usually offer flexible QoS and use dynamic pricing strategies. Moreover, the service providers can change at runtime, e.g., they may encounter problems so that their QoS drops suddenly. This makes achieving the optimization goal at runtime (e.g., the maximization of the utility) more difficult. To address this problem, first, this paper proposes an SC-based negotiation framework. The SCs can ensure that the transactions are automatically and reliably performed as agreed upon between the service requesters and providers. The DLTs can provide the reliable data of the requests and responses of the service requesters and providers to the SCs. In addition, the SCs can identify the troubled service providers, and find other service providers to replace them at runtime. Second, this paper proposes a Bayesian Nash equilibrium (BNE) of the service providers. In the BNE, the cost-efficient service providers offer the high QoS the service requester asks for and report their costs truthfully. This BNE enables the selection of the cost-efficient service providers and the achievement of the (near) maximization of the service requesters' utility. This paper implements the proposed negotiation framework on a DLT platform called Hyperledger Fabric. The experiment results demonstrate that the proposed approach outperforms the existing approaches and can adapt to the changes of the service providers.

QoS-aware service selection seeks to find the optimal service providers to achieve the optimization goal of a service requester, such as the maximization of utility, while satisfying global QoS requirements. Service providers are usually self-interested and have some private information, such as minimum prices, that would significantly factor into the decision making of the service requester. Thus, service requesters face a decision making dilemma with incomplete information. Recent work has used iterative combinatorial auctions to address this problem. However, such studies do not sufficiently consider that the service requester can elicit the private information from service providers by observing their actions. This can help the service selection process achieve better outcomes. In this paper, we propose a type of incentive contract that can motivate the service providers to offer the QoS and prices that the service requester prefers. Based on the incentive contracts, we propose an incentive mechanism for effective service selection. In the mechanism, a service requester offers a set of incentive contracts to the service providers and then elicits their private information based on their responses to the incentive contracts. The process is iterated until the service requester finally obtains a solution that fulfills the global QoS requirements. Experimental results show that the incentive contracts have a positive impact on both service requesters and providers and that the incentive mechanism outperforms the existing combinatorial auction-based approaches in finding optimal solutions.

Bag-of-ngrams (BoN) models are commonly used for representing text. One of the main drawbacks of traditional BoN is the ignorance of n-gram's semantics. In this paper, we introduce the concept of Neural Bag-of-ngrams (Neural-BoN), which replaces sparse one-hot n-gram representation in traditional BoN with dense and rich-semantic n-gram representations. We first propose context guided n-gram representation by adding n-grams to word embeddings model. However, the context guided learning strategy of word embeddings is likely to miss some semantics for text-level tasks. Text guided n-gram representation and label guided n-gram representation are proposed to capture more semantics like topic or sentiment tendencies. Neural-BoN with the latter two n-gram representations achieve state-of-the-art results on 4 document-level classification datasets and 6 semantic relatedness categories. They are also on par with some sophisticated DNNs on 3 sentence-level classification datasets. Similar to traditional BoN, Neural-BoN is efficient, robust and easy to implement. We expect it to be a strong baseline and be used in more real-world applications.

An important issue in QoS-aware Web service composition is how to select a set of Web services to perform the tasks within a requested service while meeting global QoS constraints. We consider the Web services are self-interested and will use dynamic pricing strategy. In general, the service cost is the minimum price acceptable to a Web service. We can obtain a composite Web service with the maximum utility by assigning the tasks to the Web services with the lowest costs. A Web service usually will not expose his cost, and thus, we face a decision making problem with incomplete information. Recent approaches use iterative combinatorial auction to address the problem. However, truthful bidding is not optimal strategy for Web services in these approaches. In this paper, we propose an incentive mechanism for choosing the optimal Web service for each task and show there exists a Bayesian Nash equilibrium of Web services, in which each Web service will bid truthfully. Finally, the experimental results show that our mechanism outperforms the existing combinatorial auction-based approaches.

The service-oriented paradigm offers support for engineering service-based systems based on service composition. QoS (Quality of Service)-aware service composition chooses a set of services to collectively construct a service-based system, while satisfying global QoS constraints and budget restriction. The service providers naturally are self-interested and strive to maximize their own utilities. Existing approaches use iterative combinatorial auction to address the problem. However, truthful bidding is not optimal strategy for service providers in these approaches. In this paper, we propose a multi-round Vickrey auction to choose an optimal service provider for each task while satisfying our global QoS constraints and budget restriction, and show there may exist a Bayesian Nash equilibrium, in which the service providers will not choose strategically to stay silent and will truthfully bid. Finally, the experimental results show that our approach outperforms the existing combinatorial auction-based approaches.

NBSVM is one of the most popular methods for text classification and has been widely used as baselines for various text representation approaches. It uses Naive Bayes (NB) feature to weight sparse bag-of-n-grams representation. N-gram captures word order in short context and NB feature assigns more weights to those important words. However, NBSVM suffers from sparsity problem and is reported to be exceeded by newly proposed distributed (dense) text representations learned by neural networks. In this paper, we transfer the n-grams and NB weighting to neural models. We train n-gram embeddings and use NB weighting to guide the neural models to focus on important words. In fact, our methods can be viewed as distributed (dense) counterparts of sparse bag-of-n-grams in NBSVM. We discover that n-grams and NB weighting are also effective in distributed representations. As a result, our models achieve new strong baselines on 9 text classification datasets, e.g. on IMDB dataset, we reach performance of 93.5% accuracy, which exceeds previous state-of-the-art results obtained by deep neural models. All source codes are publicly available at https://github.com/zhezhaoa/neural_BOW_toolkit.

In QoS-aware service selection, a service requester seeks to maximize its utility by selecting a service provider that charges the lowest service price while meeting the requester's QoS requirements. In existing selection approaches, a service requester focuses on finding providers based on their QoS and thereby ignores their service prices that could change with their QoS. High QoS may provide more benefits, but may require a high service price. As a result, the highest QoS may not produce the maximum utility. A service requester and candidate service providers have a conflicting interest over service prices. Since a provider would not reveal its minimum acceptabl price, it is important for a requester to predict the minimum price for a service that meets its QoS requirements. We propose a collaborative approach to predicting a provider's minimum price for a desired QoS based on prior usage experience. The experimental results show our approach can find the optimal service providers efficiently and effectively.

Feature selection is an essential data processing step to remove irrelevant and redundant attributes for shorter learning time, better accuracy, and better comprehensibility. A number of algorithms have been proposed in both data mining and machine learning areas. These algorithms are usually used in a single table environment, where data are stored in one relational table or one flat file. They are not suitable for a multi‐relational environment, where data are stored in multiple tables joined to one another by semantic relationships. To address this problem, in this article, we propose a novel approach called FARS to conduct both Feature And Relation Selection for efficient multi‐relational classification. Through this approach, we not only extend the traditional feature selection method to select relevant features from multi‐relations, but also develop a new method to reconstruct the multi‐relational database schema and eliminate irrelevant tables to improve classification performance further. The results of the experiments conducted on both real and synthetic databases show that FARS can effectively choose a small set of relevant features, thereby enhancing classification efficiency and prediction accuracy significantly.

An automated Web service discovery requires Web service capability specifications of a high precision. Semantic-based approaches are inherently more precise than conventional keyword-based approaches. This paper proposes to build capability specifications of Web services based on an Environment Ontology, main concepts of which are the environment entities in a particular application domain and their interactions. For each environment entity, there is a tree-like hierarchical state machine modeling the effects which are to be achieved by the Web services on this environment entity. The proposed approach is based on the assumption that the Web service capability specifications, built on the effects of the environment entities, are more accessible and observable. Algorithms for constructing the domain environment ontology and the matchmaking between the Web service capability specifications are presented to show how the Web service discovery is supported. An example on Travel Service is given to illustrate this proposed approach.

Capability specification is key problem for Web service discovery. Conventional one-step process based capability specification has its limitations. This paper proposes an approach for semantic behavior-based capability specification of Web service to stride over the limitations. Meta-level environment ontology is proposed to provide formal and sharable specifications of environment resources in a particular domain. For each environment resource, there is a corresponding hierarchical state machine specifying its dynamic characteristics. Then, effects on the environment resources are modelled with the hierarchical state machines. On the basis of the environment ontology, forest-structured communicating hierarchical state machines (FCHM) are defined and expected to be semantics of capability specification of Web services, which can be derived from the effects that Web services impose on their environments