ABSTRACT Key Performance Indicator (KPI) anomaly detection is a crucial component of Artificial Intelligence for IT Operations (AIOps). In practical scenarios, service anomalies can lead to missing KPI values, which reduce the accuracy of anomaly detection. Moreover, existing methods frequently struggle to effectively detect subtle anomalies, thereby limiting detection performance. This study proposes a KPI anomaly detection framework that integrates missing value interpolation and adversarial training. First, a missing value interpolation model is built using an improved Transformer. The model is optimized via a mean absolute error loss constructed by randomly masking observations, while a diagonal mask in the self‐attention mechanism enhances interpolation accuracy. Second, a detection architecture is developed based on Variational Autoencoder‐Gated Recurrent Unit (VAE‐GRU) and adversarial training, which amplifies reconstruction error sensitivity to subtle anomalies. Finally, the Streaming Peaks Over Threshold (SPOT) algorithm is incorporated for adaptive thresholding based on the distribution of reconstruction errors. Experimental results demonstrate that the proposed framework achieves superior F1 scores and recall compared to existing methods on multiple benchmark datasets.

In the fifth generation mobile networks(5G), D2D (device-to-device) communication technology is introduced to provide services to users by utilizing the resources of idle mobile devices, and users can schedule computing tasks to be executed on mobile servers (MSs). This paper aims at reducing overall time delay and energy consumption by taking into account MSs mobility, task attributes, resource status for D2D scenario. We formulate the matching problem as a one-to-one matching game and propose a scheduling strategy based on binary graph matching algorithm. Simulations show that the strategy proposed in this paper can comprehensively reduce the task execution delay and energy consumption, and improve the success rate of task execution.

In Mobile Edge Computing (MEC), the services that a user receives change dynamically with location due to the user's mobility. If we mine the user's location data, predicting the user's next location, we can get the user's services to be used. It is convenient for the edge server to preload the user's services. When users reaches predicted location, the edge servers near users provide timely services. Therefore, this paper proposes a Spatio-temporal Position Prediction Model (SPPM) for Mobile Users Based on LSTM (Long Short-Term Memory) model in the mobile edge computing. Firstly, the time series feature extraction method is used to preprocess the historical location data of the mobile user. Next, the model uses the PCA data dimensionality reduction algorithm to process the data and then uses the LSTM model to predict the next spatiotemporal trajectory point of the mobile user. Finally, using the 17621 user trajectory data of the Geolife GPS trajectory data set, the algorithm is tested and verified. The experimental results show that the SPPM model proposed in this paper has higher prediction accuracy and more accurate prediction position.

In the edge computing, the service is deployed to the edge server through virtualization technology. Most strategies of service deployment are singleness and ignore the diversity of users' requirements and the service deployment cost at the edge. This paper proposes a clustering-based algorithm for service deployment, which considers the delay at the user side and the edge-side services deployment cost, and establishes a service deployment model based on multi-objective integer linear programming. Firstly, the K-means clustering algorithm is optimized to solve the problem of hotspot migration in the process of service deployment and reduce the deployment cost at the edge server side, then alternative enhanced heuristic algorithm is proposed to find the approximate optimal solution of services deployment. Experiments show that the algorithm can reasonably deploy services. Compared with traditional heuristic algorithms, the algorithm proposed in this paper has better performance in terms of user side and edge server side.

In order to reduce the delay and energy consumption of mobile devices, a computational offload strategy is adopted in mobile edge computing (MEC). At present, most computation offloading strategies only consider two computing resources, mobile devices and MEC servers. However, the computing power of the cloud server is much larger than that of the MEC server. Tasks with high computational complexity still need to be handed over to the cloud server for processing. This paper proposes an edge-cloud collaborative multi-task computing unloading model that considers both latency and energy cost. Usually the model solving is transformed into a search solution in finite strategy space. In this paper, the nonlinear exponential inertia weight particle swarm optimization (PSO) algorithm is used to get solution. By dynamically adjusting the inertia weight, the algorithm can make up for the convergence premature defect of the standard particle swarm optimization algorithm, and effectively avoid falling into the local optimal solution. Simulation experiments show that the strategy obtained by the model has lower total cost compared with different computation offloading models and strategies.

In modern petroleum extraction industry, Internet of Things (IoT) technology is widely used, and various of sensors constitute a typical cyber-physical systems (CPS). There are more requirements of interoperability and interaction between well site device systems. In such industrial critical domain, high-confidence is also important. This paper presents the requirements and running environment of developing high-confidence CPS. To satisfy the requirements of interoperability and data exchange among heterogeneous well site systems, a foundation framework with well defined interfaces and quality of service constraints was established. Based on this framework, designing high-confidence petroleum extraction CPS using Model-Driven Architecture approach is proposed, the long-lived models produced in the process that can be applied to any implementation technologies and platforms through model transformation which make the systems more portable. A case study shows the detail of the development process and using communication bridges to build relationships between models. At last some important problems which need to be paid more attention in model transformation process are discussed.

Context-aware web services have been attracting significant attention as an important approach for improving the usability of web services. In this paper, we explore a novel approach to model dynamic behaviors of interacting context-aware web services, aiming to effectively process and take advantage of contexts and realize behavior adaptation of web services and further to facilitate the development of context-aware application of web services. We present an interaction model of context-aware web services based on context-aware process network (CAPN), which is a data-flow and channel-based model of cooperative computation. The CAPN is extended to context-aware web service network by introducing a kind of sensor processes, which is used to catch contextual data from external environment. Through modeling the register link's behaviors, we present how a web service can respond to its context changes dynamically. The formal behavior semantics of our model is described by calculus of communicating systems process algebra. The behavior adaptation and context awareness in our model are discussed. An eXtensible Markup Language-formatted service behavior description language named BML4WS is designed to describe behaviors and behavior adaptation of interacting context-aware web services. Finally, an application case is demonstrated to illustrate the proposed model how to adapt context changes and describe service behaviors and their changes.

Formal method is an effective way for modeling and verifying concurrent system. An important research field is to describe and verify Web services by formal method. Guaranteeing the validity of Web services composition is necessary for enhancing the value of this composite service. CCS is a kind of process algebra which can be used to model concurrent systems. Web services and their composition are described and modeled based on CCS in this paper. Rules about applying CCS to Web services are explained. Finally, a case study is carried and the validity of composition model is verified. Some important points in verification are discussed

This paper proposed a Kahn process network(KPN) based service interaction model which can dynamically establish links between computational service nodes. Three advantages of KPN make it adequate to model service interactions: (1) parallelism and communication mechanism in KPNs, which enable distributed service interaction on Internet; (2) KPNs are compositional, which corresponds to the possibility to build bigger behaviors from small ones; (3) KPN can be executed, which ensures a executable service interaction environment for actual application. Under the three advantages above, we propose four kinds of service interaction rules and corresponding interaction events which enrich KPN operations and extend KPN semantics. We design a service description language called SDL to describe both static properties and dynamic interactions of services. The implementation architecture of service interaction model is present. Finally, we introduce an application case to show how to describe service interactions using SDL

This paper introduces a cooperative service composition language called CCML which aims to facilitate the description of services, their interfaces and their behavior, further to reduce the complexity required to compose services. Interaction rules among services rely on a cooperative computation model, i.e. KPN (Kahn Process Network), which adopts dataflow and channel based asynchronous communication pattern among process nodes. Formal model for behavioral semantics of CCML is based on CCS process algebra which presents a high expressive power, capable of capturing CCML behavioral semantics. We give an operational semantics to CCML in the form of a labeled transition system (LTS) and describe the events of a LTS associated to the main CCML constructs, which are sequence, condition, loop and parallel. Finally, we present an application case to show how to describe service composition using CCML