Cloud computing has revolutionized data center management by providing scalable and efficient resources for processing and data management. However, deploying containerd-based services in data centers presents significant challenges: (1) Active servers that are underutilized result in high energy consumption, necessitating optimization for energy efficiency; (2) Affinity requirements between services and servers must be considered to ensure appropriate deployments; (3) Quality of Service (QoS) requirements must be met, particularly to avoid performance interference when multiple services are deployed on the same server. To address these challenges, we propose a novel algorithm, Affinity-Interference Energy Deployment (AIED), based on Deep Reinforcement Learning (DRL). This algorithm strategically consolidates services onto fewer servers to optimize energy efficiency while adhering to stringent QoS and affinity constraints. By employing a demand-supply model to quantify QoS requirements and formulating the deployment challenge as a Markov Decision Process (MDP), our algorithm dynamically adapts to fluctuating demands and resource availability. Extensive simulations demonstrate that AIED significantly outperforms existing baseline strategies, reducing energy consumption while ensuring robust compliance with both QoS and affinity constraints.

Infrared small target detection (IRSTD) plays a vital role in various fields, especially in military early warning and maritime rescue. Its main goal is to accurately locate targets at long distances. Current deep learning (DL)-based methods mainly rely on mask-to-mask or box-to-box regression training approaches, making considerable progress in detection accuracy. However, these methods rely on large amounts of training data with expensive manual annotation. Although some researchers attempt to reduce the cost using single-point weak supervision (SPWS), the limited labeling accuracy significantly degrades the detection performance. To address these issues, we propose a novel point-to-point regression high-resolution dynamic network (P2P-HDNet), which can accurately locate the target center using only single-point annotation. Specifically, we first devise the high-resolution cross-feature extraction module (HCEM) to provide richer target detail information for the deep feature maps. Notably, HCEM maintains high resolution throughout the feature extraction process to minimize information loss. Then, the dynamic coordinate fusion module (DCFM) is devised to fully fuse the multidimensional features and enhance the positional sensitivity. Finally, we devise an adaptive target localization detection head (ATLDH) to further suppress clutter and improve the localization accuracy by regressing the Gaussian heatmap and adaptive nonmaximal suppression strategy. Extensive experimental results show that P2P-HDNet can achieve better detection accuracy than the state-of-the-art (SOTA) methods with only single-point annotation. In addition, our code and datasets will be available at:https://github.com/Anton-Nrx/P2P-HDNet.

Abstract For a long time, the problems of cold start and sparse data have always been the key problems to be solved by the recommendation system. Researchers usually use auxiliary information to deal with the aforementioned problems, thereby achieving the purpose of enhancing the recommendation effect. For example, the multitask feature learning framework (MKR) uses knowledge graphs as auxiliary information to enhance recommendations. However, the MKR algorithm has the problem of insufficient semantic information representation which affect the recommendation results. Thus, a multitask recommendation algorithm based on DeepFM and graph convolutional network (DeepFM_GCN) is proposed. The graph convolution network is used to deeply mine auxiliary entity information in the knowledge graph to supplement the sparse item semantics information in the recommendation task. Through the method of cross compression unit combined with Deep Neural Network to achieve feature sharing items and entities which to make up for the impact of insufficient feature representation. Then the DeepFM_GCN model utilizes DeepFM to deeply mine the interaction feature of users and items to avoid inaccurate items recommended to users. From the analysis of the experimental results, the DeepFM_GCN model can more fully explore user and item features, accordingly avoiding semantic ambiguity and improving prediction accuracy.

Most of the emerging applications such as the Deep Neural Networks (DNN) based smart Internet of Things (IoT) systems need intensive and high-performance computing, which is contradictory to the limited resources of IoT/terminal devices. It is a big challenge to offload all tasks to the cloud due to the bandwidth limitation, processing overhead, and transmission costs. Edge computing as an extension of cloud computing that can provide abundant computing resources near the edge of the network and thereby can potentially improve the QoS of applications. However, offloading tasks to the edge servers is liable to external security threats. How to balance the response time and the security of application services is a big challenge for realizing good application service placement. This paper proposes a time and security efficient task scheduling framework in the edge-cloud environment. The corresponding computing models are established, such as the security-related model, time model, and the risk probability model. Then, a time-guaranteed and security-aware task scheduling algorithm is proposed including the domain construction, the security-aware task ranking, and the task dispatching. Extensive simulation experiments have been conducted. Results show that the proposed method has better performance than the other four compared methods in general.

A behavior prediction method for autonomous driving vehicles based on the improved Hidden Markov Model is proposed in this paper. Firstly, several groups of initial model parameters are randomly generated to obtain a finite set of other vehicles’ possible actions. Secondly, the probability of vehicles switching between different actions, the probability of different actions and the probability of vehicles coming from different directions are studied by the Baum-Welch algorithm. Then, the statistical results obtained by Viterbi algorithm are combined with the Bayesian decision theory to determine the final results. Finally, significance hypothesis test is used to verify the correctness and reliability of the model prediction results.

Abstract Microservice design is a new paradigm of cloud application development. Different from monolithic design, microservice enjoys merits of fine‐grained and loosely coupled services, and it is becoming more and more popular. The application developed with microservice has a good advantage in independent development and flexible deployment, especially for complex distributed systems. However, there is a big gap between the reliability requirements and microservice‐based cloud applications. This article proposes a reliability model of microservice‐based cloud application by using predicate Petri net. First, a microservice reliability requirement is given, some basic concepts of predicate Petri net are defined with syntax and semantics. Second, a microservice reliability strategy is proposed, which uses microservice instances and circuit breaker to improve the reliability of the system. Based on the constructed microservice reliability model, the correctness of predicate Petri net modelling and the effectiveness of the strategies are proven theoretically. Finally, an example is given to illustrate the establishment and analysis process of the model, and several groups of experiments are carried out to verify the effectiveness and feasibility of the method. Experimental results show that the proposed microservice reliability strategy is effective.

Abstract In the process of the continuous development of the Internet of Things, cloud computing has been applied in many fields, how to guarantee the quality of service, such as low latency, high bandwidth, high reliability etc., has become a challenging problem. This paper proposes a method to model and optimize reliability for microservice‐based cloud applications using multi‐agent system (MAS), thus maximizing the reliability of cloud computing and dynamically scheduling microservices to minimize the delay within the budget. Firstly, a dynamic microservice scheduling scheme is proposed to provide efficient computing services by using MAS. A hierarchical cloud computing model is formed by predicated Petri net (PrT net) and the properties of constructed model are analysed. Secondly, agents have been utilized to describe the essential characteristics of microservice scheduling process in the cloud applications. The partial critical path (PCP) aims to maximize the reliability of cloud applications under the limitation of budget and meet the user‐defined deadline. Finally, the proposed PCPRO algorithm has been applied to cloud environment, which is suitable for different scientific workflows in the cloud computing environment. The effectiveness of this method is verified by simulation, the experiment results show the effectiveness of the proposed method.

Infrared (IR) small target detection has been extensively studied due to its importance in IR search and tracking (IRST) systems. Existing methods have some limitations in suppressing cluttered high-contrast backgrounds, which may result in more false detections. In this letter, on the one hand, we propose an improved fuzzy C-mean (IFCM) clustering to accurately segment IR small targets and complex backgrounds. On the other hand, an IFCM-based descriptor fusing multiple features (IFCM-MF) is proposed to suppress complex backgrounds. First, a sliding window is designed to quickly extract the candidate pixels. Then, to better enhance the target and suppress the background, we construct an IFCM-based local window and calculate the IFCM-MF. Finally, IR small targets are detected by adaptive thresholding operation. The experimental results show that our method can better suppress cluttered high-contrast backgrounds and significantly improve the detection performance with high speed.

Just-in-time software defect prediction (JIT-SDP) is a fine-grained software defect prediction technology, which aims to identify the defective code changes in software systems. Effort-aware software defect prediction is a software defect prediction technology that takes into consideration the cost of code inspection, which can find more defective code changes in limited test resources. The traditional effort-aware defect prediction model mainly measures the effort based on the number of lines of code (LOC) and rarely considers additional factors. This paper proposes a novel effort measure method called Multi-Metric Joint Calculation (MMJC). When measuring the effort, MMJC takes into account not only LOC, but also the distribution of modified code across different files (Entropy), the number of developers that changed the files (NDEV) and the developer experience (EXP). In the simulation experiment, MMJC is combined with Linear Regression, Decision Tree, Random Forest, LightGBM, Support Vector Machine and Neural Network, respectively, to build the software defect prediction model. Several comparative experiments are conducted between the models based on MMJC and baseline models. The results show that indicators ACC and [Formula: see text] of the models based on MMJC are improved by 35.3% and 15.9% on average in the three verification scenarios, respectively, compared with the baseline models.

Microservice architecture is a cloud‐native architectural style, which has attracted extensive attention from the scientific research and industry communities to benefit independent development and deployment. However, due to the complexity of cloud‐based platforms, the design of fault‐tolerant strategies for microservice‐oriented cloud applications becomes challenging. In order to improve the quality of service, it is essential to focus on the microservice with more criticality and maximize the reliability of the entire cloud application. This paper studies the modeling and analysis of service reliability in the cloud environment. Firstly, a formal description language is defined to model microservice, user request, and container accurately. Secondly, the reliability analysis is conducted to measure a critical microservice’s fluctuation and vibration attributes within a period, and the related properties of the constructed model are analyzed. Thirdly, a fault‐tolerant strategy with redundancy operation has been proposed to optimize cloud application reliability. Finally, the effectiveness of the method is verified by experiments. The simulation results show that the algorithm obtains the maximum benefits and has high performance through several experiments.

Cloud computing has been increasingly concerned in scientific computing area. More and more enterprises and research institutes have migrated their applications to the clouds. Due to the complexity of cloud computing system in structural and behavioral aspects, how to design the fault tolerant cloud computing system becomes a challenging problem. This paper investigates the modeling and analysis of fault tolerant strategy for deadline constrained task scheduling in cloud computing. First, a formal description language is defined to accurately model the different components of cloud application, and use it to characterize the operational mechanisms and fault behaviors. Second, we propose a fault tolerant strategy, which includes the scheduling mechanism, synchronization mechanism, and exception mechanism, to dynamically compute the execution mode and required virtual machine for tasks, thus ensuring the reliability and real-time requirement of cloud application. An enforcement algorithm is also designed to realize the proposed strategy. Third, the techniques of Petri nets are provided to analyze and validate the correctness of proposed method. Finally, several experiments are done to illustrate that the reliability of cloud application is improved and its deadline is met.

Software defect prediction, which locates defective code snippets, can assist developers in finding potential bugs and assigning their testing efforts. Traditional defect prediction features are static code metrics, which only contain statistic information of programs and fail to capture semantics in programs, leading to the degradation of defect prediction performance. To take full advantage of the semantics and static metrics of programs, we propose a framework called Defect Prediction via Attention Mechanism (DP-AM) in this paper. Specifically, DPAM first extracts vectors which are then encoded as digital vectors by mapping and word embedding from abstract syntax trees (ASTs) of programs. Then it feeds these numerical vectors into Recurrent Neural Network to automatically learn semantic features of programs. After that, it applies self-attention mechanism to further build relationship among these features. Furthermore, it employs global attention mechanism to generate significant features among them. Finally, we combine these semantic features with traditional static metrics for accurate software defect prediction. We evaluate our method in terms of F1-measure on seven open-source Java projects in Apache. Our experimental results show that DP-AM improves F1-measure by 11% in average, compared with the state-of-the-art methods.

As the second largest freshwater lake in Yunnan Province of China, Erhai Lake has suffered harmful algal blooms (HABs) since 1996. In January 2017, Dali government issued a series of ecological protection measures to prevent the outbreak of HABs. In order to evaluate the effectiveness of those protection measures, we studied the spatiotemporal distribution of HABs in Erhai Lake by using multi-source remote sensing data during 2016-2018. The results demonstrate that coverage area and frequency of HABs occurrence were decreasing after January 2017, which indicates a good respond to water protection regulations. However, in 2018, HABs still occurred around residential and tourist area. Furthermore, drastic measures also impeded local economic development due to shuttering businesses and banning farming around lake. Therefore, there is still a long way to go for local government to achieve long-term goals of sustainable economy and water environment stability of Erhai region.

An effective method for addressing the configuration optimization problem (COP) in Software Product Lines (SPLs) is to deploy a multi-objective evolutionary algorithm, for example, the state-of-the-art SATIBEA. In this paper, an improved hybrid algorithm, called SATIBEA-LSSF, is proposed to further improve the algorithm performance of SATIBEA, which is composed of a multi-children generating strategy, an enhanced mutation strategy with local searching and an elite inheritance mechanism. Empirical results on the same case studies demonstrate that our algorithm significantly outperforms the state-of-the-art for four out of five SPLs on a quality Hypervolume indicator and the convergence speed. To verify the effectiveness and robustness of our algorithm, the parameter sensitivity analysis is discussed and three observations are reported in detail.

Multi-objective evolutionary algorithm (MOEA) has been widely applied to software product lines (SPLs) for addressing the configuration optimization problems. For example, the state-of-the-art SMTIBEA algorithm extends the constraint expressiveness and supports richer constraints to better address these problems. However, it just works better than the competitor for four out of five SPLs in five objectives and the convergence speed is not significantly increased for largest Linux SPL from 5 to 30[Formula: see text]min. To further improve the optimization efficiency, we propose a parallel framework SMTPORT, which combines four corresponding SMTIBEA variants and performs these variants by utilizing parallelization techniques within the limited time budget. For case studies in LVAT repository, we conduct a series of experiments on seven real-world and highly-constrained SPLs. Empirical results demonstrate that our approach significantly outperforms the state-of-the-art for all the seven SPLs in terms of a quality Hypervolume metric and a diversity Pareto Front Size indicator.

Nowadays it is common for applications to run on data centers and deliver services to users. With the increase of tasks of multiple applications, it is a challenge for data center providers to make full use of the available resources, and improve task response time without too much computational cost. This paper focuses on load-balance based time efficient resource scheduling. A resource allocation architecture for SDN-enabled data center and a load-balance based resource allocation approach(LBA) are proposed. LBA is mainly used to maintain the the whole resource in a balancing state and assign appropriate resources to tasks, majorly consisting of three parts: Load-balance, VM-selection and Path-selection. Comprehensive simulation experiments are conducted to evaluate the effectiveness of LBA. Experiment results show that LBA can take full advantage of the available resources and improve task response time on the basis of load-balance, making both SLA violation rate and average cost as small as possible.

Forecasting monthly calls for repair from gas consumers is an important part of the gas company to improve the level of service, optimize the allocation of resources and improve the living level of people. In this paper, through the study of historical data of monthly calls for repair from gas consumers, we find that it has the characteristics of seasonal periodic variation. A hybridization methodology based on Seasonal Autoregressive Integrated Moving Average (SARIMA) and back propagation(BP) neural network is proposed, which is used to forecast monthly calls for repair from gas consumers. The time series of monthly calls for repair from gas consumers is decomposed into linear autocorrelation and non-linear structure of two parts. The SARIMA model is used to predict the linear part of the sequence, and the BP neural network model is used to predict the non-linear residual part. Finally, the forecast results of two parts are synthesized into the final result. The case study shows that the hybrid model outperforms either of the models used separately. Moreover, the hybrid model can balance the deviation of a single model with better applicability and higher accuracy.

Multi-objective evolutionary algorithm (MOEA) with the constraint solving has been successfully applied to address the configuration optimization problem in software product line (SPL), for example, the state-of-the-art SATIBEA algorithm. However, each different MOEA with special search operator demonstrates the different strength and weakness in terms of optimality and convergence speed. The SATIBEA just combines the SAT (Boolean satisfiability problem) constraint solving with the Indicator-Based Evolutionary Algorithm (IBEA) for evaluating the algorithm performance. In this paper, we propose six hybrid algorithms which combine the SAT solving with different MOEAs. Case study is based on five large-scale, rich-constrained and real-world SPLs. Empirical results demonstrate that SATMOCell algorithm obtains a competitive optimization performance to the state-of-the-art that outperforms the SATIBEA in terms of quality Hypervolume metric for 2 out of 5 SPLs within the same time budget. Moreover, the convergence speed of SATMOCell and SATssNSGA2 is comparable after 10min terminal times. Particularly, the Hypervolume value of SATssNSGA2 reports the average improvement of 1.33% after 20min terminal times.

Summary With the rapid development of cloud computing, many distributed data centers have been deployed. This means larger energy consumption requirements from the data center. How to reduce the cost of data center has received significant attention recently. Although there are several efforts in studying energy consumption of the data center, very few have considered modeling and analyzing cost‐aware job scheduling for the cloud data center. To address this emerging problem, we propose a systematic approach that considers both basic elements and their relationships in cloud data center. First, we present a formal language to describe the cloud data center, and a job scheduling net is proposed to formally model the basic elements such as user request, Web portal, data center, and server. Second, we minimize the total cost of the cloud data center by considering the multidimensional resource and local electricity price on the basis of the state space of constructed model. The dynamic job scheduling algorithm and its specific execution steps are proposed based on the alternating direction method of multipliers algorithm. Third, the operational semantics and related theories of Petri nets for establishing the correctness of our proposed method are presented. Finally, a series of simulations are performed to illustrate that the proposed method can guarantee the correct behavior of job scheduling in the cloud data center while meeting the required cost.

Summary Cloud computing has attracted much attention recently in both industry field and academic research area. More and more Internet applications are moving to the cloud environment. However, it is difficult to construct perfectly secure mechanisms facing up with complex and various attacks in cloud computing, the efficient attack‐defense strategy is highly demanded. In this paper, a stochastic game model is proposed based on combining stochastic Petri nets with game theory, which is used to describe the attack‐defense behaviors in cloud computing. The physical machine, attack‐defense behavior, and their attributes are also modeled by stochastic game model thus forming the attack‐defense game model of cloud computing. On this basis, the Nash equilibrium of attack‐defense process in physical machine is computed to get the optimal defense strategy. The related theories of Petri nets and the reachable states of attack‐defense game model are used to formally verify the correctness and effectiveness of the proposed method. The enforcement algorithm is proposed to make cloud computing dynamically evaluate and select the defense strategy to against attack behavior as quickly as possible. Both case study and simulation results show that the proposed method can adapt quickly to the changes in cloud application thus improving the security of cloud computing.

Cloud computing has attracted much interest recently from both industry and academic. However, it is difficult to model and analyze cost and utilization based task scheduling due to complex and heterogeneous environment in cloud computing. In this paper, we propose the systematic approach to modeling and analyzing cost and utilization based task scheduling for cloud application. A Game theory based task scheduling scheme is proposed based on analyzing the factors affecting the cost and utilization of virtual machine. Then a formal description language is presented to model the different components of cloud application. The backward induction game algorithm is proposed to ensure that cloud application can dynamically meet the customers' request while improving the utilization of virtual machine. The operational semantics and related theories of Petri nets help establish the correctness of our proposed method. Finally, a series of simulations are performed to evaluate the efficiency of our proposed approach.

In this paper, we propose a method to model and analyze cost-aware fault tolerant strategy for cloud computing.First, Petri nets are used to describe the structure of cloud computing, including component, cloud service and cloud application, thus forming the fault tolerant model of cloud computing.Second, a dynamic fault tolerant strategy is proposed, which can dynamically make fault tolerant strategy with the lowest cost based on the current state and failed component.Third, we present operational semantics and related theories of Petri nets for establishing the correctness of our proposed method.We have also performed a series of simulations to evaluate our proposed approach.Results show that it can help reveal the structural and behavioral characteristics of cloud computing, and reduce the fault tolerant cost.

Cloud computing has become an important, useful paradigm for building applications with cloud services. However, cloud services exist in heterogeneous environments on the Internet. It is challenging to guarantee the reliability of cloud applications. Although there are efforts studying cloud and grid service reliability, very few have considered the modeling and analysis of the reliability of cloud applications. To address this emerging, important problem, we propose the first systematic approach that considers both cloud application elements and their running environment so as to faithfully model the dynamics of cloud computing. First, we present a formal description language to model the different components of a cloud application, and use it to analyze the static and dynamic factors affecting the reliability of cloud applications. Second, we propose reliability assurance strategies to ensure that cloud applications dynamically meet their required reliability. Third, Computation Tree Logic (CTL) is used to convert the reliability assurance strategy into the CTL formulas. We present operational semantics and related theories of Petri nets for establishing the correctness of our proposed method. Finally, a series of simulations are performed to evaluate the efficiency of our proposed approach.

Cloud computing has attracted much interest recently from both industry and academia. However, the scale and highly dynamic nature of cloud application imposes significant new challenges to resource management, and efficient resource scheduling schemes are highly demanded. In this paper, we propose a systematic method to address the reliability, running time, and failure processing of resource scheduling in cloud computing. A reflection mechanism is used to abstract the resource scheduling process as a metaobject. Petri nets are used to construct the base layer model, meta layer model, metaobject protocol, and other components, thus forming the resource scheduling model. The adaptive resource scheduling strategy is converted into CTL formulas, and the properties are analyzed. Meanwhile, an enforcement algorithm is proposed, which can guarantee the correct behavior of cloud computing while meeting the required reliability within deadline constraints. The operational semantics and related theories of Petri nets help prove its effectiveness and correctness. We have also performed a series of simulations to evaluate our approach. Results show that it can help reveal the structural and behavioral characteristics of cloud computing and improve the efficiency of resource management.

The suspended particle size distribution (PSD) provides crucial information for the study of water environment. Based on the in situ data from wet and dry season in 2008-2011, the paper studied the temporal and spatial characteristics of PSD in Poyang Lake and its influence on water optical properties. The median particle size (Dv50) shows more significant variation in dry season between different districts than in wet season. The negative correlation between Dv50and mass-specific absorption coefficient of total suspended particles (ap*(400)) denotes that inorganic particles also has “package effect”. Good correlationship is found between spectral slope of PSD and spectral slope of attenuation coefficient and scattering coefficient. Compared to mass concentration, the particle size information can better explain the variability of scattering properties. Furthermore, a retrieval model for cross-sectional area concentration is established by remote sensing reflectance ratio to provide more information about particle size in Poyang Lake.

Hydrodynamic process in the largest freshwater lake in China, Poyang Lake from 2009 to 2011 was simulated by using the lake hydrodynamic model, EFDC. Results showed that simulated daily average water elevation and discharges consisted with in-situ observations. To better evaluate the model accuracy and solve the problem of field measurements deficiency, remote sensing data were employed to validate and analyze the model practicability more comprehensively. The water surface height and inundation area measured by Radar altimeter ENVISAT RA-2 and MODIS images correlated well with the model predictions, which indicated that the frequently changing dynamics of water in Poyang Lake could be effectively revealed by using hydrodynamic model. Furthermore, remote sensing techniques also provided theory for long-term validation from different perspectives.

In this paper, Petri nets are used to model the different components of service composition, and form the energy consumption model of service composition based on the relationship between components, Agent is also introduced in the energy consumption management process.Then, an adaptive energy consumption strategy are proposed to dynamically ensure that service composition can get the lowest energy consumption.The operational semantics and related theories of Petri nets help establish the correctness of our proposed method.We have also performed two simulations to evaluate our proposed approach.Results show that it can help reveal the structural and behavioral characteristics of energy consumption in service composition.

Service composition is an important means for integrating the individual Web services for creating new value added systems. However, Web service exists in the heterogeneous environments on the Internet, thus it is challenging to guarantee the reliability of composite service evolution. To address this problem, we propose the approach to modeling and analyzing the reliability of composite service evolution. First, we present a formal description language to model the different components of service composition, and use it to analyze the reliability of composite service evolution. Second, we propose an evolution mechanism to ensure that service composition can dynamically meet the required reliability. Third, we present the operational semantics and related theories of Petri nets for establishing the consistency in the evolution process. We have also performed a series of simulations to evaluate our proposed method. Results show that it can help reveal the structural and behavioral characteristics of service composition, and improve the reliability of composite service evolution.

Service composition is an important means for integrating the individual Web services for creating new value added systems. However, Web service runs in the heterogeneous environments on the Internet, it is difficult to guarantee the reliability of service composition. To address this problem, we propose a systematic method to model and analyze reliability for service composition. First, we present a formal description language to model the different components of service composition, and use it to analyze the reliability of service composition. Second, we propose reliability assurance strategy to ensure that service composition dynamically meet the required reliability. Third, we present operational semantics and related theories of Petri nets for establishing the correctness of our proposed method. We have also performed a series of simulations to evaluate our proposed approach. Results show that the method can help reveal the structural and behavioral characteristics of service composition, and improve the reliability of service composition.

Service composition is an important means for integrating the individual Web services to create new value added systems. However, because highly dynamic nature of service composition poses new challenges to resource management, efficient resource scheduling schemes are highly demanded. In this paper, a hierarchal service scheduling net is proposed to model different components of service composition, queuing theory is used to describe the competition process of available service, thus forming the scheduling model of service composition. On this basis, the evaluation function and resource scheduling strategy of service composition are proposed by considering the preference, the price and response time of available service. The related theories of Petri net are used to formally verify the correctness of proposed method. Both case study and simulation results show that the method can optimize the resource scheduling process of service composition, which has the merits of rich expressivity, while improving the performance.

Software testing is an important part of software maintenance, but it can also be very expensive. To reduce this expense, software testers may select part of their test cases so that those that are more important are run earlier in the testing process. However, the methods that can be used to select test cases for service composition and its analysis are still lacking at present. This paper proposes an aspect orientation based test case selection strategy for service composition. Aspect-orientation is used to weave testing crosscutting concerns of service composition, which includes component testing concern and testing concern of service composition, the weaving mechanism dynamically integrates these schemas into a testing enforcement model. Based on this, the test cases selection strategy for service composition is given, and abstract it as a crosscutting concern to weave into testing model, the corresponding enforcement algorithm is also given, the operation semantics and related theories of Petri nets help prove its effectiveness and feasibility. A case study explains the testing process of service composition, and a series of experiments are done to explain that the use of aspects for testing Web service is more efficient than conventional techniques, which can improve the testing quality and efficiency.

Cloud computing has attracted much interest recently from both industry and academic. More and more Internet applications are moving to the cloud environment. However, fault detection technique in cloud application is a crucial issue. This issue is especially difficult since cloud computing relies by nature on a highly dynamic environment. In this paper, we propose a model based Byzantine fault detection technique for cloud computing. A cloud computing fault net (CFN) is used to precisely model the different components of cloud computing, such as service resources, cloud module, the detection and failure process, etc, the basic properties of the constructed model are analyzed. Based on this, the fault detection strategy is proposed, which can dynamically detect the fault of cloud application in the execution process. The operational semantics and related theories of Petri nets help prove its effectiveness and correctness. An example is used to simulate the modeling and analyzing process, and a series of experiments are done to explain the effectiveness of proposed method.

Service composition is an important means for integrating the individual Web services to create new value added systems that can satisfy complex requirements. However, it is a challenge to enforce fault diagnosis mechanism for those applications due to the uncertainty of service quality in distributive and heterogeneous environment. In this paper, a Byzantine fault diagnosis method for service composition based on Petri nets is proposed. The reliability of service are taken into account for the appropriate selection of required services. And a service composition fault net (SCFN) is proposed, which can be used to model different components of service composition. Finally, the fault detection strategy is provided for processing fault of service composition in dynamic environment. Theories of Petri nets help prove its correctness and effectiveness, thus guarantee the reliability of service composition. A case study illustrates the applicability of proposed method, and its feasibility has been demonstrated by simulation.

Service composition is an important means for integrating the individual Web services to create new value added systems that can satisfy complex requirements. However, it is a challenge to analyze security requirements for those applications due to the uncertainty factors in distributive environment. This paper proposes an approach to modeling and analyzing security requirements of service composition. Petri nets are used to model the different components of service composition, the dynamic matching strategy of service composition is proposed. Aspect-orientation is used to weave the security requirements into service composition, which includes evaluation concern, authorization, security level outputting and access outputting. The operation semantics and related theories of Petri nets help prove its effectiveness and correctness. An example explains the modeling and analyzing process of service composition, and a series of experiments are done to explain that the use of aspects for analyzing security requirements of service composition is more efficient than conventional techniques.

Service composition is an effective way to build complex Web service applications. However, it is a challenge to handle failure recovery due to the uncertainty of service in distributed and heterogeneous environment. This paper proposes an approach to handling failure recovery in service composition. Petri nets are used to model the different components of service composition, failure recovery rules and service selection strategies are given. Based on these, aspect-orientation is used to weave failure recovery concern into service composition, which includes failure warning concern, service selection concern and recovery concern, the weaving mechanism dynamically integrates these schemas into a failure recovery model. The operation semantics and related theories of Petri nets help prove its effectiveness and correctness. A case study and experimental results demonstrate the approach can simplify the failure recovery process, and improve the design quality of service composition.

Service composition is an effective way to achieve value-added service, which has found wide application in various key areas. However, most access control techniques for service composition were in ad hoc fashion and fell short in precise notations. In this paper, we propose a certificate driven access control strategy for service composition. Petri nets are used to precisely define and model the different components of service composition. The access control strategy for service composition are proposed, which can dynamically adjust available service to meet the actual requirements. Based on this, theories of Petri nets help prove correctness of the access control strategy and the enforcement algorithm is given, thereby getting the service composition which can meet the functional requirements while meets the required security. The proposed method is applied to a real-world domain to show the feasibility and effectiveness.

Service composition is an effective way to achieve value-added service, which has found wide application in software system. Fault handling is critical to achieve high reliability for these applications. However, the existing service composition methods seldom consider services' fault handling, which results in high risk of runtime failure. This paper proposes a formal aspect-oriented approach to designing and analyzing fault of service composition. The underlying formalism is Petri net and its corresponding modeling method. The fault handling process is encapsulated into aspect net and base net, and Petri net is used to model the core concerns and crosscutting concerns, the weaving mechanism systematically integrates these schemas into a complete service composition model. Based on the model, the related theories of Petri net help prove the correctness of fault handling. Finally, an Export Service and simulation results show that our method can ensure the high reliability and design quantity of service composition.

Service composition is an effective way to achieve value-added service, which has found wide application in various areas. security design at architecture level is critical to achieve high assurance for these applications. However, most security design techniques for service composition were in ad hoc fashion and fell short in precise notations. This paper proposes a formal aspect-oriented approach to designing and analyzing secure service composition. The underlying formalism is Petri net and its modeling method, and focuses on the service authorization, implementation trace ability, data protection and fault handling. Aspect specification provides means to observe behaviors of basic aspect schema, and to describe their interrelationship, while the weaving mechanism systematically integrates these schemas into a complete service composition model. Based on this, the security and fault recovery mechanism of service composition are analyzed, and its correctness and effectiveness are proved. A case study of Export Service demonstrates the approach can simplify the modeling process and improve the design quality.

Reliability is a key issue of the service-oriented architecture (SOA) that is widely employed in distributed systems such as e-commerce and e-government. Redundancy based technologies are usually employed for building reliable service composition on top of unreliable Web services. This paper proposes a strategy for modeling and analyzing fault tolerant service composition. The strategy consists of service selection mechanism, service synchronization mechanism and task exception mechanism. Petri nets are used to construct different components of service composition. Once the model is constructed, theories of Petri nets help prove the consistency of processing states and reliability of the strategy. The corresponding enforcement method for constructing fault-tolerant service composition is proposed. Experiments are conducted to demonstrate the applicability and effectiveness of the fault tolerant strategy.

Process of service composition is complex and error-prone, which makes a formal modeling and analysis method highly desirable. This paper presents a Petri net-based approach to analyzing the soundness and compositionality of services in BPEL. A set of translation rules is proposed to transform BPEL processes into Petri nets, by which behaviors of the BPEL processes are articulated. The instantiation net of target services are used to capture all of the possible implementation flows of composition processes. Based on theories of Petri nets, the principles for analyzing soundness and compositionality of Web services are provided. A detailed example is given to demonstrate the applicability of our method.