Route planning algorithms, as a core technology of intelligent and connected vehicles (ICVs), significantly enhance road safety and traffic efficiency. Fusing traffic data and edge server information into route planning algorithms enables ICVs to avoid road accidents and enhances their access to low-latency computational resources. However, developing a route planning approach for ICVs faces two major challenges: (i) learning anomaly distributions from traffic data is complicated due to the scarcity of anomaly datasets, and (ii) the large-scale of routes complicates the evaluation of computational resources. In this article, we propose a route planning approach with traffic data and edge server (RP-TDES) to address these above challenges. Specifically, the anomaly detection module of the RP-TDES approach integrates a generator that mines spatiotemporal features and a discriminator based on similarity measures. Both components are optimized through adversarial training to address the scarcity of anomalous events. In addition, to address the challenge of evaluating the computational resources for a entire route, we propose an evaluation method that considers the vehicle's mobility characteristics. Finally, we validate the effectiveness of our approach on both real-world and synthetic road networks, and the experiment results show that our approach outperforms the baseline in route planning in terms of vehicle travel time and edge service capability. Meanwhile, experiment results demonstrate that our anomaly detection model also outperforms baseline methods for accident detection.

Microservice is a popular architecture to construct applications from a set of small independent services in cloud environment, leading to high cohesion, high availability, low coupling, and decent scalability. Due to large number of independent services in a microservice system, system faults generated from a single service would propagate to multiple services, eventually degraded the overall system performance and Quality of Service (QoS). Thus, it is crucial to efficiently and autonomously diagnose the runtime system fault. However, the complexity and dynamism of microservice systems and cloud environment pose unique challenges to precisely and robustly identify the faults and localize the root causes. In this paper, we propose an Autonomous Model Selection-Ensemble-Stacking (AMSES) framework for microservice system fault identification. The proposed framework can automatically select, ensemble, and stack optimal models from candidate unsupervised detection models for identifying different fault types robustly. In addition, AMSES can adaptively localize the fault services using autoselected root cause localization model. Moreover, by exploiting the fault degree and causal inferring score, we can diagnose the detected system fault precisely and interpretably. To evaluate the effectiveness, we empirically compare AMSES with state-of-the-art models on three kinds of faults on two microservice benchmarks: Sock-Shop and Train-Ticket. The experimental results show that AMSES can achieve$\mathbf{8 7. 1 \%}$and$\mathbf{9 1. 4 \%}$macroF1 average for fault type identification on Sock-Shop and TrainTicket, respectively. Meanwhile, AMSES could outperform its competitors for root cause localization with an average Avg@5 of 0.856 on Sock-Shop and 0.633 on Train-Ticket.

With the rapid evolution of artificial intelligence (AI) technologies, supporting computing-intensive and latencysensitive applications in resource-constrained environments has become increasingly challenging. In response, we propose APPOMNLS, a multi-objective optimization approach for Satellite Edge Computing (SEC) that targets application response latency, energy consumption, and on-time completion rates. It integrates a proximal policy optimization (PPO) with a self-attention mechanism under a multi-neighborhood local search framework. The Transformer-based self-attention module enhances the PPO network's representational capability, while multi-neighborhood local search switches flexibly between global and local exploration of the solution space. Experiments based on Iridium-NEXT constellation Two-Line Element (TLE) data demonstrate that our approach clearly outperforms its peers in terms of terminal response speed, energy efficiency and on-time application completion rates. APPO-MNLS brings value to SEC with the capability of guaranteeing reliable and effective global satellite network service.

The rapid evolution of vehicular edge computing (VEC) poses critical challenges in distributed caching resource management, particularly in reducing content retrieval latency and improving cache utilization efficiency. We propose a community-aware caching framework tailored for VEC scenarios, comprising two main components: a Dynamic Thematic-Community Clustering (DTCC) algorithm based on Collapsed Gibbs sampling and a Potential Game-based Caching Optimization (GCO) strategy. The DTCC algorithm captures the temporal evolution of vehicular social networks, facilitating dynamic community partitioning and topic distribution extraction. Meanwhile, GCO formulates the caching decisions of vehicles and base stations as a non-cooperative game, whose community-aware utility function design guarantees both the existence and convergence of a Nash equilibrium. Extensive experiments on real-world datasets demonstrate that GCO consistently outperforms state-of-the-art baselines across diverse performance metrics, further validating its efficacy compared with existing caching solutions.

As massive data is generated by Internet of Things (IoT) devices, user-end devices are required to implement computation-intensive functionalities, including multi-sensory data processing and analysis, sophisticated system control schemes, and artificial intelligence. Mobile Edge Computing (MEC) is a significant technology that has the potential to extend the computation and storage capacities of user-end devices by the decentralization of required resources and contents near users and at the edge. A crucial challenge in this direction is the development of a smart mechanism to effectively cache contents upon Edge Servers (ESs) near users for high effectiveness and low latency of content delivery with the constraints on computational and storage capacities of ESs. This study employs a queuing model for analyzing total request delay and interprets the content caching problem as an adversarial semi-bandits problem. We propose an Online Self-feedback Adversarial Semi-bandits Learning (OSAL) algorithm that incorporates a dual-layer learning architecture for dynamically generating caching strategies and maximizes the long-term reward. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art methods across various performance metrics in a real-world multi mobile-user content caching case.

Mobile edge computing (MEC) is an evolving paradigm for rendering services through network-accessible resources deployed over Internet of Things (IoT) nodes at the edge. Nevertheless, an MEC environment usually employs thousands of physical machines connected via hundreds of switches/routers that communicate and coordinate to deliver computing service. In such complicated systems, faults caused by software, human errors, and hardware are often unavoidable. The edge of network presents a dynamic environment with great quantities of terminals, high mobility of mobile devices, heterogeneous applications, and intermittent traffic. In such an environment, MEC can suffer from unbalanced resource provisioning and interruptions of faults occurring at different levels, which further causes task faults and affects service quality. To address this challenge, this work proposes a novel fault-tolerant offloading method for handling faults by leveraging a reinforcement-learning-based service offloading decision model. The model synthesizes a Dueling Deep Q Network (DQN)-based algorithm for deciding user offloading behaviors and an adaptive checkpointing method for improving task execution reliability. For the purpose of model validation and comparison, extensive simulations are conducted. Numerical results clearly demonstrate that the proposed method is highly effective and outperforms existing methods.

Extracting local variable refactoring is frequently employed to replace one or more occurrences of a complex expression with simple accesses to a newly introduced variable. To facilitate refactoring, most IDEs can automate the extract local variable refactorings when the to-be-extracted expressions are selected by developers. However, refactoring tools usually replace all expressions that are lexically identical to the selected one without a comprehensive analysis of the safety of the refactoring. The automatically conducted refactorings may lead to serious software defects. Besides that, existing refactoring tools rely heavily on software developers to spot to-be-extracted expressions although it is often challenging for inexperienced developers and maintainers to make the selection. To this end, in this article, we propose an automated approach, called ValExtractor+ , to recommending extract local variable refactoring opportunities and to automatically and safely conduct the refactorings. ValExtractor+ is composed of two parts, i.e., solutionAdvisor and opportunityAdvisor . Given a to-be-extracted expression, solutionAdvisor leverages lightweight static source code analysis to validate potential side effects of the expression, and to identify expressions that could be extracted together with the selected expression as a single variable without changing the semantics of the program or introducing any new exceptions. The static code analysis significantly improves the safety of automated extraction of local variables. To free programmers from manually selecting to-be-extracted expressions, opportunityAdvisor leverages solutionAdvisor to automatically retrieve all expressions that could be extracted safely as well as their refactoring solutions. It then leverages a learning-based classifier to predict which of the retrieved expressions should be extracted. Evaluations on open-source applications suggest that solutionAdvisor successfully avoided all defects (more than two hundred) caused by extracting local variable refactorings conducted by Eclipse (243 defects) or IntelliJ IDEA (263 defects). Additionally, opportunityAdvisor was able to effectively recommend expressions for extraction, achieving 307 true positives (TP) and 21,121 true negatives (TN). Four pull requests from our work (PR IDs: 66, 333, 439, and 360) were successfully merged into the Eclipse community repository, showcasing the practical impact and robustness of our approach as recognized by the wider developer community.

Mobile Edge Computing (MEC) is an emerging computing paradigm that offloads cloud center functions to the edge server. In a MEC environment, edge servers' limited storage and processing capacity require selective service caching, where only a part of required content can be placed directly upon the destination edge server and the remaining at remote cloud end. A primary challenge in this context is the creation of an effective and responsive service caching algorithm that improves the Quality of Service (QoS) perceived by users while reducing operational costs. This study applies an$M$/ G /1 queuing model as the foundational framework and transforms the service caching problem as an adversarial semi-bandit problem. We propose a delay-aware Genetic-Follow-the-Regularized-Leader (GFRL) algorithm, which is capable of guiding decentralized caching decisions. Experimental results indicate that GFRL outperforms traditional methods across various performance metrics.

Mobile Edge Computing (MEC) provides users with low-latency and highly responsive services by deploying Edge Stations (ESs) close to applications. However, the limited battery life, computational power, and communication transmission ca-pabilities of mobile devices make it impractical to deploy compu-tation tasks solely on a single node. Instead, dynamic allocation of computational resources according to user mobility is considered to be an effective solution. To address the challenge, we develop a user mobility-aware and destination prediction-based method, SASpre, for edge service preallocation. Experiments demonstrate that SASpre beats its peers in terms of user coverage rates, service times, and service responsiveness.

Mobile edge computing is an emerging computing paradigm boosting resource-demanding and delay-sensitive applications through deploying computing infrastructures at the edge of the Internet nearby mobile requesters and users. In an Internet of Vehicles (IoV) environment, Vehicular Edge Computing (VEC) is capable of exploiting network edge devices, in terms of, e.g., Roadside Units (RSUs), for predictive content caching for optimizing quality-of-experience (QoE) of nearby content requesters based on content popularity analysis, it remains a great challenge to accurately predict content popularity of mobile requesters and appropriately cache required content with low miss rate accordingly in a VEC environment with high user mobility and dynamics. To address this challenge mentioned above, in this paper, we propose predictive content popularity-aware approach, i.e., KM_SVD++, to edge caching in an VEC environment. The proposed approach is capable of achieving high hit rate of mobile content requestors in VEC and low latency of content delivery by leveraging a Kalman filtering model for predicting locations of vehicles and a SVD++ one for yielding decisions for cache deployment and replacement. We conduct extensive simulations as well to prove its effectiveness.

Mobile edge computing(MEC) is an innovative technology that deploys computing resources around the demand side to provide near-request and responsiveness-guaranteed computing and storage services. A major attention paid by related works in this direction is mobility, where mobile traces of both edge users and servers are analyzed and exploited for accommodating offloading and migration requests for computation resources in a highly dynamic MEC environment. Our research in this work suggests that information of user interests, in terms of points of interest (POI), can be exploited in conjunction with mobility as well and proposes a hybrid method for for interest-informed and mobility-aware service migration path selection(HIMS). It synthesizes a trajectory prediction model and user interests prediction one for selecting target servers and reliable service migration paths. Experimental results demonstrate that our approach outperforms traditional methods across multiple performance metrics, especially those with sole input of mobility.

The growing need for applications that require significant computational power and high responsiveness has significantly driven the advancement of multi-access edge computing (MEC), with satellite edge computing (SEC) emerging as a formidable solution for regions where devices are marooned in areas with sparse computational resources. We present a study on enhancing task scheduling and resource allocation efficiency under the SEC framework, introducing a novel system model that simulates a heterogeneous network characterized by variable bandwidths, channel gains, and transmission powers. We propose a tailored SEC architecture that addresses stringent latency requirements and devise a dynamic scheduling method that adjusts task priorities based on urgency. Our experiments, grounded in realistic parameters from the Iridium and OneWeb satellite constellations, demonstrate the efficacy of our algorithm. The findings underscore significant improvements in managing the SEC landscape, providing robust solutions that enhance overall system performance and reliability in global service networks.

Platoon refers to a group of vehicles traveling in a train-like strategy with a lean inter-vehicle gap, which can increase road capacity and reduce energy consumption. A platoon is composed of several member vehicles and one leader vehicle which determines the driving pattern of the platoon. Therefore, it is crucial to select a vehicle with the highest reputation value as the leader vehicle in a platoon. Reputation value is a private parameter of each vehicle, and how to preserve its privacy is also an issue worth paying attention to. Therefore, in this paper, a reputation-based and privacy-preserving platoon management (RPPM) scheme in vehicular networks is proposed. Specifically, we design a secure comparison protocol (SCP) to select a leader vehicle for each platoon. The SCP protocol not only reduces the involvement of trust authority but also preserves the privacy of vehicles’ reputation values. Furthermore, the cloud server aggregates reputation ciphertexts and feedback scores of the member vehicles based on the homomorphism characteristic of Paillier ciphertexts, and the reputation value privacy of member vehicles is preserved without affecting the aggregation results. The theoretical analysis indicates that the RPPM scheme is privacy-preserving and secure enough to resist several common attacks in vehicular networks. Simulations are conducted to demonstrate the performance of the RPPM scheme, and the results show that the RPPM scheme significantly outperforms the existing schemes in computation and communication overheads.

Vehicle Edge Computing (VEC) is the deployment of applications close to edge servers to provide low latency and highly responsive services to users. However, due to the complexity and dynamics of the VEC environment, it is prone to errors and failures, and the reliability of edge service migration may be compromised if no measures are taken to cope with different levels of failures. To address this issue, this paper proposes an modified (m, n)-fault tolerance strategy (M-MNFT). Unlike the traditional one, which only considers ES failures, M-MNFT additionally selects redundant edge base stations to ensure task reliability during task migration, and takes into account the fact that the relative distance between the request and the base station is as small as possible when the request is sent, so as to avoid the impact of the edge base station failure on the Quality of Service (QoS) during task migration. In addition, we have performed extensive simulations to show that M-MNFT outperforms existing methods in terms of the number of delayed requests, on-time finish rate, and average waiting time.

Extract local variable is a well-known and widely used refactoring. It is frequently employed to replace one or more occurrences of a complex expression with simple accesses to a newly added variable. Although most IDEs provide tool support for extract local variables, such tools without deep analysis of the refactorings may result in semantic errors. To this end, in this paper, we propose a novel and more reliable approach, called ValExtractor, to conduct extract variable refactorings automatically. The major challenge of automated extract local variable refactorings is how to efficiently and accurately identify the side effect of the extracted expressions and the potential interaction between the extracted expressions and their contexts without time-consuming dynamic execution of the involved programs. To resolve this challenge, ValExtractor leverages a lightweight static source code analysis to validate the side effect of the selected expression, and to identify which occurrences of the selected expression could be extracted together without changing the semantics of the program or introducing potential new exceptions. Our evaluation results on open-source Java applications suggest that Eclipse and IntelliJ IDEA, the state-of-the-practice refactoring engines, resulted in a large number of faulty extract variable refactorings whereas ValExtractor successfully avoided all such errors. The proposed approach has been merged into (and distributed with) Eclipse to improve the safety of extract local variable refactoring.

Feature envy is one of the well-recognized code smells that should be removed by software refactoring. A major challenge in feature envy detection is that traditional approaches are less accurate whereas deep learning-based approaches are suffering from the lack of high-quality large-scale training data. Although existing refactoring detection tools could be employed to discover real-world feature envy examples, the noise (i.e., false positives) within the resulting data could significantly influence the quality of the training data as well as the performance of the models trained on the data. To this end, in this paper, we propose a sequence of heuristic rules and a decision tree-based classifier to filter out false positives reported by state-of-the-art refactoring detection tools. The data after filtering serve as the positive items in the requested training data. From the same subject projects, we randomly select methods that are different from positive items as negative items. With the real-world examples (both positive and negative examples), we design and train a deep learning-based binary model to predict whether a given method should be moved to a potential target class. Different from existing models, it leverages additional features, i.e., coupling between methods and classes (CBMC) and the message passing coupling between methods and classes (MCMC) that have not yet been exploited by existing approaches. Our evaluation results on real-world open-source projects suggest that the proposed approach substantially outperforms the state of the art in feature envy detection, improving precision and recall by 38.5% and 20.8%, respectively.

Mobile edge computing (MEC) is a forward-looking technology that provides services through resources to meet the needs of cloud-edge Internet of Things (IoT) devices. It provides computing and storage data facilities for IoT users and further renders services through resources in vicinity to fulfill the needs from IoT devices at the cloud edge. However, a major difficulty in guaranteeing reliable resource provisioning is mobility, which brings in chances of service migrations among difference distributed edge nodes and thus causes potential risks of service failures or disruptions. Existing solutions in this direction can be ineffective since they tend to consider that stability of inter-edge-node data transmission to be irrelevant to user mobility and are thus in lack of a comprehensive model for estimating effectiveness of migration paths selected. In this article, instead, we consider that the effectiveness of migrations paths to be selected are highly dependent on user mobility as well as inter-edge-node stability propose a novel predictive and mobile track-aware approach to fault-tolerant service migration path selection in MEC (PTSM). It is capable of exploiting uses trajectories for accurate predictions of future tracks and selecting target servers as well as migration paths with guaranteed migration reliability and performance in terms of multiple metrics. We demonstrate with extensive simulations and numerical results that our proposed method outperforms its peers in terms of migration reliability and performance.

Mobile edge computing (MEC) is a prospective technology to render services through resources to fulfill the requirements of IoT (Internet of Things) devices at the cloud edge. The highly dynamic and heterogeneous characteristics of IoT devices bring both opportunities and challenges, i.e., a higher-than-usual occurrence rate of failures. Such failures occur at all architectural levels of the IoT applications: IoT sensor and actuator nodes can be missed, network links between IoT nodes can be down, and processing and storage IoT components can fail. In this work, for optimizing service offloading efficiency, energy consumption, and system reliability, a semi-online fault-tolerant offloading method (UDQF) was proposed for countering MEC failures by adopting a semi-online-learning-based service offloading strategy. The proposed strategy leverages a Dueling Deep Q network-based algorithm to determine user offloading behavior and utilizes an adaptive checkpointing mechanism (periodically storing the system state and restarting the system at the last checkpointing) to improve the task reliability. To valid and compare the model, the simulated results indicate that the proposed method outperforms other counterparts in multiple metrics.

Pedestrian Attribute Recognition (PAR) is an indispensable topic in smart video analysis. The recognition of fine-grained attributes is challenging work as they are indistinguishable in surveillance images. In this study, we propose an Attention Auxiliary Spatial Fusion (AASF) model to improve the performance of PAR from the following two aspects: (1) We employ an Embedded Attention (EA) module to encode position information into channel information so that it can aggregate features in two different spatial directions with the small-scale visual clues. (2) We propose a Feature Pyramid Adaptive Fusion (FPAF) module to adaptively select useful features for multiple attributes from different levels with contradictory information. Extensive experiments conducted on two large public indoor and outdoor PAR datasets demonstrate that our model achieves state-of-the-art results, especially obtaining better performances on fine-grained attributes.

Abstract Quality of data services is crucial for operational large-scale internet-of-things (IoT) research data infrastructure, in particular when serving large amounts of distributed users. Effectively detecting runtime anomalies and diagnosing their root cause helps to defend against adversarial attacks, thereby essentially boosting system security and robustness of the IoT infrastructure services. However, conventional anomaly detection methods are inadequate when facing the dynamic complexities of these systems. In contrast, supervised machine learning methods are unable to exploit large amounts of data due to the unavailability of labeled data. This paper leverages popular GAN-based generative models and end-to-end one-class classification to improve unsupervised anomaly detection. A novel heterogeneous BiGAN-based anomaly detection model Heterogeneous Temporal Anomaly-reconstruction GAN (HTA-GAN) is proposed to make better use of a one-class classifier and a novel anomaly scoring function. The Generator-Encoder-Discriminator BiGAN structure can lead to practical anomaly score computation and temporal feature capturing. We empirically compare the proposed approach with several state-of-the-art anomaly detection methods on real-world datasets, anomaly benchmarks and synthetic datasets. The results show that HTA-GAN outperforms its competitors and demonstrates better robustness.

With the proliferation of novel Internet of Things (IoT) mobile applications and advanced communication technologies, nowadays we are surrounded by ubiquitous sensors and smart devices. These smart IoT devices generate a large volume of data day and night at the edge of the network, create a huge demand for edge computing resources, and thus, promote the emergence of the multiaccess edge computing (MEC) paradigm. In MEC environments, IoT devices or mobile users are allowed to offload their computational tasks to nearby edge servers to overcome the limitation of local computing resources. Though edge servers could provide low-latency service with high-responsible computing capabilities, they are still facing many challenges posed by the limited hardware resources and diverse offloading requests. However, traditional approaches are usually based on the centralized architecture and batch-processing scheduling mode, which might lead to low efficiency and high communication overhead. Besides, they also lack the consideration of task diversity and priorities, which are crucial in real-world application scenarios. Thus, smart task scheduling and resource provision strategies with a high real-time property are urgently needed for better user experience and higher resource utilization. In this article, we target the online edge IoT task scheduling and resource allocation problem and propose a decentralized approach (DoSRA). The experiments based on real-world edge environments have demonstrated that the proposed approach could achieve at most a 35.34% reduction on the average weighted offloading response time.

A mobile edge computing (MEC) paradgim is evolving as an increasingly popular means for developing and deploying smart-city-oriented applications. MEC servers can receive a great deal of requests from devices of mobile users, especially in crowded scenes, e.g., a city’s central business district and school areas. It thus remains a great challenge for appropriate scheduling and managing strategies to avoid hotspots, guarantee load-fairness among MEC servers, and maintain high resource utilization at the same time. To address this challenge, we propose a coalitional-game-based and location-aware approach to MEC service migration for mobile user reallocation in crowded scenes. Our proposed method includes: 1) dividing MEC servers into multiple coalitions according to their inter-Euclidean distance by using a modified$k$-means clustering method; 2) discovering hotspots in every coalition area and scheduling services based on their corresponding cooperations; and 3) migrating services to appropriate edge servers to achieve high utilization and load-fairness among coalition members. Experimental results based on a real-world mobile trajectory dataset for crowded scenes, and an urban-edge-server-position dataset demonstrate that our method outperforms existing ones in terms of load fairness, number of migrations, and utilization rate of edge servers.

The mobile edge computing (MEC) paradigm places traditional digital infrastructure next to mobile networks and thus drives substantial improvements in performance and latency for mobile computing cases like gaming, video streaming, and IoT. However, it remains a great challenge to provide a effictive and performance guaranteed strategies for services offloading and migration in the MEC environment. Most existing solutions in this direction tend to consider task offloading as a offline decision making process by employing transient positions of users as model inputs. In this work instead, we consider a predictive-trajectory-aware task offloading strategy called PreMig. Simulations clearly demonstrate that our proposed strategy outperforms traditional ones in terms of effective service rate and migration overhead.

With more Graph Convolutional Network (GCN) models applied to recommendation tasks, graph data of tremendous scalability becomes much harder to train for existing models. Many existing works of adapting GCN models to recommendation tasks often try to improve accuracy by aggregating messages from all high-order neighboring nodes, which can result in insufficient computational efficiency. To tackle with data with large scalability, we propose a novel recommendation algorithm—Rec-clusterGCN, which utilizes the cluster structure of graphs, and is suitable for SGD-based training. It goes as follows: at every step, a dense subgraph of user-item interaction will be constructed, and each node will only aggregate messages from neighboring nodes within the subgraph. Next, our model accepts the LightGCN model structure. It can reduce a huge amount of computational time cost and make training of larger graph possible without sacrificing too much accuracy. In addition, by using our node enhancement technique, the performance of Rec-clusterGCN is further improved. The experimental results also indicate that the proposed algorithm outperforms most baseline algorithms. Significant improvement has been made in computational efficiency (on average about 30.0% relative improvement for different layers in the Gowalla dataset).

Protein-protein interactions (PPIs) play a critical role in cellular activities. However, discover them experimentally is exhausted. How to predict the missing PPIs with the known PPI networks (PPINs) is a challenging task considering their large scale and extreme sparsity. To utilize the known data efficiently, this work proposes a Highly-Confident Protein Interactome Prediction (HPIP) model with two-fold ideas: a) employing a variational autoencoder model based on Gaussian distribution as a basic PPI predictor for processing the sparse input PPIN; b) embedding the basic PPI predictor into an elastic architecture which based on a block and probability summing strategy. Experimental results on two real world PPINs from the STRING database indicate that HPIP can effectively predict PPIs with high confidence.

This paper aims to analyze the influence of different driving behavior on multilane traffic flows, via extending the well-studied single-lane car-following model into a three-lane model. To this end, we classify all drivers’ characteristics into three types: calm, moderate and aggressive, in accordance with their differences in response coefficient, expectation safe distance, maximum speed and lane-changing intention. Based on Kerner’s three-phase traffic theory, our model can reproduce the empirical features of spontaneous traffic breakdown phenomena, revealing the mechanism of congestion formation and state transitions. In addition, we examine how the traffic accident, honking and fast-slow lanes could affect the heterogeneous traffic flows. Numerical experiments show that the drivers’ characteristics play an essential role in stabilizing the traffic flow on multilane roads. The results may contribute to traffic planning and control on the urban roadways.

Recently, the vehicular edge computing (VEC) paradigm becoming an emerging solution for offloading computation-intensive tasks in the vehicular environment. However, pure edge resources can be limited and insufficient when vehicles and users are in great numbers. Thus, intelligent and efficient task deployment strategies for hybrid and layered edge infrastructures are in high need. In this paper, we propose a novel deployment approach for vehicular applications with multiple interdependent tasks in a hybrid three-layer edge computing infrastructure. We consider that each application can be divided into multiple interdependent tasks, and tasks can be deployed to different layers for execution. We propose an efficient multiple tasks deploying algorithm (MTDA) for yielding high-quality deployment solutions through prioritizing applications for meeting deadline constraints and tasks for meeting dependency constraints and simulative results clearly demonstrate that our proposed method outperforms traditional ones in terms of average application completion time and deadline meeting rate.

An opportunistic link between two mobile devices or nodes can be constructed when they are within each other’s communication range. Typically, cyber–physical environments consist of a number of mobile devices that are potentially able to establish opportunistic contacts and serve mobile applications in a cost-effective way. Opportunistic mobile service computing is a promising paradigm capable of utilizing the pervasive mobile computational resources around the users. Mobile users are thus allowed to exploit nearby mobile services to boost their computing capabilities without investment in their resource pool. Nevertheless, various challenges, especially its quality-of-service and reliability-aware scheduling, are yet to be addressed. Existing studies and related scheduling strategies consider mobile users to be fully stable and available. In this article, we propose a novel method for reliability-aware and deadline-constrained service composition over opportunistic networks. We leverage the Krill–Herd-based algorithm to yield a deadline-constrained, reliability-aware, and well-executable service composition schedule based on the estimation of completion time and reliability of schedule candidates. We carry out extensive case studies based on some well-known mobile service composition templates and a real-world opportunistic contact data set. The comparison results suggest that the proposed approach outperforms existing ones in terms of success rate and completion time of composed services.Note to Practitioners—Recently, the rapid development of mobile devices and mobile communication leads to the prosperity of mobile service computing. Services running on mobile devices within a limited range are allowed to be composed to coordinate through wireless communication technologies and perform complex tasks and business processes. Despite its great potential, mobile service compositions remains a challenge since the mobility of users and devices imposes high unpredictability on the execution of tasks. A careful investigation into existing methods has found their various limitations, e.g., assuming time-invariant availability of mobile services. This article presents a novel reliability-aware and deadline-constrained service composition method for mobile opportunistic networks. Instead of assuming time-invariant availability of mobile nodes, the proposed method is capable of estimating service availability at run-time and leveraging a Krill–Herd-based algorithm to yield the deadline-constrained, reliability-aware, and well-executable service composition schedules. Case studies based on well-known service composition templates and real-world data sets suggest that it outperforms traditional ones in terms of success and completion time of composed services. It can thus aid the design and optimization of composite services as well as their smooth execution in a mobile environment. It can help practitioners better manage the reliability and performance of real-world applications built upon mobile services.

Most existing researches on cloud workflow systems have focused on resource scheduling with the aims to minimize system delay under budget constraints or optimize system cost under deadline constraints. However, cloud providers cannot guarantee a failure-free cloud environment, a compact scheduling plan is prone to failure, thus, workflow system reliability has been identified as a critical and challenging issue in the volatile cloud environment. With the ability of cloud, it is easy for users to implement the active fault tolerance schemes, e.g., Scale-Out. However, it will lead to issues like security problem and extra management cost. In this paper, we first investigate Scale-Up and Scale-Hybrid schemes to fully explore the possibilities offered by the ability of cloud. We formally model the problem of optimizing the reliability of a cloud workflow system under budget constraints with these three fault-tolerance schemes. These optimization problems are discrete and non-convex. Thus, we propose a genetic algorithm based method for workflow fault tolerance (GA4WFT). Finally, we evaluate the effectiveness and efficiency of proposed GA4WFT with three different fault-tolerance schemes through experiments conducted on Amazon EC2 data.

Edge computing is a promising paradigm that can boost the performance of novel mobile applications and energize the real-time governance of Internet-of-Things (IoT) big data. In edge computing, mobile application vendors are allowed to employ edge resources to speed up end-users' applications in an elastic and on-demand manner. However, due to the complex geographical distribution of edge servers and users, how to decide the most appropriate destination edge server to hire and how to decide the corresponding user-server allocation plan with as-low-as-possible monetary cost are the key problems for application vendors. Instead of assuming a simultaneous-batch-arrival pattern of incoming users and considering static optimization of the Edge User Allocation (EUA) problem by most existing studies, in this paper, we consider an online EUA problem where users' arrival and departure follow a general pattern. We take the long-term edge user allocation rate and edge server leasing cost as scheduling targets and propose a decentralized collaborative and fuzzy-control-based approach to yielding real-time user-edge-server allocation schedules. In this approach, edge users are allowed to independently make their own allocation decision only based on local information (i.e., the status of nearby edge servers). Experiments on real-world edge datasets demonstrate our approach outperforms state-of-the-art approaches in terms of long-term allocation rate and system cost.

Recently, mobile edge computing (MEC) is widely believed to be a promising and powerful paradigm for bringing enterprise applications closer to data sources such as IoT devices or local edge servers. It is capable of energizing novel mobile applications, especially the ultra-latency-sensitive ones, by providing powerful local computing capabilities and lower end-to-end delays. Nevertheless, various challenges, especially the reliability-guaranteed scheduling of multitask business processes in terms of, e.g., workflows, upon distributed edge resources and servers, are yet to be carefully addressed. In this paper, we propose a novel edge-environment-based multi-workflow scheduling method, which incorporates a reliability estimation model for edge-workflows and a coevolutionary algorithm for yielding scheduling decisions. The proposed approach aims at maximizing the reliability, in terms of success rates, of services deployed upon edge infrastructures while minimizing service invocation cost for users. We conduct simulative experimental case studies based on multiple well-known scientific workflow templates and a well-known dataset of edge resource locations as well. Simulative results clearly suggest that our proposed approach outperforms traditional ones in terms of workflow success rate and monetary cost.

Cloud computing has nowadays become a dominant technology to reduce the computation cost by elastically providing resources to users on a pay-per-use basis. More and more scientific and business applications represented by workflows have been moved or are in active transition to cloud platforms. Therefore, efficient cloud workflow scheduling methods are in high demand. This paper investigates how to simultaneously optimize makespan and economical cost for workflow scheduling in clouds and proposes a multiobjective evolutionary list scheduling (MOELS) algorithm to address it. It embeds the classic list scheduling into a powerful multiobjective evolutionary algorithm (MOEA): a genome is represented by a scheduling sequence and a preference weight and is interpreted to a scheduling solution via a specifically designed list scheduling heuristic, and the genomes in the population are evolved through tailored genetic operators. The simulation experiments with the real-world data show that MOELS outperforms some state-of-the-art methods as it can always achieve a higher hypervolume (HV) value. Note to Practitioners-This paper describes a novel method called MOELS for minimizing both costs and makespan when deploying a workflow into a cloud datacenter. MOELS seamlessly combines a list scheduling heuristic and an evolutionary algorithm to have complementary advantages. It is compared with two state-of-the-art algorithms MOHEFT (multiobjective heterogeneous earliest finish time) and EMS-C (evolutionary multiobjective scheduling for cloud) in the simulation experiments. The results show that the average hypervolume value from MOELS is 3.42% higher than that of MOHEFT, and 2.27% higher than that of EMS-C. The runtime that MOELS requires rises moderately as a workflow size increases.

The rapid development of mobile communication technologies prompts the emergence of mobile edge computing (MEC). As the key technology toward 5th generation (5G) wireless networks, it allows mobile users to offload their computational tasks to nearby servers deployed in base stations to alleviate the shortage of mobile resource. Nevertheless, various challenges, especially the edge-user-allocation problem, are yet to be properly addressed. Traditional studies consider this problem as a static global optimization problem where user positions are considered to be time-invariant and user-mobility-related information is not fully exploited. In reality, however, edge users are usually with high mobility and time-varying positions, which usually result in users reallocations among different base stations and impact on user-perceived quality-of-service (QoS). To overcome the above limitations, we consider the edge user allocation problem as an online decision-making and evolvable process and develop a mobility-aware and migration-enabled approach, named MobMig, for allocating users at real-time. Experiments based on real-world MEC dataset clearly demonstrate that our approach achieves higher user coverage rate and lower reallocations than traditional ones.

High-dimensional and sparse (HiDS) matrices are the basic inputs of recommender systems. Recently, autoencoder-based approaches to analyzing HiDS matrices from recommender systems are becoming increasingly popular, owing to their good representative learning ability and scalability. However, traditional autoencoder-based approaches can usually bring great computational burden on HiDS data due to its frequent manipulations of large latent factor (LF) matrices data. To address this issue, we propose a Fast Autoencoder (FAE)-based recommender. It is capable of manipulating single LFs rather than LF matrices with improved computational efficiency. Moreover, we consider a Hogwild!-based parallelization mechanism for further accelerating its training efficiciency of the proposed recommender. Experimental results show that our proposed method considerably outperform traditional ones, e.g., the classical Autoencoder-based recommenders, in terms of recommendation accuracy and computational efficiency.

Accurately estimating unknown quality-of-service (QoS) data based on historical records of Web-service invocations is vital for automatic service selection. This work presents an effective scheme for addressing this issue via alternating direction method-based matrix factorization. Its main idea consists of a) adopting the principle of the alternating direction method to decompose the task of building a matrix factorization-based QoS-estimator into small subtasks, where each one trains a subset of desired parameters based on the latest status of the whole parameter set; b) building an ensemble of diversified single models with sophisticated diversifying and aggregating mechanism; and c) parallelizing the construction process of the ensemble to drastically reduce the time cost. Experimental results on two industrial QoS datasets demonstrate that with the proposed scheme, more accurate QoS estimates can be achieved than its peers with comparable computing time with the help of its practical parallelization.

Energy efficiency has become one of the major concerns for today's cloud datacenters. Dynamic virtual machine (VM) consolidation is a promising approach for improving the resource utilization and energy efficiency of datacenters. However, the live migration technology that VM consolidation relies on is costly in itself, and this migration cost is usually heterogeneous as well as the datacenter. This paper investigates the following bi-objective optimization problem: how to pay limited migration costs to save as much energy as possible via dynamic VM consolidation in a heterogeneous cloud datacenter. To capture these two conflicting objectives, a consolidation score function is designed for an overall evaluation on the basis of a migration cost estimation method and an upper bound estimation method for maximal saved power. To optimize the consolidation score, a greedy heuristic and a swap operation are introduced, and an improved grouping genetic algorithm (IGGA) based on them is proposed. Lastly, empirical studies are performed, and the evaluation results show that IGGA outperforms existing VM consolidation methods.

When a single service on its own cannot fulfill a sophisticated application, a composition of services is required. Existing methods mostly use a fixed-price scheme for service pricing and determine service allocation for composition based on a first-price auction. However, in a dynamic service market, it is difficult for service providers to determine a fixed price that is profitable while attractive to customers. Meanwhile, this mechanism cannot ensure that the providers who require the least cost to provide services would win the auction, because the pricing strategy of service providers is unpredictable. To address such issues, in this paper, we propose Vickrey-Clarke-Groves auction-based dynamic pricing for a generalized service composition. We consider fine-grained services as candidates for composition as well as coarse-grained ones. In our approach, service providers bid for services of different granularities in the composite service and based on received bids, a user decides a composition that minimizes the social cost while meeting quality constraints. Experimental results at last verify the feasibility and effectiveness of the proposed approach.

Generating highly accurate predictions for missing quality-of-service (QoS) data is an important issue. Latent factor (LF)-based QoS-predictors have proven to be effective in dealing with it. However, they are based on first-order solvers that cannot well address their target problem that is inherently bilinear and nonconvex, thereby leaving a significant opportunity for accuracy improvement. This paper proposes to incorporate an efficient second-order solver into them to raise their accuracy. To do so, we adopt the principle of Hessian-free optimization and successfully avoid the direct manipulation of a Hessian matrix, by employing the efficiently obtainable product between its Gauss-Newton approximation and an arbitrary vector. Thus, the second-order information is innovatively integrated into them. Experimental results on two industrial QoS datasets indicate that compared with the state-of-the-art predictors, the newly proposed one achieves significantly higher prediction accuracy at the expense of affordable computational burden. Hence, it is especially suitable for industrial applications requiring high prediction accuracy of unknown QoS data.

Nowadays it is becoming more and more attractive to execute workflow applications in the cloud because it enables workflow applications to use computing resources on demand. Meanwhile, it also challenges traditional workflow scheduling algorithms that only concentrate on optimizing the execution time. This paper investigates how to minimize execution cost of a workflow in clouds under a deadline constraint and proposes a metaheuristic algorithm L-ACO as well as a simple heuristic ProLiS. ProLiS distributes the deadline to each task, proportionally to a novel definition of probabilistic upward rank, and follows a two-step list scheduling methodology: rank tasks and sequentially allocates each task a service which meets the sub-deadline and minimizes the cost. L-ACO employs ant colony optimization to carry out deadline-constrained cost optimization: the ant constructs an ordered task list according to the pheromone trail and probabilistic upward rank, and uses the same deadline distribution and service selection methods as ProLiS to build solutions. Moreover, the deadline is relaxed to guide the search of L-ACO towards constrained optimization. Experimental results show that compared with traditional algorithms, the performance of ProLiS is very competitive and L-ACO performs the best in terms of execution costs and success ratios of meeting deadlines.

The OWL-based Web Service ontology is one of the most important standards for semantic service composition. Performance analysis of composite service processes specified in OWL-S enables us to understand whether the process meet the SLA requirements. In this work, we propose a Petri-net-based formal framework for OWL-S processes using non-markovian-stochastic-petri-nets (NMSPN) as the intermediate representation. The main innovation of this research includes a translation from OWL-S to non-markovian-stochastic-petri-nets and a well-defined control flow model for composite services built on OWL-S.

Summary Cloud computing is a novel paradigm capable of rationalizing the use of computational resources by means of outsourcing and virtualization. Elasticity is one of the most attractive features of cloud computing. Elastic clouds are able to adapt to workload changes by provisioning and de‐provisioning resources in an autonomic manner, such that at each point in time the available resources match the current demand as closely as possible. However, elasticity adds complexity, which makes quantitative analysis of cloud performance and power consumption difficult. Such analysis is required to evaluate and quantify the cost‐benefit of a strategy portfolio and the quantitative runtime performance and power consumption experienced by cloud‐users. In this study, we present a comprehensive analytical approach to performance and power consumption analysis of elastic clouds. Several metrics are defined and evaluated: expected task completion time, power consumption rate, and task rejection rate under different load conditions, elasticity intensities, and error intensities. To validate the proposed approach, we obtain experimental data through a real‐world cloud and conduct a confidence interval analysis. The analysis results suggest the perfect coverage of theoretical results by corresponding experimental confidence intervals. Copyright © 2016 John Wiley & Sons, Ltd.

Collaborative filtering (CF)-based recommenders are achieved by matrix factorization (MF) to obtain high prediction accuracy and scalability. Most current MF-based models, however, are static ones that cannot adapt to incremental user feedbacks. This work aims to develop a general, incremental- and-static-combined scheme for MF-based CF to obtain highly accurate and computationally affordable incremental recommenders. With it, a recommender is designed to consist of two components, i.e., a static one built on static rating data, and an incremental one built on a sub-matrix related to rating-variations only. Highly reliable predictions are thus generated by fusing their results. The experiments on large industrial datasets show that desired accuracy and acceptable computational complexity are achieved by the resulting recommender with the proposed scheme.

Nonnegative matrix factorization (NMF)-based models possess fine representativeness of a target matrix, which is critically important in collaborative filtering (CF)-based recommender systems. However, current NMF-based CF recommenders suffer from the problem of high computational and storage complexity, as well as slow convergence rate, which prevents them from industrial usage in context of big data. To address these issues, this paper proposes an alternating direction method (ADM)-based nonnegative latent factor (ANLF) model. The main idea is to implement the ADM-based optimization with regard to each single feature, to obtain high convergence rate as well as low complexity. Both computational and storage costs of ANLF are linear with the size of given data in the target matrix, which ensures high efficiency when dealing with extremely sparse matrices usually seen in CF problems. As demonstrated by the experiments on large, real data sets, ANLF also ensures fast convergence and high prediction accuracy, as well as the maintenance of nonnegativity constraints. Moreover, it is simple and easy to implement for real applications of learning systems.

Automatic Web-service selection is an important research topic in the domain of service computing. During this process, reliable predictions for quality of service (QoS) based on historical service invocations are vital to users. This work aims at making highly accurate predictions for missing QoS data via building an ensemble of nonnegative latent factor (NLF) models. Its motivations are: 1) the fulfillment of nonnegativity constraints can better represent the positive value nature of QoS data, thereby boosting the prediction accuracy and 2) since QoS prediction is a learning task, it is promising to further improve the prediction accuracy with a carefully designed ensemble model. To achieve this, we first implement an NLF model for QoS prediction. This model is then diversified through feature sampling and randomness injection to form a diversified NLF model, based on which an ensemble is built. Comparison results between the proposed ensemble and several widely employed and state-of-the-art QoS predictors on two large, real data sets demonstrate that the former can outperform the latter well in terms of prediction accuracy.

Latent-factor-based Quality-of-Service predictors can achieve high prediction accuracy and good scalability. However, most of them are based on first-order models that cannot well deal with their target problem that is inherently non-convex. Since second-order approaches have proven to be effective to such problems, this work proposes to implement a second-order predictor with an aim to achieve the high accuracy unlikely obtained by any existing methods. To do so, this work adopts the principle of Hessian-free optimization and successfully avoids the usage of a Hessian matrix by employing the efficiently obtainable product between its Gauss-Newton approximation and an arbitrary vector. Experimental results on two industrial QoS datasets indicate that the newly proposed predictor is highly accurate with fine computational efficiency.

Summary Analyzing the quantitative performance plays an important role in understanding and improving the quality of cloud computing systems and cloud‐based applications. In cloud computing, service requests from users go through numerous provider‐specific steps from the instant it is submitted to when the requested service is fully delivered. Quantitative performance analysis is not an easy task because of the complexity of cloud provisioning control flows and the increasing scale and complexity of real‐world cloud infrastructures. This work proposes a probabilistic queuing network‐based model for the performance analysis of cloud infrastructures. It considers expected task completion time and rejection probability as the performance metrics. Experimental performance data suggest the correctness of the proposed model. Copyright © 2015 John Wiley & Sons, Ltd.

SUMMARY Web services are emerging as a major technology for deploying automated interactions between distributed and heterogeneous applications. The accurate prediction of their quality of service (QoS) is important because their users rely on it to decide whether they meet the QoS requirement. The existing studies of QoS prediction usually assume that QoS of service activities follows certain distributions. These distributions are used as static model inputs into stochastic process models to obtain analytical QoS results. Instead, we consider the QoS activities to be fluctuating and introduce a dynamic framework to predict the runtime QoS by employing an Autoregressive Moving Average Model and QoS reduction rules. In the case study of a real‐world composite service sample, a comparison between existing approaches and the proposed one is presented, and results suggest that the proposed one achieves higher prediction accuracy.Copyright © 2014 John Wiley & Sons, Ltd.

Cloud computing is a recently developed new technology for complex systems with massive service sharing, which is different from the resource sharing of the grid computing systems. In a cloud environment, service requests from users go through numerous provider-specific steps from the instant it is submitted to when the requested service is fully delivered. Quality modeling and analysis of clouds are not easy tasks because of the complexity of the automated provisioning mechanism and dynamically changing cloud environment. This work proposes an analytical model-based approach for quality evaluation of Infrastructure-as-a-Service cloud by considering expected request completion time, rejection probability, and system overhead rate as key quality metrics. It also features with the modeling of different warm-up and cool-down strategies of machines and the ability to identify the optimal balance between system overhead and performance. To validate the correctness of the proposed model, we obtain simulative quality-of-service (QoS) data and conduct a confidence interval analysis. The result can be used to help design and optimize industrial cloud computing systems.

Recommender systems are an important kind of learning systems, which can be achieved by latent-factor (LF)-based collaborative filtering (CF) with high efficiency and scalability. LF-based CF models rely on an optimization process with respect to some desired latent features; however, most of them employ first-order optimization algorithms, e.g., gradient decent schemes, to conduct their optimization task, thereby failing in discovering patterns reflected by higher order information. This work proposes to build a new LF-based CF model via second-order optimization to achieve higher accuracy. We first investigate a Hessian-free optimization framework, and employ its principle to avoid direct usage of the Hessian matrix by computing its product with an arbitrary vector. We then propose the Hessian-free optimization-based LF model, which is able to extract latent factors from the given incomplete matrices via a second-order optimization process. Compared with LF models based on first-order optimization algorithms, experimental results on two industrial datasets show that the proposed one can offer higher prediction accuracy with reasonable computational efficiency. Hence, it is a promising model for implementing high-performance recommenders.

Cloud computing is a promising paradigm capable of rationalizing the use of computational resources by means of outsourcing and virtualization. Virtualization allows to instantiate virtual machines (VMs) on top of fewer physical systems managed by a VM manager. Performance evaluation of clouds is required to evaluate and quantify the cost-benefit of a strategy portfolio and the quality of service (QoS) experienced by end-users. Such evaluation is not feasible by means of simulation or on-the-field measurement, due to the great scale of parameter spaces that have to be traversed. In this study, we present a stochastic-queuing-network-based approach to performance analysis of migration-enabled clouds in error-prone environment. Several performance metrics are defined and evaluated: utilization, expected task completion time, and task rejection rate under different load conditions and error intensities. To validate the proposed approach, we obtain experimental performance data through a real-world cloud and conduct a confidence-interval analysis. The analysis results suggest the perfect coverage of theoretical performance results by corresponding experimental confidence intervals.

With the increasing call for green cloud, reducing energy consumption has been an important requirement for cloud resource providers not only to reduce operating costs, but also to improve system reliability. Dynamic voltage scaling (DVS) has been a key technique in exploiting the hardware characteristics of cloud datacenters to save energy by lowering the supply voltage and operating frequency. This paper presents a novel stochastic framework for energy efficiency and performance analysis of DVS-enabled cloud. This framework uses virtual machine request arrival rate, failure rate, repair rate, and service rate of datacenter servers as model inputs. Based on a queuing-network-based analysis, this paper gives analytic solutions of three metrics. The proposed framework can be used to help the design and optimization of energy-aware high performance cloud systems.

Matrix-factorization (MF)-based approaches prove to be highly accurate and scalable in addressing collaborative filtering (CF) problems. During the MF process, the non-negativity, which ensures good representativeness of the learnt model, is critically important. However, current non-negative MF (NMF) models are mostly designed for problems in computer vision, while CF problems differ from them due to their extreme sparsity of the target rating-matrix. Currently available NMF-based CF models are based on matrix manipulation and lack practicability for industrial use. In this work, we focus on developing an NMF-based CF model with a single-element-based approach. The idea is to investigate the non-negative update process depending on each involved feature rather than on the whole feature matrices. With the non-negative single-element-based update rules, we subsequently integrate the Tikhonov regularizing terms, and propose the regularized single-element-based NMF (RSNMF) model. RSNMF is especially suitable for solving CF problems subject to the constraint of non-negativity. The experiments on large industrial datasets show high accuracy and low-computational complexity achieved by RSNMF.

Ontology Web Language for Services (OWL-S), one of the most significant semantic Web service ontologies proposed to date, provides a core ontological framework and guidelines for describing the properties and capabilities of services in an unambiguous computer-interpretable form. Analysis of the quality of service of composite service processes specified in OWL-S enables service users to decide whether the process meets nonfunctional requirements. In this paper, we propose a probabilistic approach for reliability analysis of OWL-S processes, employing the non-Markovian stochastic Petri net (NMSPN) as the fundamental model. Based on the NMSPN representations of the OWL-S elements, we introduce an analytical method for the calculation of the process-normal-completion probability as the reliability estimate. This method takes the probabilistic parameters of service invocations and messages as model inputs. To validate the feasibility and accuracy of our approach, we obtain runtime experimental data and conduct a confidence interval analysis in a case study. A sensitivity analysis is also performed to determine the impact of model parameters on reliability and to help identify the reliability bottlenecks.

SUMMARY The Ontology Web Language (OWL)‐based web service ontology is one of the major standards for modeling and description of distributed service composition. To analyze the performance of composite service, processes specified in OWL‐S give way to tell whether the process meets the performance requirements and to choose the process with higher efficiency from those with similar function. In this paper, we propose a stochastic Petri net‐based approach for performance analysis of OWL‐S processes, which employs non‐Markovian stochastic Petri nets as the intermediate representation. The main innovation of this research includes a translation from OWL‐S to non‐Markovian stochastic Petri nets and a performance (using expected‐process‐normal‐completion‐time as the metric) analysis method. We also validate the correctness of the approach in the case study by showing 90% CI obtained from experimental results that cover corresponding theoretical prediction values. Copyright © 2012 John Wiley & Sons, Ltd.

Business Process Execution Language (BPEL) is considered as the de facto standard for Web service composition. To analyze the performance of composite service processes specified in BPEL gives the way to tell whether the process meets the performance requirements. In this paper, we propose a translation-based approach for performance analysis of BPEL processes, which employs a general stochastic Petri net (GSPN) as the intermediate representation. A set of translation rules is defined for constructs and activities of BPEL so that the processes specified in BPEL can be translated into the GSPN representations. Based on the GSPN representation of BPEL processes, we introduce a state-space method to calculate the expected-process-normal-completion-time as the performance estimate. In the case study, we obtain experimental data and conduct a confidence interval analysis to validate the feasibility and accuracy of the translation-based approach.

OWL-S, one of the most significant Semantic web service ontologies proposed to date, provides Web Service providers with a core ontological framework and guidelines for describing the properties and capabilities of their web Services in unambiguous, computer interpretable form. In this paper, we propose a probabilistic model for OWL-S processes, employing the Non-markovian stochastic Petri net(NMSPN) as the intermediate representation. A set of translation rules is defined for constructs and activities of OWL-S so that processes specified in OWL-S can be translated into NMSPN. Based on the NMSPN representation, analytical methods for QoS analysis can be designed.

Abstract Web Service Choreography Description Language (WS‐CDL) is a mainstream standard for the description of peer‐to‐peer collaborations for the participants of service composition. To predict the dependability of composite service processes specified in WS‐CDL allows service designers and user to decided whether the process meets the nonfunction requirements of trustworthiness, and to choose the process with better dependability to those with identical function. Unfortunately, very limited research attention is paid to dependability of WS‐CDL. In this paper, we propose a model‐driven approach for dependability prediction of composite service built on WS‐CDL. The main innovation of this research includes a complete translation from WS‐CDL to generalized stochastic Petri nets (GSPN) and a dependability (using process‐normal‐completion‐probability as the metric of dependability of service composition) calculation algorithm based on GSPN. We also validate the accuracy of the approach in the experimental study by showing 95% confidence intervals obtained from experimental dependability results that cover the corresponding theoretical prediction values. Copyright © 2011 John Wiley & Sons, Ltd.

Abstract Quality of service (QoS) of workflows and workflow‐based applications is given increasing attention by both industry and academic. In this paper, we propose a novel analytical framework to analyze QoS (metrics include make‐span, cost, and reliability) of workflow systems based on GWF‐net, which extends traditional workflow net by associating tasks with generally distributed firing delay and time‐to‐failure. The GFW‐net model is used to model process structure and task organization of workflows at the process level. In contrast with prevailing QoS models based on Markovian process, we introduce a reduction technique to evaluate QoS of GWF‐net process avoiding the state‐explosion problem and tedious mathematical derivation of state‐transition probabilities. Through a case study, we show that our framework is capable of modeling real‐world workflow‐based application effectively. Also, experiments and confidence‐interval analysis in the case study indicate that the reduction methods are verified by real results. We also compare our approach with related research in the text. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract There is a growing demand for using commercial‐off‐the‐shelf (COTS) software components to facilitate the development of software systems. Among many research topics for component‐based software, quality‐of‐service (QoS) evaluation is yet to be given the importance it deserves. In this paper, we propose a novel analytical model to evaluate the QoS of component‐based software systems. We use the component execution graph (CEG) graph model to model the architecture at the process level and the interdependence among components. The CEG graph can explicitly capture sequential, parallel, selective and iterative compositions of components. For QoS estimation, each component in the CEG model is associated with execution rate, failure rate and cost per unit time. Three metrics of the QoS are considered and analytically calculated, namely make‐span, reliability and cost. Through a case study, we show that our model is capable of modeling real‐world COTS software systems effectively. Also, Monte‐Carlo simulation in the case study indicates that analytical results are consistent with simulation and all are covered by 95% confidence intervals. We also present a sensitivity analysis technique to identify QoS bottlenecks. This paper concludes with a comparison with related work. Copyright © 2007 John Wiley & Sons, Ltd.

Performance prediction is one of the most important research topics of workflow. To investigate the performance of workflow systems in queuing condition, this paper extends traditional WF-net into WFQN (WF queuing network), by modeling tasks as FIFS (first-in-first-service) queues and the source place as the input of tokens following poisson arrival process. Analytical methods are introduced to evaluate the queue-length, wait-time and completion-duration. The case study (especially the case of airline ticket booking application) shows that WFQN can model real-world workflow-based applications effectively. Through Montecarlo simulations in the case study, we show analytical models are verified by simulative results. We also present a sensitivity analysis technique to identify performance bottle-necks of WFQN. This paper concludes with a comparison with relate work.