Schema matching is a crucial step in data integration for in-depth information mining, aimed at capturing seman-tic correspondences between elements of multi-source datasets. The existing schema matching methods still face problems such as insufficient representations of schema information diversity, poor cross-domain expression ability, and inadequate adaptability to complex data environments. We propose a composite schema matching method based on contrastive learning. Our method de-signs multi-domain word lists for data augmentation, the schema-based matching part fine-tunes pre-trained models through con-trastive learning and finally designs multi-model voting strategies to complete the composite matches. It also designs a format validation strategy to enhance the performance of the schema matching model. The experimental results show that our method has improved the F1 comprehensive performance indicators on three benchmark datasets by an average compared to existing benchmark methods.

Colored Petri nets (CPNs) provide descriptions of the concurrent behaviors for software and hardware. Model checking based on CPNs is an effective method to simulate and verify the concurrent behavior in system design. However, the model-checking method traverses the full state space, which suffers from the state-space explosion problem. A reduced state-space generation method related to the property of concurrent systems is proposed. Specifically, we extend CPNs to define a property-related model (CPN-PR) and give a property-related analysis method whose results can be used to generate the CPN-PR model. A reduced state-space generation method is developed based on enabled binding element filtering rules. The stutter trace equivalence between the state spaces of CPN and CPN-PR has been proven by showing that the reduced state space may not change the model-checking result. A comparison experiment is conducted to demonstrate the effectiveness of our method.

The widespread adoption of cloud data centers has led to a rise in energy consumption, with the associated carbon emissions posing a further threat to the environment. Cloud providers are increasingly moving towards sustainable data centers powered by renewable energy sources (RES). The existing approaches fail to efficiently coordinate IT and cooling resources in such data centers due to the intermittent nature of RES and the complexity of state and action spaces among different devices, resulting in poor holistic energy efficiency. In this paper, a reinforcement learning (RL) based framework is proposed to optimize the holistic energy consumption of sustainable cloud data centers. First, a joint prediction method MTL-LSTM is developed to accurately evaluate both energy consumption and thermal status of each physical machine (PM) under different optimization scenarios to improve the state space information of the RL algorithm. Then, this framework designs a novel energy-aware approach named BayesDDQN, which leverages Bayesian optimization to synchronize the adjustments of VM migration and cooling parameter within the hybrid action space of the Double Deep Q-Network (DDQN) for achieving the holistic energy optimization. Moverover, the pre-cooling technology is integrated to further alleviate hotspot by making full use of RES. Experimental results demonstrate that the proposed RL-based framework achieves an average reduction of 2.83% in holistic energy consumption and 4.74% in brown energy, which also reduces cooling energy consumption by 13.48% with minimal occurrences of hotspots. Furthermore, the proposed MTL-LSTM method reduces the root mean square error (RMSE) of energy consumption and inlet temperature predictions by nearly half compared to LSTM and XGBoost.

The rapid growth and widespread adoption of cloud computing have led to significant electricity costs and environmental impacts. Traditional approaches that rely on static utilization thresholds are ineffective in dynamic cloud environments, and simply consolidating virtual machines (VMs) to minimize energy costs does not necessarily result in the lowest carbon footprints. In this paper, a deep reinforcement learning (DRL) based framework called CFWS is proposed to enhance the energy efficiency of renewable energy sources (RES) supplied data centers (DCs). CFWS incorporates an adaptive thresholds adjustment method TCN-MAD by evaluating the predicted probability of a physical machine (PM) being overloaded to prevent unnecessary VM migrations and mitigate service level agreement (SLA) violations due to imbalanced workload distribution. Additionally, CFWS introduces a novel action space in the DRL algorithm by representing VM migrations among geo-distributed cloud data centers as flattened indices to accelerate its execution efficiency. Simulation results demonstrate that CFWS can achieve a superior optimization of energy costs and carbon footprints, saving 5.67% to 13.22% brown energy with maximized RES utilization. Furthermore, CFWS reduces VM migrations by up to 86.53% and maintains the lowest SLA violations within suboptimal execution time in comparison to the state-of-art algorithms.

The accuracy of data analysis depends on data quality, and addressing data consistency issues is a key challenge to improve it. Constant Conditional Functional Dependency (CCFD) is an effective approach that ensures data consistency by enforcing bindings of semantically related values, thus providing quality assurance for data analysis and decision-making processes. However, with the growth of data scale, especially the increasing number of data tuples and attributes, existing single-machine CCFD discovery algorithms face issues of low computational efficiency and lengthy computation time. This paper proposes a time-efficient distributed CCFD discovery algorithm (DCCFD). Through the optimization of data preprocessing and index mapping, the data organization structure is enhanced, laying the foundation for the discovery of CCFDs under distributed conditions. The Spark parallel computing framework is used to partition the dataset, which accelerates the parallel loading and processing of data. Additionally, this algorithm ensures accuracy and processing speed when discovering dependencies by efficiently generating frequent itemsets and verifying CCFDs in parallel. Experiments on multiple real datasets show that, especially with the complex Airline dataset, the DCCFD algorithm not only accurately discovers CCFDs, but also reduces the average running time by 75.64% compared with the preCFDMiner algorithm.

Fuzzing is increasingly being utilized as a method to test the reliability of Deep Learning (DL) systems. In order to detect more errors in DL systems, exploring the internal logic of more DNNs has become the main objective of fuzzing. Despite advancements in the seed selection aspect of fuzzing, considerable opportunities still exist for improving testing efficiency. Current research has issues with the repeated consideration of neurons in the model that will be covered in the future by other seeds, leading to redundant seeds and lower testing efficiency. Additionally, there is a lack of a method to measure the potential of seeds to increase coverage, making it difficult to select the most worthy seeds for mutation in each iteration. We propose an uncovered neurons information based (UNIB) fuzzing method for DNN. UNIB uses clustering methods to organize the seed queue based on initial seed data, aiming to enhance the coverage rate improved in each iteration. It also integrates coverage information from the testing phase to identify the seeds with the greatest potential. The experimental results show that UNIB achieved a higher NC than the second-best method by 1.1% and 3% in LetNet-4 and LetNet-5, respectively. UNIB consistently required the fewest number of iterations to reach the same NC as other methods. For both LetNet-4 and LetNet-5, the adversarial test case sets generated by UNIB exhibited the highest diversity.

Time-triggered (TT) network provides a low-cost service to meet the strong demand of modern industry networks for real-time communication. Both simulation and reachability analysis provide effective research methods for TT networks. This paper presents a verification framework for the TT network based on Timed Colored Petri Nets (TCPN). We propose an automatic formal modeling method for the behavior of message transmissions in the TT network. We harness timed multisets of TCPN to model and analyze time-triggered message transmission latencies. For a holistic system evaluation, we characterize the critical system properties such as boundedness and liveness. We substantiate the properties by the reachability analysis. We demonstrate the effectiveness through a case study by simulating the message transmission and reachability analysis in the state space. Finally, we analyze the influencing factors of the state space in the automotive scenario. The proposed automatic modeling method can effectively reduce the state space scale.

In a multi-cloud storage system, provenance data records all operations and ownership during its lifecycle, which is critical for data security and audibility. However, recording provenance data also poses some challenging security and storage issues. In this paper, we present a secure and efficient multi-cloud storage data source scheme, BMDP. We use blockchain technology to ensure the secure storage of provenance data and design a smart contract to utilize the provenance data to ensure the proper operation of the multi-cloud storage system. Finally, we analyze the scheme’s safety and do simulation experiments to show that the scheme has practicality.

Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models.

Cloud storage services have been used by most businesses and individual users. However, data loss, service interruptions and cyber attacks often lead to cloud storage services not being provided properly, and these incidents have caused financial losses to users. Second, traditional and single-cloud model disaster recovery services are no longer suitable for the current complex cloud storage systems. Therefore, a scheme to provide disaster recovery for cloud storage services in a multi-cloud storage environment is needed in real production. In this paper, we propose a disaster recovery scheme based on blockchain technology. The proposed scheme outlined in this study aims to address the issue of data availability within the cloud storage landscape. The proposed scheme achieves this goal by dividing data into hot and cold categories, verifying the integrity of copy data via blockchain technology, and utilizing blockchain networks to manage multi-cloud storage systems. Experimental findings demonstrate that the proposed scheme yields superior results in terms of computation and time overheads.

Abstract Covering array generation (CAG) is the key research problem in combinatorial testing and is an NP‐complete problem. With the increasing complexity of software under test and the need for higher interaction covering strength t , the techniques for constructing high‐strength covering arrays are expected. This paper presents a hybrid heuristic memetic algorithm named QSSMA for high‐strength CAG problem. The sub‐optimal solution acceptance rate is introduced to generate multiple test cases after each iteration to improve the efficiency of constructing high‐covering strength test suites. The QSSMA method could successfully build high‐strength test suites for some instances where t up to 15 within one day cutoff time and report five new best test suite size records. Extensive experiments demonstrate that QSSMA is a competitive method compared to state‐of‐the‐art methods.

Abstract The state space explosion restricts the error detection of concurrent software. The abstraction can provide a solution to avoid state space explosion, but it is easy to ignore important details, resulting in inaccurate detection results. This paper proposes a methodology of fine‐coarse‐grained automatic modelling for Java source programs. By the principle that the execution details of property‐unchecked, non‐interactive, and unrelated statements do not affect the model checking results, we model coarse‐grained model fragments for such statements, while fine‐grained model fragments for property‐checked, interactive, and related statements. Our method reduces the model and state space and ensures the error detection of the source program based on model checking. Moreover, we prove the equivalence of the fine‐grained model, the coarse‐grained model, and the program. Finally, this paper gives an experiment to verify the effectiveness of the proposed method.

High radix SRT division plays an important role in contemporary microprocessors as the quotient digit selection tables effectively reduce the computation complexity of the quotient digits. The quotient digit selection table is constructed according to the rounded lower and upper bounds of the overlapping regions in the traditional method. The table construction process lacks in mathematical rigor and consequently is susceptible to error. This paper proposes an algebraic method for computing the quotient digit selection tables. We characterize the quotient digit selection functions to construct quotient digit selection tables required for SRT division and SRT square root with any valid redundancy. The functions include the maximum and minimum legal quotient digit selections. We compute the truncations of the remainder$p$and divisor$d$when$d \in [1,2)$. We implement procedures to compute quotient digit selection tables by our functions. The computation of the quotient digit selection table for the case radix-4 with the quotient digit set$[-2,2]$is presented by using the minimum quotient digit selection function. Our functions can compute quotient digit selection tables in the design phase of SRT division and square root by given radix and redundancy.

Massively multiplayer online games (MMOGs) are a new type of large-scale interactive applications providing a seamless virtual world for millions of gamers from all over the world. Traditionally, MMOGs are implemented using a client-server architecture. With the maturity of cloud computing, many MMOG providers such as Blizzard Entertainment and Jagex Limited have begun to utilize virtualized machines to serve their consumers due to the elasticity, adaptability, and cost advantages of clouds. It is a major challenge for MMOG providers how to dynamically allocate resources in an on-demand pattern in order to reduce the energy cost associated with operating a MMOG cloud while providing good-enough quality of experience (QoE) for MMOG gamers. In this paper, we propose a dynamic resource allocation scheme for MMOGs in heterogeneous cloud computing systems, which makes use of both dynamic virtual machine (VM) consolidation among physical machines (PMs) and long short-term memory based VM resizing at physical machine level to achieve the energy efficiency and desired QoE requirements. Our scheme considers multiple types of resources, characteristic of AFK (away from keyboard) gamers, heterogeneous PMs and VMs, strict QoE requirements and overheads incurred due to migrating VMs. Furthermore, a novel hybrid algorithm based on differential evolution and modified first-fit heuristic is presented for dynamic consolidation of VMs in heterogeneous cloud data centers. The experiment results show that, compared to the traditional over-provisioning policy, our resource allocation scheme can achieve up to 44.8% energy savings while ensuring the QoE requirements of MMOG gamers under rapidly changing workloads.

Combinatorial testing (CT) is an effective testing method that can detect failures caused by the interaction of parameters of the software under test (SUT). With the increasing complexity of SUT and the parameters involved, the variable strength test suite supporting high strength interaction is challenging in a practical testing scenario. This paper presents a multi-learning-based quantum particle swarm optimization (IQIPSO) for high and variable strength covering array generation (VSCAG). Specifically, a specially designed data structure and several combination location methods are proposed to support and speed up the high-strength VSCAG. Besides, multi-learning strategies, including Lamarckian and Baldwinian learning, are applied to IQIPSO to address the premature convergence leading to a large test suite size. Studies for parameter settings of IQIPSO are presented systematically. The IQIPSO method successfully builds test suites where strength is up to 15 and totally reports 13 new best test suite size records. Extensive experiments demonstrate that IQIPSO tends to outperform most other existing methods.

Knowledge Graph is an important research field that involves the storage and management of knowledge, but the incompleteness and sparsity of Knowledge Graphs hinder their application in many fields. Knowledge Graph Reasoning aims to alleviate this problem by completing missing paths or identifying wrong paths between entities. Graph Convolution Network (GCN) based methods are one of the state-of-the-art approaches to this work. However, it is difficult to directly generalize to unknown nodes and utilizes valid information from the local neighborhood which results in poor flexibility and extensibility and will loss of important information. This paper presents EG-KGR, a plug-and-play knowledge reasoning model based on enhanced graph sampling and aggregate inductive learning algorithm to relieve the above problems and enhance existing GCN-based methods. Specifically, EG-KGR supports incremental characteristics, uses inductive learning to replace transductive learning, and designs random sampling and local information sampling optimization methods to improve the model's generalization ability, prediction accuracy, and running speed. Extensive experimental results show that our EG-KGR can achieve optimal results.

Abstract Multipath networking schemes can be employed to efficiently balance traffic and address link failures. However, these schemes can also cause packet disorder, thereby deteriorating the overall network performance. To address these problems, we propose splitting a single flow over a multipath scheme (SFMS) at a fine granularity based on software‐defined networking. The SFMS splits an elephant flow into several subflows according to the residual bandwidths and delays of the link‐disjointed paths inter the data centers (inter‐DC) network to enhance the transmission efficiency of traffic. The experimental results confirm that the SFMS not only reduces the flow completion time required from the time the first packet of an elephant flow is sent until the last packet is received while avoiding packet disorder.

Abstract Cloud computing services are ubiquitous in society and cloud recommender systems play a crucial role in intelligently selecting services for cloud users. Currently, recommendations are static with low scalability. Only one recommendation list is generated at a time and the recommender strategy in the recommendation cycle is not adjustable. This paper presents a new elastic recommender process (ERP) for cloud users. A Markov model is used to characterize the dynamic relationship between different user states. The ERP generates an elastic recommendation that can be used to dynamically adjust the recommender strategy to meet the user's needs based on their browsing records in the current service cycle without the recommender system's involvement. Experimental results show that the ERP improves the effectiveness of the recommender thus increasing the accuracy and diversity of its recommendations.

Abstract Combinatorial testing (CT) can efficiently detect failures caused by interactions of parameters of software under test. The CT study has undergone a transition from traditional CT to constrained CT, which is crucial for real‐world systems testing. Under this scenario, constrained covering array generation (CCAG), a vital combinatorial optimisation issue targeted with constructing a test suite of minimal size while properly addressing constraints, remains challenging in CT. To the authors’ best knowledge, this paper presents a synergic method first based on quantum particle swarm optimisation (QPSO) for the CCAG problems. Three auxiliary strategies, including contraction‐expansion coefficient adaptive change strategy, differential evolution strategy, and discretisation strategy, are proposed to improve the performance of QPSO. Meanwhile, the improved QPSO method combines with the three different constraint handling strategies and an enhanced one‐test‐at‐a‐time strategy as a synergic QPSO method named QPIO to solve the CCAG problem. In the experiment, we investigate the impacts of parameter settings on the performance of the QPIO. Extensive experimental results show that the QPIO algorithm is a competitive method compared to the representative methods for CCAG. Besides, the QPIO method enriches the application of the QPSO algorithm in the context of CT.

Prompted by the remarkable progress in both cloud computing and GPU virtualization, cloud gaming has been attracting more and more attention in the gaming industry. With the cloud gaming model, players do not need to download or install the game on local devices, and constantly upgrade their devices. Despite these advantages, cloud gaming faces several challenges for its success, including long response delay, poor game fairness, and high operational cost. To this end, this article proposes an edge computing-assisted multiplayer cloud gaming system named ECACG to improve multiplayer cloud gaming experiences and operating costs by offloading the game rendering task to the nearby edge server. Based on the ECACG, two decision processes are completed. One is player request scheduling and the other is rendering server allocation. The decision problem is formulated into a constrained multiobjective optimization model. A novel hybrid algorithm based on deep reinforcement learning and heuristic strategy is developed to solve the optimization problem. The effectiveness of the proposed ECACG is evaluated by simulation experiments based on the real-world parameters. The simulation results show that compared with the existing schemes, the proposed ECACG can achieve lower rental costs and better fairness, while providing the good-enough response delay for players.

Cloud services request lower cost compared to traditional software of self-purchased infrastructure due to the characteristics of on-demand resource provisioning and pay-as-you-go mode. Current enterprises compact their business software as services into cloud platform to users. In the cloud services market, service providers attempt to make more profits from their services, while users hope to choose low-cost services with high-quality. The conflict of interests between users and service providers is an important challenge for the booming cloud service market. This article characterizes this application problem formally based on a utility game model of service providers and users. In the model, QoS is considered as the basis for determining the utilities of both parties from an economic point of view. By analyzing the behaviors of users and service providers, we introduce the concept of reputation cost for the first time in the model and find a QoS solution that balances the utilities of users and service providers in service transactions. In such a balance, any change in either party's strategy will result in a loss of utility. And then a QoS optimization method is designed to obtain a near-optimal QoS solution for a tradeoff between user satisfaction and provider profit. Extensive simulation experiments are conducted to substantiate the effectiveness of our method. The results are applicable to win-win service applications between service providers and users.

Cloud computing and big data technologies have gained great popularity in recent years. MapReduce is still one of the most efficient and well-adopted computing paradigms for providing big data services. MapReduce applications need to be executed on cloud platform where failures are inevitable. Hadoop is the de facto implementation of MapReduce, but it deploys a coarse grained and unsatisfactory fault tolerant services. The failed tasks are rescheduled from scratch to re-execute from the very beginning, which apparently brings amount of overload for failure recovery, and the whole job would be heavily delayed as failures happen. In this paper, we propose a novel multi-trigger checkpointing approach for fast recovery of MapReduce tasks, named a Multi-trigger Checkpointing Tactic for fAst TAsk Recovery (McTAR). As a finer-grained and better fault tolerance tactic, our McTAR employs multi-trigger checkpoint generation, push-pull combined intermediate data distribution and optimized failure task prediction techniques together to make the recovery task attempt be able to start at a specific progress according to the valid checkpoint for intermediate data. In this way, McTAR could effectively speed up the recovery process of MapReduce jobs and highly reduce the task recovery delay.

Abstract The research on personalized recommendation of Web services plays an important role in the field of Web services technology applications. Fortunately, not all users have completely different service preferences. Due to the same application scenarios and personal interests, some users have the same preferences for certain types of Web services. This paper explores the problem of user clustering in the service environment, grouping users according to their service preferences. It helps service providers to identify and characterize the preferences of similar users and provide them with customized services. We propose two combination-based clustering algorithms which make full use of the advantages of the K-means algorithm and the affinity propagation algorithm. In addition, a three-stage clustering process is elaborated to improve the accuracy of user clustering. To reduce the time complexity of the algorithms, we create a parallel execution model of the algorithms implemented by a higher-order MapReduce sequence linking technology. Extensive experiments on simulated datasets and real datasets are performed on the comparisons between the proposed algorithms and the other combination-based clustering algorithms. The experimental results substantiate that the proposed algorithms can effectively distinguish user group with different preferences.

As the technology of cloud computing becomes mature, the service idea of IT resources has been promoted rapidly. The various available cloud services have different quality of service (QoS). Different users have different QoS requirements for services. Finding the best QoS strategy which a user is satisfied with is a multi-criteria NP-hard problem. The completed theory and applied research have proved that the swarm intelligence method can effectively solve most multiobjective optimization problems. Therefore, a Pareto optimal solution for QoS strategy can be found by an evolutionary algorithm based on swarm intelligence. However, so far, only few solutions based on these approaches have been proposed and there exists no comparative study published to date. This motivated us to perform an analysis of state of the art evolutionary algorithm based on swarm intelligence. By a large number of contrast experiments under different circumstances, three QoS evolutionary algorithms based on swarm intelligence are analyzed and compared.

Combinatorial testing is an important software engineering problem. Covering array generation (CAG), a discrete optimization problem, is the most popular field of research in combinatorial testing (CT). The problem has attracted many researchers in the community of software engineering. This paper presents two novel versions of discrete particle-swarm-simulated-annealing-based memetic algorithms named D-PSMA for CAG problem. We elaborate with both particle swarm optimization searching operators and simulated annealing local searching operators synthetically to equilibrate the exploration and exploitation capabilities. The concept of suboptimal solution acceptance rate is introduced for excellent suboptimal solution. After each iteration, multiple test cases are generated that improve the efficiency of test suite generation. Meanwhile, an improved boundary constraint strategy are proposed. In the experiment, we used different test suite generation strategies, boundary constraint strategies and discrete memetic algorithm in the general framework, and found the optimal combination for CAG problem. Extensive experiments demonstrate that D-PSMA has competitiveness when compared to other existing classical algorithms.

The software sharing platform in the Internet provides great convenience for the promotion, application and communication of software (especially source software). But there inevitably exists the problem of software quality on the open Internet platform. How the users choose software to download and use becomes a new challenge for software sharing platforms. Aimed at the above problems and challenges, the internal relations between the data collected on platform and experience of user are analyzed. And then a software popularity recommendation method based on evaluation model is presented. The method constructs two evaluation indexes based on the collected data on the platform, including attention-degree and satisfaction-degree; solves the problem of small sample data's influence on the accuracy of evaluation model by using the Wilson interval model and makes a tradeoff between the recommendation results of old and new software by using Newton cooling law. The experimental results show that the software popularity recommendation method based on evaluation model helps users to screen for software, which can effectively improve the service performance of software sharing platform.

Timing analysis plays an important role in embedded real-time system design. And the cache hit/miss behavior analysis is a key problem in timing analysis, especially difficult in the sharing caches among multiple cores. The combination of Abstraction Interpretation (AI) and Satisfiability Modulo Theories (SMT) methods are used in timing analysis. SMT based on the rigorous first-order logic has both computational and analytical capabilities, thus the usage of SMT in timing analysis can more effectively relieve the pressure of AI. However, the existing methods do not solve the problem of behavior analysis of shared instruction caches, and do not complete the computation of upper bounds on the execution time of tasks. In this paper, we adopt AI to complete the refined hit/miss classification of instructions accessing the shared instruction cache, employ SMT to complete the difficult work of behavior analysis of shared instruction cache, and explore an iteration optimization algorithm of logarithmic order to compute the results. From the experiments, our approach can accurately predict the upper bounds on the execution time.

Traffic Engineering (TE), an important network application, manages and measures network traffic. It is responsible for monitoring network status, analyzing network traffic and realizing to improve overall network performance. However, when faced with different network applications and requirements, traditional network protocol is very onesided in considering link weight and the network equipment is difficult to obtain global information. Effective real-time and dynamic adjustion can not be guaranteed by traditional network. To solve the problem, an information entropy weight algorithm(IEW-algorithm) is proposed in this paper to calculate routing weights. This algorithm utilizes Software Defined Networking (SDN) with the character that centralizes all of network state information in SDN controller. It can be seen through the experiments that this algorithm can specify the optimal path for the application of different requirements based on link state.

In order to meet the demand of people's travel, all kinds of hailing-taxi apps have emerged. Although the advent of these apps brings people convenience greatly, their shortcomings and deficiencies are also increasingly apparent. Due to the subjectivity of the taxi drivers, some drivers may pick up a passenger without considering the problem of traffic congestion, which decrease the efficiency of taxis. So an efficient coordination of taxi network at a large scale becomes a new challenge in the intelligent transportation. In order to better solve the insufficiency of taxi-hailing apps, strengthen the passengers' experience and improve the efficiency of taxi service, an intelligent recommendation service for taxi based on real-time traffic in the road network is designed. And then a global optimization algorithm based on simulated annealing particle swarm optimization is presented to search a vacant taxi which can reach the location of the passenger in the shortest time. The comparative experiments with other algorithms prove that the algorithm has certain effectiveness and efficiency.

Routing protocol based on the social relationship in opportunistic networks can gain better performance in the opportunistic network with the social attribute. Several algorithms are proposed in literature for static social relationship in opportunistic networks, however these algorithms based on static social relationship cannot be used for the routing purpose in opportunistic networks as the social relationship in opportunistic networks are dynamic in nature. This paper investigates the problem of opportunistic network routing algorithms based on the dynamic social relationship. A new model of the dynamic social relationship in opportunistic networks is presented, and a new prediction method is introduced to predict the movement pattern and the encounter time. An optimized routing protocol in opportunistic network based on the dynamic social relationship is developed to rout messages. Our experiment results show that the proposed protocol is very efficient in the opportunistic networks with the dynamic social relationship, and the routing protocol in opportunistic is very suitable for the real application.

Feature representation of data is a key issue for recognition related tasks. Inspired by the creative ability of human beings, in this paper we propose a novel feature learning framework named within-class reconstruction (WCR). In WCR, the feature representation of the input sample are used to reconstruct all the samples within the same class. We minimize the mean squared error (MSE) cost function to update feature extracting functions. Furthermore, most unsupervised learning methods such as auto-encoders could embed in the proposed framework. To evaluate the effectiveness of the proposed framework, CNN is used to extract the feature representations and reconstruct the within-class samples. The experimental results demonstrate that the representations learned by the proposed WCR achieve better performance than that of auto-encoders. All the codes have been made publicly available at https://github.com/step123456789/wcr.

With the development of web service technology, identifying and discovering the users with similar preferences have an important significance to service selection and service optimization in the service environment. In order to divide the users into groups based on their preference similarity in the process of service selection, a combination-based clustering algorithm, named AAK, is presented in this paper. The method combines the K-means algorithm with the Affinity Propagation (AP) algorithm to cluster the users with similar preferences. In the clustering process, the algorithm makes full use of the advantages of the two algorithms, including the high partition accuracy of K-means algorithm and the independence in the prior knowledge of AP algorithm, which breaks the limitation of using a single clustering algorithm. Then a parallel execution model of the algorithm is built and implemented by a high order MapReduce sequence linking technology. Finally AAK algorithm is compared with its serial model and the other combination-based clustering methods on Matlab platform and Hadoop platform. The experimental results show that AAK algorithm can be applied to distinguish user group with different preferences and has a good effectiveness and efficiency.

Cloud applications are typically composed of multiple cloud service components communicating with each other through web service interfaces, where each component fulfills specified functionalities. Lack of effective fault tolerance scheme is one of major obstacles for enhancing availability and efficiency of complex and aging cloud application systems. In this paper, we propose a holistic software rejuvenation based fault tolerance scheme for cloud applications, which contains three indispensible parts: adaptive failure detection, aging degree evaluation, and checkpoint with trace replay based component rejuvenation. Through a preliminary and qualitative evaluation, it shows that our new fault tolerance scheme brings promising improvement on the availability of cloud applications.

Web service composition problem was considered as a planning problem by previous research. However, many factors constantly affect the QoS and results of invocation of web services, thus the environment of web services is dynamic. As result, web service composition problem should be considered as an uncertain planning problem. This paper uses Markov property to deal with the uncertain planning problem for service composition. According to the uncertainty model, we propose a reinforcement learning method to compose web services. Without knowing the transition function and reward function, our uncertain planning method uses an estimated value function to approach a real function and is able to obtain a composite service. The results of experiments show that our method can effectively reduce computing time of the service composition.

How we manage Web services depends on how we understand their variable parts and invariable parts. Studying them separately could make Web service research much easier and make our software architecture much more loose-coupled. We summarize two variable parts that affect Web service compositions: uncertain invocation results and uncertain quality of services. These uncertain factors affect success rate of service composition. Previous studies model the Web service problem as a planning problem, while this problem is considered as an uncertain planning problem in this paper. Specifically, we use Partially Observable Markov Decision Process to deal with the uncertain planning problem for service composition. According to the uncertain model, we propose a reinforcement learning method, which is an uncertainty planning method, to compose web services. The proposed method does not need to know complete information of services, instead it uses historical data and estimates the successful possibilities that services are composed together with respect to service outcomes and QoS. Simulation experiments verify the validity of the algorithm, and the results also show that our method improves the success rate of the service composition.

Effective verification methods are necessary for finding bugs in complex system design. Given domain specific knowledge, design verification engineers typically specify cross coverage for certain risky areas where bugs tend to appear. This paper proposes a pragmatic coverage model based on cross coverage. We address the verification on user specified cross coverage regions. The proposed analysis models the probability of exposing the bug within a given number of samplings, and derives the expected number of samples until bug detection. The approach is applicable to random, round-robin, and hybrid sampling strategies in recurring and nonrecurring cases based on our cross coverage model. We have written Matlab and C programs that use our formulas to calculate the probabilities. Experimental results show that our analysis is consistent with Monte Carlo simulation.

In a composite service for multiple users, network position of multiple users are different, leading to users perceptions to one service are different. In addition, network parameter values, which vary due to many reasons (such as network congestions), influence QoS of Web services. We propose a reliable service composition method which uses multiple candidate services to process service requests for a variety of users. Initially, multiple service execution paths for each kind of user requests are calculated. In runtime, the optimal services are chosen according to the current network state. We present an application scenarios for the method.

As the development of collaborative applications, the parallel interactions among processes are more and more complicated and frequent. However, modeling of the interactions among many collaborative processes is a complicated and error-prone procedure. In this paper, firstly, a novel model based on Petri net, called PIPN, was proposed. PIPN is suitable to define and analyze the parallel interactions among collaborative processes. Secondly, seven parallel interactive modes were summarized according to three views of parallel interactions, which are unidirectional or bidirectional, single-point or multi-point, and synchronous or asynchronous. Then the formal definitions and control flow graphs of these modes were given. Finally, an example, called micro blog, was modeled to verify the reasonableness and feasibility of the work in this paper.