SSD-based graph processing systems have emerged as a cost-effective solution for handling the ever-growing, large-scale graphs that exceed the memory capacity of a single machine. However, the mismatch between the large SSD access granularity (e.g., 4KB) and the small size of the graph vertex data leads to significant read amplification and low I/O efficiency. Despite existing works proposing techniques like dynamic active data gathering or reordering-based graph preprocessing to tackle this challenge, they inevitably cause problems such as expensive on-line computation overheads, inefficient graph traversal, and I/O imbalance, thus degrading the performance of graph processing.

Side-channel analysis (SCA) is a critical tool for evaluating the security of cryptographic devices, as physical leakage can reveal sensitive information such as cryptographic keys. Multi-channel fusion attacks (MCFAs) have proven to be efficient in exploiting side-channel leakages. However, a crucial step before performing MCFA is to assess whether the leakages from different channels can be effectively fused. To address this, we propose a black-box approach based on the χ2test of independence to assess multi-channel leakage suitability for fusion. By treating the leakages as categorical variables, our method evaluates their association without requiring knowledge of the cryptographic key or device implementation.

Industrial Internet of Things (IIoT) is the leading application scenario of the fifth generation wireless communication systems (5G) and beyond. Nonorthogonal multiple access (NOMA) has become a key technology for 5G due to its high spectrum efficiency. In this article, a joint channel and power resource allocation problem is investigated for cross-cell IIoT networks with aim of maximizing sum rate of NOMA-based machine-to-machine pairs and cellular Machine Devices (cMDs). Since joint channel and power resource allocation problem is an NP-hard problem, the original problem is transformed into a hypergraph model to optimize channel and power resource allocation. Then, a channel allocation algorithm based on hypergraph coloring theory is proposed, and an alternative power allocation algorithm is presented. Next, some properties of hypergraph coloring and complexities are analyzed. Finally, simulation results demonstrate that the proposed algorithm outperforms the graph-based algorithm in terms of sum rate, and also improves the spectrum efficiency significantly.

Internet of Vehicles (IoV) has become an indispensable data sensing and processing platform in Internet of Things (IoT) for intelligent transportation. The mounted cameras on the vehicles along with the fixed roadside cameras are utilized to provide pictorial services for IoV users and law enforcement agencies. For such forensic services, ensuring the security and privacy of vehicles while guaranteeing the efficiency of data transmission among vehicles is important. In this paper, we propose a lightweight incentive authentication scheme (LIAS) for forensic services in IoV. LIAS is developed on a three-tier architecture containing cloud layer, fog layer, and user layer. LIAS uses pairing-free certificateless signcryption, pseudonym update mechanism, and incentive mechanism to realize a secure anonymous authentication efficiently. We conduct correctness and security analysis, as well as performance analysis and evaluation to validate the high security and efficiency of LIAS. Experimental results reveal that, the communication and computation overheads as well as the message delay and packet loss of LIAS are much lower than those of state-of-the-art techniques. Note to Practitioners—This paper is motivated by the security and privacy issues of forensic services in IoV for intelligent transportation. Our goal is to improve the security and privacy of vehicles while guaranteeing the lightweight and incentive of data transmission among the vehicles. Fog-assisted IoV is introduced to fully utilize the capacities of near-user edge devices as well as the connections between fog nodes and devices. However, it still faces the difficulties in ensuring vehicles’ security and privacy. Moreover, vehicles’ information dissemination could be easily monitored because of the unavoidable defect of wireless communication. Thereby, it is essential to guarantee the security and privacy of vehicles while enhancing the efficiency of vehicles’ data transmission during the forensic service. To this end, this paper proposes a lightweight conditional anonymous authentication scheme for forensic services in IoV, which is developed based on the pairing-free technique to achieve secure anonymous authentication with high efficiency. This paper also designs a user tracing mechanism, incentive mechanism, and pseudonym update mechanism to realize safe and effective forensic service in IoV.

Realistic epidemic spreading is usually driven by traffic flow in networks, which is not captured in classic diffusion models. Moreover, the progress of a node's infection from mild to severe phase has not been particularly addressed in previous epidemic modeling. To address these issues, we propose a novel traffic-driven epidemic spreading model by introducing a new epidemic state, that is, the severe state, which characterizes the serious infection of a node different from the initial mild infection. We derive the dynamic equations of our model with the tools of individual-based mean-field approximation and continuous-time Markov chain. We find that, besides infection and recovery rates, the epidemic threshold of our model is determined by the largest real eigenvalue of a communication frequency matrix we construct. Finally, we study how the epidemic spreading is influenced by representative distributions of infection control resources. In particular, we observe that the uniform and Weibull distributions of control resources, which have very close performance, are much better than the Pareto distribution in suppressing the epidemic spreading.

Outlier detection has been widely explored and applied to different real-world problems. However, outlier characterization that consists in finding and understanding the outlying aspects of the anomalous observations is still challenging. In this paper, we present a new approach to simultaneously detect subspace outliers and characterize them. We introduce the Dimension-wise Local Outlier Factor (DLOF) function to quantify the degree of outlierness of the data points in each feature dimension. The obtained DLOFs are used in an outlier ensemble so as to detect and rank the anomalous points. Subsequently, the same DLOFs are analyzed in order to characterize the detected outliers with their relevant subspace and their same-type anomalies. Experiments on various datasets show the efficacy of our method. Indeed, we demonstrate through an experimental evaluation that the proposed approach is competitive compared to the existing solutions in terms of both detection and characterization accuracy.

Trojan attacks on deep neural networks (DNNs) exploit abackdoorembedded in a DNN model that can hijack any input with an attacker’s chosen signature trigger. Emerging defence mechanisms are mainly designed and validated on vision domain tasks (e.g., image classification) on 2D Convolutional Neural Network (CNN) model architectures; a defence mechanism that is general across vision, text, and audio domain tasks is demanded. This work designs and evaluates a run-time Trojan detection method exploitingSTRongIntentionalPerturbation of inputs that is a multi-domain input-agnostic Trojan detection defence acrossVision,Text andAudio domains—thus termed as STRIP-ViTA. Specifically, STRIP-ViTA is demonstratively independent of not only task domain but also model architectures. Most importantly, unlike other detection mechanisms, it requires neither machine learning expertise nor expensive computational resource, which are the reason behind DNN model outsourcing scenario—one main attack surface of Trojan attack. We have extensively evaluated the performance of STRIP-ViTA over: i) CIFAR10 and GTSRB datasets using 2D CNNs for vision tasks; ii) IMDB and consumer complaint datasets using both LSTM and 1D CNNs for text tasks; and iii) speech command dataset using both 1D CNNs and 2D CNNs for audio tasks. Experimental results based on more than 30 tested Trojaned models (including publicly Trojaned model) corroborate that STRIP-ViTA performs well across all nine architectures and five datasets. Overall, STRIP-ViTA can effectively detect trigger inputs with small false acceptance rate (FAR) with an acceptable preset false rejection rate (FRR). In particular, for vision tasks, we can always achieve a 0 percent FRR and FAR given strong attack success rate always preferred by the attacker. By setting FRR to be 3 percent, average FAR of 1.1 and 3.55 percent are achieved for text and audio tasks, respectively. Moreover, we have evaluated STRIP-ViTA against a number of advanced backdoor attacks and compare its effectiveness with other recent state-of-the-arts.

Cloud storage services give users a great facility in data management such as data collection, storage and sharing, but also bring some potential security hazards. An utmost importance is how to ensure the integrity of data files stored in the cloud, particular for user groups without trusted managers. Existing literature focuses on integrity checking for groups with managers who have lots of permissions. To overcome the shortage of public auditing for non-manager user groups in clouds, we develop a novel framework IPANM that integrates$(t,n)$threshold technology, blinding technology, and incentive mechanism to realize an incentive privacy-preserving public auditing scheme. In IPANM, the data integrity is guaranteed by our$(t,n)$threshold signature based public auditing and the data privacy during public auditing is protected by the blinding technology. The generation of signatures can be accelerated by our blockchain-aided incentive mechanism that mobilizes the initiative of signers in the signature generation by rewarding the contributed signers. We formally prove the security of our IPANM and conduct numerical analysis and evaluation study to validate its high efficiency. The experimental results demonstrate that IPANM has lower overheads of storage, communication, and computation as compared to the state-of-the-art technique IAID-PDP and NPP.

Maximizing profit is a key goal for cloud service providers in the modern cloud business market. Service revenue and business cost are two major factors in determining profit and highly depend on multiserver configuration. Understanding the relationship between multiserver configuration and profit is important to service providers. Although existing articles have explored this issue, few of them consider deadline miss rate and soft error reliability of cloud services in multiserver configuration for profit maximization. Since deadline misses violate cloud services’ real-time requirements and soft error prevents successful processing of cloud services, it is necessary to consider the impact of deadline miss rate and soft error reliability on service providers’ profits when configuring the multiserver. This article introduces a deadline miss rate and soft error reliability aware multiserver configuration scheme for maximizing cloud service providers’ profit. Specifically, we derive the deadline miss rate considering the heterogeneity of cloud service requests, and propose an analytical method to compute the soft error reliability of multiserver systems. Based on the new deadline miss rate and soft error reliability models, we formulate the multiserver configuration optimization problem and introduce an augmented Lagrange multiplier-based iterative method to find the optimal multiserver configuration. Extensive experiments evaluate the efficacy of the proposed multiserver configuration approach. Compared with the two state-of-the-art methods, the profit gained by our scheme can be up to 11.92% higher.

Heterogeneous MPSoCs consisting of integrated CPUs and GPUs are suitable platforms for embedded applications running on handheld devices such as smart phones. As the handheld devices are mostly powered by battery, the integrated CPU and GPU MPSoC is usually designed with an emphasis on low-power rather than performance. In this paper, we are interested in exploring a power-efficient layer mapping of convolution neural networks (CNNs) deployed on integrated CPU and GPU platforms. Specifically, we investigate the impact of layer mapping of YoloV3-Tiny (i.e., a widely-used CNN in both industry and academia) on system power consumption through numerous experiments on NVIDIA board Jetson TX2. The experimental results indicate that 1) almost all of the convolution layers are not suitable for mapping to CPU, 2) the pooling layer can be mapped to CPU for reducing power consumption, but the mapping may lead to a decrease in inference speed when the layer's output tensor size is large, 3) the detection layer can be mapped to CPU as long as its floating-point operation scale is not too large, and 4) the channel and upsampling layers are both suitable for mapping to CPU. These observations obtained in this study can be further utilized to guide the design of power-efficient layer mapping strategies for integrated CPU and GPU platforms.

To ensure data security and privacy during big data publishing, it is challenging to design a security and privacy protection scheme for the big data environment with a large scale of users. At the same time, due to the users' dynamically joining and exiting, it is also very important to design a user's dynamic update mechanism. To address such challenges, we design a novel scalable and updatable attribute-based privacy protection scheme (SUAPP) for big data publishing. The proposed scheme can realize users' hierarchical management, which can reduce the overhead on key generation and management caused by the large scale of data users in the big data center (BDC). We set a user group for each attribute, then adapt the Chinese remaining theorem to dynamically assist the big data center to generate and update group keys for the attribute users group. Analyses and experiments show that while ensuring the privacy protection of big data publishing, our scheme also has low communication and computation overhead and higher efficiency compared with two state peer schemes.

Nowadays, computers, as well as smart devices, are connected through communication networks making them more vulnerable to attacks. Honeypots are proposed as deception tools but usually used as part of a proactive defense strategy. Hence, this article demonstrates how honeypots data can be analyzed in an active defense strategy. Furthermore, anomaly detection based on unsupervised machine learning techniques allows to build autonomous systems and to detect unknown anomalies without the need for prior knowledge. However, the unsupervised techniques applied for honeypots data analysis do not value the advantages of these tools’ data, particularly the high probability that they include a large number of previously unseen anomalies with unexpected and diverse patterns. Therefore, in the present work, the aim is to improve the unsupervised anomaly detection in honeypots data by varying the data feature subset and the parameterization of the anomaly detection algorithm. To this purpose, an outlier ensemble with LOF (Local Outlier Factor) as a base algorithm is proposed. The ensemble outperforms existing solutions as depicted in the experiments where a detection rate higher than 92% is achieved.

With the rapid development of Internet and cloud storage, data security sharing and copyright protection are becoming more and more important. In this paper, we introduce a robust image watermarking algorithm for copyright protection based on variational autoencoder networks. The proposed image watermarking embedding and extracting network consists of three parts: encoder subnetwork, decoder subnetwork, and detector subnetwork. In the training process, the encoder and decoder subnetworks learn a robust image representation model and further implement the embedding of 1-bit watermark image to the cover image. Meanwhile, the detector subnetwork learns to extract the 1-bit watermark image from the embedding image. Experimental results demonstrate that the watermarked images generated by the proposed algorithm have better visual effects and are more robust against geometric and noise attacks than traditional approaches in the transform domain.

Along with the wide deployment of infrastructures and the rapid development of virtualization techniques in cloud computing, more and more enterprises begin to adopt cloud services, inspiring the emergence of various cloud service providers. The goal of cloud service providers is to pursue profit maximization. To achieve this goal, cloud service providers need to have a good understanding of the economics of cloud computing. However, the existing pricing strategies rarely consider the interaction between user requests for services and the cloud service provider and hence cannot accurately reflect the supply and demand law of the cloud service market. In addition, few previous pricing strategies take into account the risk involved in the pricing contract. In this article, we first propose a dynamic pricing strategy that is developed based on the customer perceived value (CPV) and is able to accurately capture the real situation of supply and demand in marketing. The strategy is utilized to estimate the user's demand for cloud services. We then design a profit maximization scheme that is developed based on the CPV-aware dynamic pricing strategy and considers the risk in the pricing contract. The scheme is utilized to derive the optimal multiserver configuration for maximizing the profit. Extensive simulations are carried out to verify the proposed customer perceived value and risk-aware profit maximization scheme. As compared to two state of the art benchmarking methods, the proposed scheme gains 31.6 and 30.8 percent more profit on average, respectively.

Summary In a data sharing group, each user can upload, modify, and access group files and a user is required to generate a new signature for the modified file after modification. There is a situation that two or more users modify the same file at almost the same time, which should be avoided as it gives rise to a signature conflict. However, the existing schemes do not take it into consideration. In this paper, we proposed a new mechanism SeShare for data storing based on blockchain to realize signature uniqueness, which solves the problem of generating signatures for the same file meanwhile by different group users. Specifically, we record every signature of a file in a blockchain in chronological order, and only one user is allowed to add new signature at the end of the blockchain when modification conflicts occur. On the other hand, to provide a secure data sharing service, SeShare introduces an efficient public auditing scheme for file integrity verification when a group user leaves the group. We also prove the security of the proposed scheme and evaluate the performance at the end of this paper. Our experimental results demonstrate the efficiency of public auditing for user leaving.

Both soft-error reliability (SER) due to transient faults and lifetime reliability (LTR) due to permanent faults are key concerns in real-time MPSoCs. Existing works have investigated related problems, however, most of them only focus on one of the two reliability concerns. A few efforts do consider both types of reliability together, but ignore the impacts of hardware- and application-level variations on reliability, thus are not applicable to state-of-the-art MPSoCs under variations. In this paper, we focus on increasing SER without sacrificing LTR since transient faults occur much more frequently than permanent faults. Specifically, we propose a novel task allocation and scheduling scheme to maximize SER while satisfying a LTR constraint for soft real-time MPSoCs. Considering that SER is the objective while LTR is a constraint in our problem, and LTR is highly related to core temperature profiles, we dedicate to investigating the effects of variations in core soft-error rate, task vulnerability to soft errors, and task execution time on SER. To the best of our knowledge, our work is the first attempt that jointly handles the two reliability issues as well as taking into account the effects of variations on reliability. Experimental results show that our scheme improves the SER by up to 66% as compared to a number of representative existing approaches while meeting the same LTR constraint.

In data sharing schemes, the file owners should obtain rewards by sharing files with others as they put energy in these files. Therefore, we proposed an incentive data sharing scheme in this paper which encourages users to share data and also supports customization. Customization allows the owners to decide the threshold of access, the importance of each attributive classification which determines users' priority level of file modification and file ownership obtaining when the original owner leaves according to the priority level value. To support a convincing customized data sharing scheme, we introduce the knowledge of blockchain and construct a suitable access structure based on weighted attributes. The blockchain is used to ensure the fairness in incentive. Based on weighted attributes, an attribute set is disposed to a numerical value and the owner of the attribute set is able to obtain the file when the value is not less than the threshold, which is different from the normal access control policy. We prove the security from integrity, privacy and the availability of access key. The performance of the proposed scheme is evaluated at the end of this paper.

Internet of Things devices, such as video-based detectors or road side units are being deployed in emerging applications like sustainable and intelligent transportation systems. Oftentimes, stringent operation and energy cost constraints are exerted on this type of applications, necessitating a hybrid supply of renewable and grid energy. The key issue of a cost-constrained hybrid of renewable and grid power is its uncertainty in energy availability. The characteristic of approximate computation that accepts an approximate result when energy is limited and executes more computations yielding better results if more energy is available, can be exploited to intelligently handle the uncertainty. In this paper, we first propose an energy-adaptive task allocation scheme that optimally assigns real-time approximate-computation tasks to individual processors and subsequently enables a matching of the cost-constrained hybrid supply of energy with the energy demand of the resultant task schedule. We then present a quality of service (QoS)-driven task scheduling scheme that determines the optional execution cycles of tasks on individual processors for optimization of system QoS. A dynamic task scheduling scheme is also designed to adapt at runtime the task execution to the varying amount of the available energy. Simulation results show that our schemes can reduce system energy consumption by up to 29% and improve system QoS by up to 108% as compared to benchmarking algorithms.

Due to the abundant storage resources and high reliability data service of cloud computing, more individuals and enterprises are motivated to outsource their data to public cloud platform and enable legal data users to search and download what they need in the outsourced dataset. However, in “Paid Data Sharing” model, some valuable data should be encrypted before outsourcing for protecting owner’s economic benefits, which is an obstacle for flexible application. Specifically, if the owner does not know who (user) will download which data files in advance and even does not know the attributes of user, he/she has to either remain online all the time or import a trusted third party (TTP) to distribute the file decryption key to data user. Obviously, making the owner always remain online is too inflexible, and wholly depending on the security of TTP is a potential risk. In this paper, we propose a flexible, secure, and reliable data sharing scheme based on collaboration in multicloud environment. For securely and instantly providing data sharing service even if the owner is offline and without TTP, we distribute all encrypted split data/key blocks together to multiple cloud service providers (CSPs), respectively. An elaborate cryptographic protocol we designed helps the owner verify the correctness of data exchange bills, which is directly related to the owner’s economic benefits. Besides, in order to support reliable data service, the erasure‐correcting code technic is exploited for tolerating multiple failures among CSPs, and we offer a secure keyword search mechanism that makes the system more close to reality. Extensive security analyses and experiments on real‐world data show that our scheme is secure and efficient.

Cloud data privacy-preserving and integrity verification have become major research areas. Many existing schemes use group signatures to make sure that the data stored in cloud is unbroken for the purpose of privacy and anonymity. However, group signatures do not consider user equality and the framing caused by manager. Therefore, we propose data sharing scheme for non-manager groups, which reconstructs democratic group signature with threshold traceability to homomorphic authentication. We further present a public auditing scheme for non-manager shared data. In our scheme, group manager's rights are distributed to all members equally and some of them can work together to trace the signer if it is necessary. Besides identity privacy, data privacy, traceability and non-frameability, our scheme also ensures the efficiency and the feasibility. The experimental results show the overhead of the auditing is independent of the group user numbers and effectiveness of our approach. And thanks to the low overhead, our scheme can be further used in mobile cloud storage.

With the arrival of big data era, cloud storage has become more ubiquitous. A growing number of consumers remote their data into cloud, as cloud can provide them with ample storage space and powerful computational capacity. However, storing data in cloud means that data is out of their control. How to verify the integrity of stored data and retrieve the corrupted data has become an urgent security problem. In this paper, we propose a new efficient proof of retrievability scheme, named as IPOR, for cloud storage systems. The IPOR not only can verify the integrity of remote data, but also can retrieve the original data of corrupted blocks from the healthy servers with probability 100%. Moreover, IPOR obviously decreases the complexity of data integrity tags and it requires performing a few multiplication and addition operations to retrieve the corrupted data. Therefore, our scheme is much more efficient than the state-of-the-art schemes. In addition, the security analysis indicates that our scheme is provably secure.

In cloud storage systems, data security management is becoming a serious matter. Big data and accessibility power is increasingly high, though the benefits are clear, such a service is also relinquishing users' physical possession of their outsourced data, which inevitably poses new security risks toward the correctness of the data in cloud. As a result, cloud storage security has become one of the driving components in Cloud Computing regarding to data manipulation trust on both hosting center and on-transit. This paper proposes a TPM-Based Security over Multi-Cloud Storage Architecture (MCSA) grounded on Erasure Codes to apply root of trust based on hardware authenticity. An erasure codes such as Reed-Solomon, is capable of assuring stability in storage costs with best practice to guarantee data accessibility failure recovery. A Multi-Cloud Control Node manages other Control Nodes evolved in the cloud; this work introduces TPM-Based Security functions per Control node in the architecture. This concept will resolve a number of storage security issues, hence Cloud Computing adoption.

Abstract Machine‐type communication (MTC) is a very important application of the Internet of things. It has a vast market and application scenarios. However, supporting a large number of low‐power devices transmission is an important issue in long‐term evolution/long‐term evolution advanced (LTE/LTE‐A) networks. Specifically, when a large number of machine‐type communication devices (MTCDs) with low‐power consumption requirements simultaneously request access to the LTE/LTE‐A networks, each MTCD needs an independent complete access authentication process with core network, which may cause a serious signaling congestion in the core network. To solve this problem, in this paper, we propose a novel group authentication protocol with privacy‐preserving for MTC in the LTE/LTE‐A networks. The proposed protocol cannot only simultaneously authenticate a group of MTCDs and minimize the signaling overhead but also provide robust privacy‐preserving for each MTCD (including anonymity, unlinkability, and traceability). In particular, our scheme can avoid denial of service attack by filtering some illegal devices in the first four procedures of the mutual authentication. Moreover, our scheme fulfills all the security requirements of the MTC in LTE/LTE‐A networks. In addition, the formal verification by the ProVerif tool shows that the proposed scheme is secure against various malicious attacks, and the performance evaluation indicates that it achieves outstanding results in terms of signaling and computation overhead. Copyright © 2016 John Wiley & Sons, Ltd.

Cloud computing provides promising platforms for executing large applications with enormous computational resources to offer on demand. In a Cloud model, users are charged based on their usage of resources and the required quality of service (QoS) specifications. Although there are many existing workflow scheduling algorithms in traditional distributed or heterogeneous computing environments, they have difficulties in being directly applied to the Cloud environments since Cloud differs from traditional heterogeneous environments by its service-based resource managing method and pay-per-use pricing strategies. In this paper, we highlight such difficulties, and model the workflow scheduling problem which optimizes both makespan and cost as a Multi-objective Optimization Problem (MOP) for the Cloud environments. We propose an evolutionary multi-objective optimization (EMO)-based algorithm to solve this workflow scheduling problem on an infrastructure as a service (IaaS) platform. Novel schemes for problem-specific encoding and population initialization, fitness evaluation and genetic operators are proposed in this algorithm. Extensive experiments on real world workflows and randomly generated workflows show that the schedules produced by our evolutionary algorithm present more stability on most of the workflows with the instance-based IaaS computing and pricing models. The results also show that our algorithm can achieve significantly better solutions than existing state-of-the-art QoS optimization scheduling algorithms in most cases. The conducted experiments are based on the on-demand instance types of Amazon EC2; however, the proposed algorithm are easy to be extended to the resources and pricing models of other IaaS services.

Based on Petri net (PN) models of automated manufacturing systems, this paper proposes a deadlock prevention method to obtain a maximally permissive (optimal) supervisor while minimizing its structure. The optimal supervisor can be achieved by forbidding all first-met bad markings (FBMs) and permitting all legal markings in a PN model. An FBM obtained via a single transition's firing at a legal marking is a deadlock or marking that inevitably evolves into a deadlock. A lexicographic multiobjective integer programming problem with multiple objectives to be achieved sequentially is formulated to design such an optimal and structurally minimal supervisor. As a nonlinear function, the quantity of its directed arcs is minimized. A conversion method is proposed to convert the nonlinear model into a linear one. With the premise that each place in the supervisor is associated with a nonnegative place invariant, the controlled net holds all legal markings of the net model, and the supervisor has the minimal structure. Finally, some examples are used to illustrate the application of the proposed approach.

The above paper proposes a method to design optimal control places with self-loops for flexible manufacturing systems by solving an integer linear programming problem (ILPP) at each iteration. However, some constraints in the ILPP are redundant. This technical correspondence shows that they can be removed without changing the feasible region of the ILPP.

From the core concepts of SOA (Service-Oriented Architecture) ——"Service" starting the service composition is discussed in detail, from the service relationships network modeling, services dynamic composition approach based on Business Process Execution Language BPEL (Business Process Execution Language) is proposed in this paper, meanwhile two concepts of service agent and service quality are described, which achieve the service process dynamic execution.

To stay on the cutting edge, Web services ought to adapt themselves to the evolving business requirements and the changing environments. For a long-running service choreography, its member services may need to evolve even at run-time. However, inconsistencies or spurious results (e.g., unspecified receptions and deadlocks) may occur if these services evolve dynamically in an uncoordinated manner. To cope with this problem, we propose an approach that supports the dynamic evolution of choreographies. In our approach, two mechanisms are proposed to make sure that the choreography evolution can be conducted in an orderly fashion. First, an evolution protocol is proposed to support dynamic co-evolution of the member services in a choreography. Second, the proposed approach restricts choreography changes to one single service only if the relevant partner services can evolve simultaneously. A typical purchase order application is used to motivate our proposal and illustrate the viability of our approach.

A pattern is a general, reusable solution to a commonly occurring problem in software design. Programmers use patterns to produce effective and reliable software systems. However, pattern-related information usually reflects high-level user intentions that might not be available in source code when the coding process has been completed. Without proper documentation, those patterns may be concealed or destroyed during software maintenance and evolution. Being conscious of pattern occurrences can help understand source code and thereby provide deeper insight into a software product. In this paper, we argue that one should properly annotate object-oriented design patterns in source code and verify the consistency between patterns and code. This idea is demonstrated with the Singleton as well as the Strategy pattern, and we show how to assure some semantic aspects of these two patterns using a permission type system.