With the rapid development of software-defined networking/network function virtualization (NFV) technologies, service function chaining (SFC) has become a key enabler for end-to-end service provisioning in future networks. In the Internet of Things (IoT), the highly dynamic nature of the network environment demands flexible and adaptive mechanisms for dynamic SFC deployment to fully utilize network resources while meeting the service requirements. Although reinforcement learning (RL) techniques offer a promising approach to dynamic SFC deployment, the learning delay of RL may limit its prompt response to sudden changes in network state and/or service demand. To address this challenge in this article, we propose to employ a deep$Q$-learning network (DQN) method for dynamic SFC deployment combined with a tidal virtual machine (TVM) control mechanism for adaptive virtual machine (VM) auto-scaling. We present a tidal DQN framework (TDQNF) that integrates the DQN method and TVM control in the ETSI NFV architecture and develop the algorithms for implementing DQN-based decisions for SFC deployment and TVM control for VM scaling. The performance of the TDQNF framework with the proposed algorithms has been evaluated through extensive simulation experiments. The obtained experimental results verify the effectiveness of the proposed scheme and indicate better performance in terms of system delay, packet loss, and load balancing in large-scale networks compared to existing methods.

Sharded blockchain offers scalability, decentralization, immutability, and linear improvement, making it a promising solution for addressing the trust problem in large-scale collaborative IoT. However, a high proportion of cross-shard transactions can severely limit the performance of decentralized blockchain. Furthermore, the dynamic assemblage characteristic of collaborative sensing in sharded blockchain is often ignored. To overcome these limitations, we propose HMMDShard, a dynamic blockchain sharding scheme based on the Hidden Markov Model. HMMDShard leverages fine-grained blockchain sharding and fully embraces the dynamic assemblage characteristic of IoT collaborative sensing. By integrating the Hidden Markov Model, we achieve adaptive dynamic incremental updating of blockchain shards, effectively reducing cross-shard transactions across all shards. We conduct a comprehensive analysis of the security issues and properties of HMMDShard, and evaluate its performance through the implementation of a system prototype. The results demonstrate that HMMDShard significantly reduces the proportion of cross-shard transactions and outperforms other baselines in terms of system throughput and transaction confirmation latency.

Summary The micro‐blog network is one of the most popular social networking platforms. By calculating the degree centrality, we found that hub nodes play an important role in micro‐blog networks, which is the main power of message forwarding. To improve the security of the micro‐blog network, we proposed a defending scheme against malicious Uniform Resource Locator (URL) diffusing in micro‐blog networks with hub nodes. After a node found a new malicious URL, it will edit a warning massage about the malicious URL. If the normal node obtains malicious URL warning message, it will send private message with the warning message to the hub node and update its blog article. The malicious URL warning messages spread rapidly in the whole networks in a short period because of the influence of Hub nodes. At the same time, we add the comparison mechanism to reduce the redundancy of spreading the warning message in the networks. So the security of entire micro‐blog networks can be improved against malicious URL without increasing the network load. Experiments show that our scheme can effectively defend against the malicious URL in the any scale of micro‐blog networks. Copyright © 2016 John Wiley & Sons, Ltd.

The correctness of implementation codes is important especially for safety-critical software usually written in C programming language. We present a correctness verification method (CVM for short) for C codes based on an automatic theorem proving tool-VCC, and propose a specification simplification method to im-prove the correctness and readability of verification specification codes. Using CVM method, the scheduling module of a real-time operating system FreeRTOS6.1.1 is verified, which shows the feasibility and effectiveness when CVM method is applied to the real production software. Experiments show that the CVM method is feasible and effective in verifying the correctness the C codes, and the specification simplification method is also effective.

Traditional web services transaction processing mechanism handle exception by forward recovery and backward recovery. These compensation mechanisms often lead to waste of resources and time. In this paper, we propose a framework for predicting outcomes of service executions as part of service compositions which allows to choose service instances that are likely to lead to a successful result in the first instance and thus reduces the need for invoking costly recovery mechanisms. The framework makes use of watchdogs to maintain an awareness of service availability and a pre-coordinator which has oversight of the whole composite Web service and acts as a control center. An analysis of a scenario shows that we cannot only provide users with a more satisfactory result, but also can reduce the overhead costs of resources and waste.

Program bugs may result in unexpected software error, crash or serious security attack. Static program analysis is one of the most common methods to find program bugs. In this paper we present MLSA - a static analysis tool based on LLVM Intermediate Representation (IR), which can analyze programs written in multiple programming languages. MLSA combines symbolic execution with Z3 SMT solver to find bugs. At present, MLSA can detect some kinds of bugs, such as divide zero error, pointer overflow and dead code. Moreover, as a framework, MLSA follows the scalability and extensibility principles, which can help detect other types of bugs. Experiments show that MLSA is effective in finding bugs in real world software.

Nowadays, the ubiquity of smart phones make them carry large amounts of personal sensitive information, but at the same time, there are also many Apps in Android APP market that target to collect users' sensitive data. So it becomes quite important to prevent users from the threat of privacy leakage. In this paper, we analyze the Android's privacy protection mechanism, and describe various threats to users' different types of privacy data. After that, we enumerate two ways that can leak sensitive information, and discuss the current solutions and techniques from aspects of privacy protection enhancement and privacy leakage detection. We also make a fine-grained classification for these two aspects, and study the difference between solutions in each category. Finally, we summarize the deficiency of existing research of Android privacy protection and propose the future research direction.

In the Cloud computing community, the calculation of the reputation using the feedback of cloud customers is widely adopted to address the issue of trustworthiness of cloud services. Currently, most methods pursue a global reputation score essentially assuming that the value of a cloud service's reputation is the same for every consumer. However, depending on the expectations and needs of a consumer, there can be significant deviation of perceived reputation for the same cloud service. In this paper we propose a trust management framework that differentiates reputation for various user groups thus providing what we term local reputation. To achieve this we compute the similarity of consumers based a decision-tree model which is used to cluster feedback into localised scores. To refine the result, a time decay factor applicable to feedback is also to be considered. The simulation results illustrate that our approach is feasible and also effective for consumers to choose reputable cloud service.

Web services are developed independently and deployed in a distributed environment, new service can be obtained by composing existing ones. The rapid introduction of new services also results in undesirable interactions between services. These conflicts are not mismatches of interfaces, but are usually based on the data in the executing instance and therefore runtime management of conflicts in Web services should be considered. We study the problem from the perspective of user's revenue, and propose an online approach to resolve conflicts is proposed.

With the rapid development of web service standards and technology, the number of web services on Internet is increasing rapidly. Consequently, discovering the right service to meet a user's requirements quickly and accurately is crucial for the service community. Many web service discovery methods use web service models with semantic descriptions based on ontologies, allowing to apply logical reasoning to the discovery task. However, requiring logical reasoning can lead to sacrifices in efficiency of web services discovery. To address this problem, this paper proposes a combination of ontology encoding with the similarity of information content approach. We encode the concepts in the ontology in a binary encoding in order to improve the discovery efficiency and then we calculate the semantic similarity of information content between services. Validation of efficiency of the proposed approach is conducted through an experiment using the owls-tc2.0 as benchmark test set. The experimental results show that the proposed method not only can improve the efficiency of service discovery, but also can significantly improve the accuracy of service discovery compared with other discovery methods.

Service-Oriented Computing is benefit of interoperation among services. Current service-oriented computing research is much more concerning the low level interoperation among services, such as service discovery, service composition etc. However, the high level research issue-the feature interaction problem is also challenging the interoperation of service-oriented computing. Traditional feature interaction methods are focused on the service design phrase with formal methods or software engineering analysis. Autonomy and distribution of service deploying style have made the needs of runtime detecting and resolving feature interaction in SOC research community. This paper investigates the detection of feature interactions in web services at runtime and proposes ESTRIPs, an extended STRIPS operation conflict-free of services in business process detection method, which reasons from OWL-S and SWRL combined with runtime SOAP messages. First, we give the model of the feature interaction problem in business process during its execution and then the ESTRIPS method given in detail. The implementation of a prototype is illustrated. Using a real world scenario shows the plausibility of our method of detecting feature interactions of business process.

Feature interaction has been identified as a problem in the telecommunications domain in the 1980s, but since it has been shown to be a problem of systems that are composed of individually designed components. Clearly Web service composition is a way of building services from independently designed components and hence is subject to the same problem. This paper investigates the detection of feature interactions in Web services at runtime and proposes a novel detection method by taking inspiration from the Situation Calculus. Two case studies show that it is effective for detecting feature interactions in composite Web services.

Automatic Web services composition can be achieved by using AI planning techniques. HTN planning has been adopted to handle the OWL-S Web service composition problem. However, existing composition methods based on HTN planning have not considered the choice of decompositions available to a problem which can lead to a variety of valid solutions.In this paper, we propose a model of combining a Markov decision process model and HTN planning to address Web services composition. In the model, HTN planning is enhanced to decompose a task in multiple ways and hence be able to find more than one plan,taking both functional and non-functional properties into account. Furthermore, an evaluation method to choose the optimal plan and some experimental results illustrate that the proposed approach works effectively.

One of the main benefits of web services is the dynamic composability, however how to achieve this is one of the current research challenges. Web service composition has been studied and, amongst other methods, the use of natural computing methods has been proposed previously. In this paper, we address the need for a fast response when computing the most suitable sequence of services. In particular, we propose a novel heuristic immune algorithm with an efficient encoding and mutation method. The algorithm involves two steps: an immune selection operation, which is maintaining antibody population diversity and a clonal selection. The use of a vaccine during the evolution provides heuristic information that accelerates the convergence. Our experimental results illustrate that the proposed heuristic immune algorithm is very effective in improving the convergence speed.