Wangyang Wei

A QoS-Satisfied Prediction Model for Cloud-Service Composition Based on a Hidden Markov Model

Various significant issues in cloud computing, such as service provision, service matching, and service assessment, have attracted researchers’ attention recently. Quality of service (QoS) plays an increasingly important role in the provision of cloud-based services, by aiming for the seamless and dynamic integration of cloud-service components. In this paper, we focus on QoS-satisfied predictions about the composition of cloud-service components and present a QoS-satisfied prediction model based on a hidden Markov model. In providing a cloud-based service for a user, if the user’s QoS cannot be satisfied by a single cloud-service component, component composition should be considered, where its QoS-satisfied capability needs to be proactively predicted to be able to guarantee the user’s QoS. We discuss the proposed model in detail and prove some aspects of the model. Simulation results show that our model can achieve high prediction accuracies.

Reputation Revision Method for Selecting Cloud Services Based on Prior Knowledge and a Market Mechanism

The trust levels of cloud services should be evaluated to ensure their reliability. The effectiveness of these evaluations has major effects on user satisfaction, which is increasingly important. However, it is difficult to provide objective evaluations in open and dynamic environments because of the possibilities of malicious evaluations, individual preferences, and intentional praise. In this study, we propose a novel unfair rating filtering method for a reputation revision system. This method uses prior knowledge as the basis of similarity when calculating the average rating, which facilitates the recognition and filtering of unfair ratings. In addition, the overall performance is increased by a market mechanism that allows users and service providers to adjust their choice of services and service configuration in a timely manner. The experimental results showed that this method filtered unfair ratings in an effective manner, which greatly improved the precision of the reputation revision system.

Research on Grade Optimization Self-tuning Method for System Dependability Based on Autonomic Computing

In order to enhance the service performance, the preservation and increase of system dependability are researched and a system dependability self-tuning method is proposed based on autonomic computing. The self-tuning method attempts to achieve the sustained growth of system dependability by on-line evaluation, dynamic prediction and tuning of self-tuning scheme. There are four tuning methods random interval, settled interval, dependability threshold value and event trigger, which are discussed in the simulation experimentation. The tuning effects that affect system dependability increment are analyzed, and the optimal tuning opportunities for each tuning method are given. The result of simulation experiment shows that the tuning methods will ensure the positive increase of system dependability increment except random interval.

A Novel Multi-x Cooperative Decision-making Mechanism for Cognitive Internet of Things

Cognitive Internet of Things (CIoT) mainly consists of a group of autonomous nodes (ANs) which are commonly combined into domains and should have the intelligence to perceive, analyze, decide and act. Cooperation has shown to be a good technique for collective behavior of ANs that locally interact with each other in distributed environments. In this paper, we study the cooperative decision-making mechanism of multi-ANs and multi-domains and present the corresponding cooperative decision-making process in CIoT. Multi-ANs cooperation deals with the cases that one AN cannot meet the QoS and network performance object (NPO) and multi-domains cooperation addresses the cases that the ANs of only one domain cannot meet the QoS and NPO. Based on the cooperative decision-making mechanism, the simulative experiments are done and show that the NPO can be satisfied perfectly.

B-iTRS: A Bio-Inspired Trusted Routing Scheme for Wireless Sensor Networks

In WSNs, routing algorithms need to handle dynamical changes of network topology, extra overhead, energy saving, and other requirements. Therefore, routing in WSNs is an extremely interesting and challenging issue. In this paper, we present a novel bio-inspired trusted routing scheme (B-iTRS) based on ant colony optimization (ACO) and Physarum autonomic optimization (PAO). For trust assessment, B-iTRS monitors neighbors’ behavior in real time, receives feedback from Sink, and then assesses neighbors’ trusts based on the acquired information. For routing scheme, each node finds routes to the Sink based on ACO and PAO. In the process of path finding, B-iTRS senses the load and trust value of each node and then calculates the link load and link trust of the found routes to support the route selection. Moreover, B-iTRS also assesses the route based on PAO to maintain the route table. Simulation results show how B-iTRS can achieve the effective performance compared to existing state-of-the-art algorithms.

A^3srC: Autonomic Assessment Approach to IOT Security Risk Based on Multidimensional Normal Cloud

Around autonomic assessment problem of security risk, considering the high hybrid and heterogeneity of Internet of Things (IOT, for short), the potential influence and occurrence possibility of each main threat is described, evaluated and measured in accordance with fuzziness and randomicity of IOT security index. By which, the security risk grade and system tolerance degree of hybrid IOT security scene with incremental deployment characteristics is qualitatively analyzed. Multidimensional Normal Cloud is adopted to make synthesis of risk indicators, and the autonomic assessment strategy of security risk is constructed. On the basis of above work, transmission multi-rules mapping between qualitative input of security risk and quantitative inference of assessment rules is researched, autonomic inference rules and the optimization strategy are proposed. At last, the effectiveness, autonomy and accuracy of the proposed approach are verified by simulations.

Network Security Situation Evaluation Strategy Based on Cloud Gravity Center Judgment

Aiming at the problems of subjectivity and complexity in network security situation assessment process, the Cloud model is introduced to the network security situation assessment, and a network security evaluation method based on Cloud Model is proposed. Firstly, the level of network security situation is judged by Cloud gravity center evaluation method. Secondly, Maximum boundary based Cloud Model (MCM) method is applied to obtain the final assessment results. Thirdly, future security situation is predicted by adopting BP neural network based on improved genetic algorithm according to the result of situation assessment. Finally, the effectiveness and feasibility of the proposed evaluation method and the prediction effect for GA-BPNN are verified by the simulation experiment. The results show that the proposed evaluation and prediction method is feasible, and the results of the assessment are more objective and accurate.

P-bRS: A Physarum-Based Routing Scheme for Wireless Sensor Networks

Routing in wireless sensor networks (WSNs) is an extremely challenging issue due to the features of WSNs. Inspired by the large and single-celled amoeboid organism, slime mold Physarum polycephalum, we establish a novel selecting next hop model (SNH). Based on this model, we present a novel Physarum-based routing scheme (P-bRS) for WSNs to balance routing efficiency and energy equilibrium. In P-bRS, a sensor node can choose the proper next hop by using SNH which comprehensively considers the distance, energy residue, and location of the next hop. The simulation results show how P-bRS can achieve the effective trade-off between routing efficiency and energy equilibrium compared to two famous algorithms.

Simulation on System Dependability Self-Tuning Method Based on Grade Optimization

Ensuring system dependability is a key problem to increase the user service performance. Depending on transcendental self-tuning knowledge, a system dependability self-tuning method based on grade optimization is proposed in this paper. It attempts to implement the sustained growth of system dependability by online dependability evaluation, dependability dynamic prediction and self-tuning scheme selection in turn, which accomplishes the self renewal of transcendental knowledge according to the realtime feedback of self-tuning strategy. The result of experiment shows that it will ensure the increase of system dependability increment.

A Service Self-Optimization Algorithm based on Autonomic Computing

Under the intrusion or abnormal attack, how to autonomously supply undegraded service to users is the ultimate goal of network securiy technology. Firstly, combined with martingale difference principle, a Service Self Optimization Algorithm based on Autonomic Computing-S2OAC is proposed. Secondly, according to the prior self optimizing knowledge and parameter information of inner environment, S2OAC searches the convergence trend of self optimizing function and executes the dynamic self optimization, aiming at minimum the optimization mode rate and maximum the service performance. Thirdly, set of the best optimization mode is updated and prediction model is renewed, which will implement the static self optimization and improve the accuricy of self optimization prediction. At last, the simulation results validate the efficiency and superiority of S2OAC.