Trustworthy Service Selection Integrating Cloud Model and Possibility Degree Ranking of Interval Numbers

It has been challenging to select suitable services from abundant candidates in cloud environment. Aiming to the characteristics of batch computing mode and stream computing mode, a novel trustworthy service selection approach is proposed integrating cloud model and interval numbers theory. To facilitate potential users to understand the quality of service, the trustworthiness of service is described with interval number using reverse cloud generator, and the services with poor performance are filtered out by employing deviation degree or proximity degree. Two formulas of possibility degree of interval numbers are designed to compare the trustworthiness values between cloud services by utilizing probability zone analysis and geometrical analysis respectively, and the ranking method for possibility degree of interval numbers is exploited to select the most trustworthy service. The experiments show that this approach is effective to improve the accuracy of service selection and select trustworthy service for potential users in cloud paradigm.     

A Retrievable Data Perturbation Method Used in Privacy-Preserving in Cloud Computing

With the increasing popularity of cloud computing,privacy has become one of the key problem in cloud security.When data is outsourced to the cloud,for data owners,they need to ensure the security of their privacy;for cloud service providers,they need some information of the data to provide high QoS services;and for authorized users,they need to access to the true value of data.The existing privacy-preserving methods can＇t meet all the needs of the three parties at the same time.To address this issue,we propose a retrievable data perturbation method and use it in the privacy-preserving in data outsourcing in cloud computing.Our scheme comes in four steps.Firstly,an improved random generator is proposed to generate an accurate ＂noise＂.Next,a perturbation algorithm is introduced to add noise to the original data.By doing this,the privacy information is hidden,but the mean and covariance of data which the service providers may need remain unchanged.Then,a retrieval algorithm is proposed to get the original data back from the perturbed data.Finally,we combine the retrievable perturbation with the access control process to ensure only the authorized users can retrieve the original data.The experiments show that our scheme perturbs date correctly,efficiently,and securely.     

Access Control Policy Analysis and Access Denial Method for Cloud Services

Personal cloud computing is an emerging trend in the computer industry. For a sustainable service, cloud computing services must control user access. The essential business characteristics of cloud computing are payment status and service level agreement. This work proposes a novel access control method for personal cloud service business. The proposed method sets metadata, policy analysis rules, and access denying rules. Metadata define the structure of access control policies and user requirements for cloud services. The policy analysis rules are used to compare conflicts and redundancies between access control policies. The access denying rules apply policies for inhibiting inappropriate access. The ontology is a theoretical foundation of this method. In this work, ontologies for payment status, access permission, service level, and the cloud provide semantic information needed to execute rules. A scenario of personal data backup cloud service is also provided in this work. This work potentially provides cloud service providers with a convenient method of controlling user access according to changeable business and marketing strategies.     

Parallelized User Clicks Recognition from Massive HTTP Data Based on Dependency Graph Model

With increasingly complex website structure and continuously advancing web technologies,accurate user clicks recognition from massive HTTP data,which is critical for web usage mining,becomes more difficult.In this paper,we propose a dependency graph model to describe the relationships between web requests.Based on this model,we design and implement a heuristic parallel algorithm to distinguish user clicks with the assistance of cloud computing technology.We evaluate the proposed algorithm with real massive data.The size of the dataset collected from a mobile core network is 228.7GB.It covers more than three million users.The experiment results demonstrate that the proposed algorithm can achieve higher accuracy than previous methods.     

Application of Rollout Strategy to Test Points Selection for Integer-Coded Fault Wise Table

Test points selection for integer-coded fault wise table is a discrete optimization problem. The global minimum set of test points can only be guaranteed by an exhaustive search which is eompurationally expensive. In this paper, this problem is formulated as a heuristic depth-first graph search problem at first. The graph node expanding method and rules are given. Then, rollout strategies are applied, which can be combined with the heuristic graph search algorithms, in a computationally more efficient manner than the optimal strategies, to obtain solutions superior to those using the greedy heuristic algorithms. The proposed rollout-based test points selection algorithm is illustrated and tested using an analog circuit and a set of simulated integer-coded fault wise tables. Computa- tional results are shown, which suggest that the rollout strategy policies are significantly better than other strategies.     

Web service selection algorithm based on principal component analysis

Existing Web service selection approaches usually assume that preferences of users have been provided in a quantitative form by users. However, due to the subjectivity and vagueness of preferences, it may be impractical for users to specify quantitative and exact preferences. Moreover, due to that Quality of Service (QoS) attributes are often interrelated, existing Web service selection approaches which employ weighted summation of QoS attribute values to compute the overall QoS of Web services may produce inaccurate results, since they do not take correlations among QoS attributes into account. To resolve these problems, a Web service selection framework considering user’s preference priority is proposed, which incorporates a searching mechanism with QoS range setting to identify services satisfying the user’s QoS constraints. With the identified service candidates, based on the idea of Principal Component Analysis (PCA), an algorithm of Web service selection named PCA-WSS (Web Service Selection based on PCA) is proposed, which can eliminate the correlations among QoS attributes and compute the overall QoS of Web services accurately. After computing the overall QoS for each service, the algorithm ranks the Web service candidates based on their overall QoS and recommends services with top QoS values to users. Finally, the effectiveness and feasibility of our approach are validated by experiments, i.e. the selected Web service by our approach is given high average evaluation than other ones by users and the time cost of PCA-WSS algorithm is not affected acutely by the number of service candidates.     

A K-Median Facility Location Agent for Low-Cost Service Selection in Digital Community Network

With the increasing number of web services, it becomes a difficult task for an ordinary user to select an appropriate service. Hence, it is conventional that users in a digital community network take part in a collaborative mechanism for the purpose of service selection. The participation usually brings unnecessary burdens for users, such as giving opinions, storing service information. Extra communication overhead hinders the performance of the network. Thus, the community administrators are facing a problem of how to obtain an overall service selection result for the whole community readily and effectively. To address this problem, we present a k-median facility location agent model. The model analyzes the procedure of service selection through five entities and six types of messages. Two algorithms are elaborated in pursuit of a global optimization concerning connection costs between users and facilities where services are deployed. To evaluate our model, we conduct extensive simulations and present a detailed analysis of the simulation results.     

QoS-aware scheduling algorithm in relay-assisted LTE-A system

This paper details on the uplink scheduling algorithm for long term evolution advanced （LTE-A） system with relays. While emulating quality of service （QoS）-aware services with different bit-rate and delay budget requirements for the upstream direction, a new QoS-aware scheduling algorithm for in-band relays is proposed. In this work, an improved scheduling metric calculation method and bit-rate guarantee scheme is applied. Moreover, this algorithm proposes an efficient scheme for the backhaul link allocation which allows information of the most backlogged users to be transmitted first. Finally, this paper concludes with simulation results to demonstrate how the proposed resource allocation strategy improves the performance of the system.     

Heterogeneous wireless network selection algorithm based on group decision

In the environment of heterogeneous wireless networks, it is vital to select a currently optimal network for applications and subscribers. The use of multiple attribute decision making （MADM） for heterogeneous network selection can provide subscribers with satisfactory service quality. Converting heterogeneous network selection into a MADM problem, the authors present an improved algorithm for MADM based on group decision theory. The algorithm combines weight vectors of multiple attribute decision making to obtain a combinational weight vector. Then the results＇ compatibility will be assessed. If they do not meet the requirements of compatibility, the judgment matrix will be modified until a comprehensive vector that satisfies compatibility requirements is produced. The vector is combined with simple weighting method （SAW） for network selection. Simulation shows that the algorithm can provide users with satisfactory quality of service （QoS）.     

Rapid Service-Oriented Business Process Generation Method Based on Ontology

Service-oriented business process generation is a key activity in the Service- Oriented Architecture （SOA） business lifecy- cle, and most of the other activities such as application execution depend on the business process being developed, After the business requirements are acquired, a developer has to use specific programming technologies to or- chestrate web services to generate a deploy- able business process. It is time-consuming to specify all the business processes from low- level web services, especially for an enterprise that focuses on a series of similar businesses. This paper proposes a rapid service-oriented business process generation method with do- main-specific assets specified in ontology sy- stems. Assets with different levels ,of granu- larity are reused to refine the high level bus- iness process framework for executable busin- ess processes using the Business Process Exe- cutional Language （BPEL）. The new method- ology significantly simplifies service-oriented business process generation by reusing assets to construct business processes. A business pro- cess generation tool is also implemented to support the efficient visual design of SOA pro- cesses with the proposed method. This paper verifies the proposed method using a shipment tracking case. The studies show that the num- ber of reusable assets increases significantly as these projects progress, and the business pro- cess generation speed also increases at the same time.     

QoS-Aware Semantic Web Service Composition with Uncertainties

Choosing the optimal execution plan is an important process of semantic web service composition. However, Quality of service （QoS） based plan selection is still challenging because the uncertain QoS values and uncertain user preference make data aggregation and decision making hard. This paper presents a novel Uncertain multiattribute decision making-based semantic web service composition algorithm （UMC） to solve the above difficulties for the first time. UMC takes all possible QoS expression types （real number, interval and linguistic） into consideration and includes three main steps： defuzzifying linguistic data, normalizing the decision matrix and evaluating alternatives synthetically. Further, the strategies of uncertain preference conversion are also discussed in UMC. Other contributions of the paper include an extensible QoS ontology to express the relative concepts, a QoS aggregation algorithm and a set of experiments that demonstrate the effectiveness and benefits of our approach.     

A Service Mining Scheme Based on Semantic for Internet of Things

As the development of Internet of things （IOT）, massive sensors have been deployed as the public infrastructure. With the development of in-depth applica- tions in IOT, service discovery and composition are chal- lenges to the end users. To handle this challenge, we de- velop a service mining scheme based on semantic for IOT to provide users with interesting composite services. In this scheme~ services can be combined and recommended to users actively according to the calculation of service sim- ilarity and an updatable semantic database. By the results of service similarity, useless compositions can be filtered out so that energy consumption on service flooding will be reduced. The update strategy of semantic database is also given out, by which the composite services can keep up with time and be more applicative. The benefits of the proposed method are that all operations such as can culating, filtering, and updating are simple enough to be performed in sensor networks.     

A User Range Accuracy Estimation Method of the Global Navigation Satellite System

An Analytic estimation method （AEM） which has less execution time is proposed to calculate the User range accuracy （URA） used by the user to detect the integrity potential risk. It shows that the most important thing of computing URA is to find the maximum error vector in the error vector space. By using the covariance matrix of the error vector, the searching of the maximum error vector is converted to an analytic geometry problem. The value of the maximum error vector can be obtained di- rectly by mathematical derivation. Experiments are made to compare the performance between the proposed AEM algorithm and the Exhaustive grid search （EGS） method. The related results show that the AEM algorithm can re- duce more than 92% of execution time. This algorithm is more suitable for computing URA at the Master control station （MCS） for the engineering applications.     

Power Allocation for Video Segment Based Caching Strategy in F-RAN Architecture

The concept of edge network caching has been proposed to alleviate the excessive pressure on the core networks.Furthermore,video segment caching technology,a method to cut the whole video into segments and cache them separately,has brought a novel idea to solve the caching problem in the smaller space for massive data.The adoption of segment caching in edge networks will divide the simple video transmission process into two coupling stages because of separate data caching,which leads to more complicated resource allocation.In this paper,this problem is discussed,and its mathematical model is established to minimize the energy consumption of video transmissions.By introducing an efficient prediction window of channel fading,an optimal dynamic scheduling algorithm based on Qlearning is proposed to minimize power consumption while ensuring smooth video streaming.The proposed Q-learning algorithm is simulated and the impacts of channel state,target video bit rate and largescale channel parameter are evaluated.Simulation results show that the proposed method can effectively reduce the total power consumption while ensuring the smooth playback of video service,thanks to the fact that the proposed method is intelligent which can effectively utilize idle resources in favorable channel states.     

An Effective Gated Scheduling with Pre-defined QoS Cross-layer Design for Wireless Networks

The proposed M-gated scheduling algorithm rules the server in wireless networks stays with a station for at most M times gated services. By the method of embedded Markov chain and probability generating function we developed a model with vacations and acquired the theoretical value of average queue length and packet delay. Like E-limlted scheduling algorithm, its performance is close to that of the optimal polling scheme, exhaustive scheduling, but the inherent unfairness problem for the latter is avoided. And it also does not require the server to know the status of stations, queues information. Compared with E-limited, M-gated has less jitter and is more robust. We then use the derived quality of service parameter as the basis for cross-layer design. Simulations results show the network is more stable and can keep the queue length and the packets delay under QoS level. When putting E- limited and M-gated scheduling in the same cross-layer design scheme, the latter has less overhead especially when the system is heavily loaded.     

Artificial Neural Network Based Trilogic SVM Control in Current Source Rectifier

Various modulation methods for the Current Source Rectifier （CSR） controlling scheme have been investigated in recent years. The traditional modulation methods have the disadvantages such as the great computing cost, sensitivity to load and system parameter variation. In this study, an Artificial neural network （ANN） based algorithm is adopted to tackle the problem. This algorithm features parallel computation and self-tunlng. The Random weight change （RWC） algorithm is employed for on-line parameter tuning to achieve better performance. The principle of the trilogic Space vector modulation （SVM） for CSR is introduced as the theoretical foun- dation. The proposed method is introduced in two parts, the constructing of the neural network and the designing of an on-line parameter tuning algorithm. The simulation results based on SABER software show that the new algorithm has a good performance, especially under a nonrated system load.     

Heterogeneous Network Selection Optimization Algorithm Based on a Markov Decision Model

A network selection optimization algorithm based on the Markov decision process(MDP)is proposed so that mobile terminals can always connect to the best wireless network in a heterogeneous network environment.Considering the different types of service requirements,the MDP model and its reward function are constructed based on the quality of service(QoS)attribute parameters of the mobile users,and the network attribute weights are calculated by using the analytic hierarchy process(AHP).The network handoff decision condition is designed according to the different types of user services and the time-varying characteristics of the network,and the MDP model is solved by using the genetic algorithm and simulated annealing(GA-SA),thus,users can seamlessly switch to the network with the best long-term expected reward value.Simulation results show that the proposed algorithm has good convergence performance,and can guarantee that users with different service types will obtain satisfactory expected total reward values and have low numbers of network handoffs.     

A Unified Framework of the Cloud Computing Service Model

After a comprehensive literature review and analysis, a unified cloud computing framework is proposed, which comprises MapReduce, a vertual machine, Hadoop distributed file system (HDFS), Hbase, Hadoop, and virtualization. This study also compares Microsoft, Trend Micro, and the proposed unified cloud computing architecture to show that the proposed unified framework of the cloud computing service model is comprehensive and appropriate for the current complexities of businesses. The findings of this study can contribute to the knowledge for academics and practitioners to understand, assess, and analyze a cloud computing service application.