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Existing works mainly focus on indexes in a single server or server-client structure. Unfortunately, such works fail to provide efficiency since performance bottleneck is introduced. This paper designs a two-layered index structure to prune search space among computing nodes for query processing. The index reduces the number of involved computing nodes while query processing, and improves I/O efficiency inside a single server. The initiation method and maintenance methods are proposed for the index, together with optimization strategies for improving query throuphput. This thesis designs the point query algorithm, the range query algorithm and the kNN query algorithm for cloud systems, including distributed algorithms among computing nodes, and optimization strategies inside a single computing node.

Nowadays cloud computing has turned into a key innovation and has become a great solution for indulging a flexible utility oriented , online allocation and storage of computing resources and client’s information in lower expense, on- interest and dynamically scalable framework on pay per use premise. This technology is a new pattern emerging in IT environment with immense necessities of framework and resources. Job Scheduling Problem is an essential issue. For efficient usage and managing resources, administrations, scheduling plays a critical role. This paper apportion the performance enhancement of Hyper- Heuristic Scheduling Approach to schedule cloudlets and resources, by taking account of both , computation time and transmission cost with two detection operators. Load Balancing and RASA concept is applied for efficient Load Scheduling, resource utilization and thereby enhancing the overall performance of cloud computing environment. The numerical investigations of HHSA were performed on CloudSim. Experimental results generated via simulation shows that enhanced heuristic scheduling approach is much better than individual heuristic approach in terms of minimizing makespan time.

In order to improve the performance of data transmission in SDN, this paper proposes a load balance solution scheme by taking advantage of the global network view of SDN. We collect 4 load features from each transmission path. These features are bandwidth utilization ratio, packet loss rate, transmission latency and transmission hops. By using this 4 load features, BP Artificial Neural Network model is trained to predict the integrated load for different path and to choose one with least load as the data-flow transmission path. The contrast experiment results show that load balancing strategy proposed in this paper can select more rational transmission path for data-flow and achieve 19.3% network latency decrease at most.

In cellular networks, as a result of the heterogeneous network layout, a number of community, in the entire network, micro base station district energy management issues that need to be unified management and scheduling, and combined with the flow of communication and transport business and the efficiency of energy management, the development of energy management system based on cloud computing platform, according to the system, can have better energy efficiency, realize the communication and the advantage of energy balance. This article mainly from the two aspects of theory and actual implementation of this management system research.

Cloud computing has a lot of security issues that are gaining great attention nowadays, including the data protection, network security, virtualization security, application integrity, and identity management. Data protection is one of the most important security issues, because organizations won’t transfer its data to remote machines if there is no guaranteed data protection from the cloud service providers. Many techniques are suggested for data protection in cloud computing, but there are still a lot of challenges in this subject. The most popular security techniques include SSL (Secure Socket Layer) Encryption, Intrusion Detection System; Multi Tenancy based Access Control, etc. Goal of this paper is to analyze and evaluate the most important security techniques for data protection in cloud computing. Furthermore, security techniques for data protection will be recommended in order to have improved security in cloud computing.

On the basis of network establishment, system architecture of warship’s information integral platform and XML distributed data exchange were researched. To focus on information system data and network resource on warship, the multilevel software architecture was adopted in simulation. On the data layer, circulated simulation data about manoeuvring, ship system, power and navigation were got. On the service layer, the software interface of all the simulated systems was supplied, which realized data collection and information storage. The last layer was for user by which human-computer interface was got through client software. To improve stability of ship’s local area network, double-ring topology was used as the main network, and very popular software named Wonderware in field of industry control was developed. With IAS platform, deployment of all the sub-stations was completed, and links between each collection and database were established. Multilevel computer network and information data integration frame on warship had many purpose, such as serving for oriented application system, data communication, information share and so on.

This paper presents a modified cuckoo search algorithm (MCSA) for solving short-term cascaded hydrothermal scheduling (ST-CHTS) problem. The short-term cascaded hydrothermal scheduling is to determine the optimal operation for thermal plants and a cascaded reservoir system while satisfying all constraints including electrical constraints of both hydro and thermal plants and hydraulic constraints of reservoirs. The MCSA has been developed by modifying the search strategy via Lévy flights to improve the performance of the conventional cuckoo search algorithm. The proposed method has been widely and successfully applied to many optimization problems in engineering fields; however, this is first time employed to search for the optimal solution of the ST-CHTS problem. The proposed method has been tested on two systems where thermal plants with nonconvex fuel cost function and a cascaded reservoir system are taken into account. The result comparison from the MCSA compared to other methods reported in the literature has revealed that the proposed MCSA is very efficient for solving the ST-CHTS problem.

Concept lattice essentially describes the relationship between objects and attributes. The reduction of multi-value attribute concept lattice is a hot topic in the fields of information retrieval, knowledge discovery and data mining etc., while the granular computing emphasizes observing and analyzing the same problem from different granular worlds. It makes the complex problems around us be mapped to an easy to handle and more simple theory of calculation. The paper gives definitions of the concept granule and the compatible concept granular set by application of information granular and the granular of layered theory, and provides an algorithm to compute concept granular set through calculation of the compatible relationship. The paper further constructs the concept granule lattice, and then deletes the attribute of smaller contribution to concept granule. Through the comparison of the concept granule lattices, the multi-value attribute reduction could be achieved and the core attribute set in the formal context could be obtained. Instances could demonstrate the high efficiency and accuracy of this algorithm that is easier to realize through programming. Through the resolution of attribute, the calculation complexity could be reduced and the efficiency of calculation could be improved.

The rapid growth in the popularity of social networking and microblogging has led to a new way of finding and broadcasting information in the recent years. The real-time microblog filtering emerges as the times require. The task of real-time microblog filtering is to decide if subsequently posted tweets are relevant to a given query which represents the special information needs. One-side feedback is one of the most difficult problems in microblog filtering. This paper focuses on exploiting the time profile of relevant microblogs to address this problem. A temporal microblog filtering based on retrieval model is proposed. Specifically, similarity threshold achieved by the language model is adjusted according to temporal burst. Evaluated on the TREC 2012 microblog real-time filtering track dataset, the experimental results show that the proposed model is significantly better than several baselines.

Cloud Computing plays an important role in academic and industry. Business process are composed and executed in the distributed loosely coupled environment. Business processes are implemented as the composite service which involves many services and these services connected by different workflow patterns. Numerous functionally similar services are available in the cloud. As it is an on-demand service, cloud service consumer needs some Quality of Service factors in services. Trust plays an import role in service selection. Selecting the trust worthy service for service composition with the cost and other constraints is a tedious task. In this paper, service trust estimation method based on Beta distribution is proposed. Trust based Cloud service selection framework for service composition is proposed and it is proved that the proposed framework provides better performance as compared to the well-known service composition methods based on the execution time and optimality value.

The system of distribution network with distributed generation is analyzed, and dynamic optimization based on an improved genetic algorithm is presented in this paper. First, the mathematical model of distribution network can be established by using constraints and objective function, which contains the network loss, the DGs investment and reliability of power supply. Then, according the construction of distribution network, this paper presents a design of improved genetic algorithm. Finally, IEEE 14 nodes system is adopted to realize the design of the algorithm.

Presently P2P-controlled bots has become an increasing threat to our network security due to the fact that P2P bots do not have a centralized point to shutdown or trace back, thus making the detection of P2P bots is very difficult. To enhance the detection rate, a new model to detect P2P bots on an individual host is proposed by improving the dendritic cells algorithm (IDCA). In the proposed approach, the raw data for P2P bot detection is obtained via APITrace tool. The processes ID are mapped into the antigens, and the behavioral data created by the processes are mapped into the signals, which are the time series input data of IDCA, are used to implement data fusion and correlation. The test experimental results show that the proposed method is effective to detect P2P-controlled bots on the host with low false positives.

The current and future computer industry is changing from single core CPU to Multicore Processors. The next generation computing systems are focusing on parallel computing to solve the problem in fast and high speed using parallel programming concepts. By running more than one task at the same time with multiple processors concurrently or parallel we can achieve high speed in our computing applications. Here the performance improvement is measured in terms of increase in the number of cores per machine and is analyzed for better optimal work load balance. The parallel computers follow different workload scheduler. In this paper we investigate how to tune the performance of threaded applications with balanced load for each core or processor. The focus here is the process of comparative analysis of single core and multicore systems to run an application program for faster execution time and optimize the scheduler for better performance.

With the rapid development of Weibo, which is the most popular microblog in china, more and more attention was paid to relative studies about it. With the objective of gathering precise information data from Weibo, which is the groundwork of these researches, a novel high efficient Weibo crawler (WCrawler) based on loginning simulation is designed. The priority evaluation is described to ensure the correlation between entires. MD5 is introduced to check for duplicates of URL crawled. Experiments demonstrate that the novel crawler has an efficiency and integrity of information collecting compared with API crawler. In addition, we present a summary of the data that collected from Weibo social network by WCrawler.

Cognitive radio networks (CRN) is IEEE 802.22 standards, also known as 5-G wireless technology. CRN carries primary users (PU) or licensed users and secondary users (CR) or un-licensed users. In this paper, we have presented an overview of CRN, further we discuss CRN functions. There are various sensing techniques which we classify and discuss, and further analyze the issues related to CRN. Finally, we conclude that each sensing technique has its own advantages and dis-advantages.

Information technology has entered a big-data era. Large-scale data is becoming the basic factors of production and it would affect the corporate social responsibility disclosure deeply, the use of big data has become the key factor to improve the competitiveness of enterprises. Through this empirical analysis, we can draw the following conclusions: corporate social responsibility is mainly composed of economic responsibility, legal responsibility, ethical responsibility and spontaneous responsibility. And CSR will have positive effect on organizational performance. All of these indicate that prompting enterprises staff to fulfill corporate social responsibility is helpful to improve the organizational performance.

Trans-regional operation with Chinese characteristics, which is a unique farm machinery social service, appeared in our country. How to select scheduling paths and farmland for obtaining the maximum profits is difficult to make decision. The scheduling and allocating income model with the goal of high-income and low-cost is established in the paper. Then the concept of parallel tasks is defined, judgment of parallel tasks, generation of scheduling path graph with heuristic information and establishment of combined scheduling and allocating rules are studied in the paper. Multi-machinery simultaneous scheduling and allocating algorithm and scheduling and allocating in demand algorithm are proposed according to the characteristic of multi-machinery and multi-point operation. Scheduling and allocating strategies of parallel tasks are improved and search-based scheduling and allocating algorithm are put forward. The feasibility and validity of search-based scheduling and allocating algorithm are further verified at the aspects of parallel task, amount of farmland and farm machinery demand.

It is a challenging task to provide an easy–to-use programming interface for distributed shared memory in parallel computing that demands high availability as well as effective synchronization. The current implementations typically use conventional Ethernet as communication medium using broadcast or multicast for synchronization mechanisms. In general, multicast is more efficient than broadcast, as it uses multicast tree to transfer data instead of multicast flooding. Multicast avoids redundant transmissions and lessens network contention. These properties make it significant for distributed shared memory. However, such solution has limited control on response time and process scheduling. This causes uncertainty of time spent in synchronization and interruption of unrelated processes in the same multicast group. In this research, we propose a dynamic SDN multicast mechanism to improve efficiency of synchronization for distributed shared memory by using an SDN-based multicast mechanism and a dynamic core based tree algorithm. The forwarding rule reduces the number of consistency-related messages without interfering the unrelated processes in the same multicast group. The dynamic core-based tree algorithm builds a multicast tree for every multicast group, hereby, SDN controller dynamically adjusts multicast tree according to joining or leaving of nodes. The experiments on the prototype system demonstrate that it could provide better performance compared to the conventional Ethernet based solution in terms of response time and process scheduling in the homogeneous testing environment.

To solve the problem of cloud resource discovery in the interconnected environment for multiple providers, a resource selection method for the multi-layer ontology is propose from the view of software developer requirements. The ontology is adopted to describe the user requirements, the resource constraint requirements and the cloud resource attribute firstly. Then, the bidimensional matching algorithm is constructed based on two principles of concept matching and attribute matching. And the resource discovery and resource selection are realized by the algorithm, combine with the ontology mapping. The method not only implements the resource selection among cloud providers, but also eliminates the implementation details in the process of resource selection. It overcomes the technical limitations of the single cloud provider. The simulation results show that the importance of the resource requirements is adopted by bidimensional matching algorithm to carry out element level matching which improves the recall and precision of resources greatly.

This paper concerns reputation and trust computation in vehicular ad hoc networks (VANETs). Various survey and implemented work has been studied and analyzed to form a new survey on trust model to VANET. In this survey it is observed that there are lot of new techniques are possible to form a new trust model in VANET to provide better security with trust concern over the entire environment of trust management in VANET. Trust management in VANET (TMV) is one well know concept and basic need to maintain a reliable secure faith full VANET on which node may believe through their close eye and could drive for safe journey with ultimate faith.

Due to the fact that people rely on social networking tools more and more in their work, it is necessary to integrate them with industrial Business Process Management (BPM) systems. Different from the widely exploited methodology by BPM system venders that furnishes social network capability to BPM suites, this paper decouples the two parts and binds them with a middle component. Such loosely coupled approach has the advantage of multiple crossing social networks and existing BPM tools can all be adhered together, which means great flexibility and working convenience. The method also enables people to seamlessly incorporate their work with frequently used public social networking tools (e.g. Facebook, twitter, Instagram, Weibo, etc.). At the end, the paper presents a prototype implementation of this method.

A systematic approach to determine the extreme value of total electrical length for tri-section stepped impedance resonator (TSSIR) is proposed in this paper. It utilizes the Lagrange multipliers method to search the extremum condition. Result demonstrates that the maximum resonator length of TSSIR is limited to triple that of the corresponding uniform impedance resonator (UIR). Spurious resonant frequency of λ/2 TSSIR is mainly analysized under the extremum condition (θ1≠θ2≠θ3). Through comparison between the proposed approach and previously assumed condition (θ1=θ2=θ3), the staircase discontinuity of transmission line can be reduced using the paper theory to achieve same harmonic ratio. Last, a simulated example is presented to verify correctness of the method. This paper aims to provide a theoretical basis and general guideline for the filter design using TSSIR.

Based on particle swarm optimization (PSO) algorithm and its power system reactive power optimization method to in-depth study and research proposed a new hybrid particle swarm optimization algorithm (HPSO). Algorithm combines the differential evolution algorithm and simulated annealing algorithm and particle swarm optimization algorithm, in particle searching optimal except for tracking individual and global, and tracks produced by particle information difference of the three value. At the same time, when the particle search space of one dimension speed lower than the setting value will be re initialized the dimensional particle velocity and the particle of differential evolution mutation. For the crossover and mutation operations, new solution may be worse than the original solution to, the introduction of simulated annealing algorithm, the metropolis rule in a certain extent accept bad solutions, allows the target function in a certain degree of deterioration, practical calculation is not according to the probability to choose the poor solution, but rather the judgment target function difference is less than allows the target function deterioration range. Hybrid particle swarm optimization algorithm combines the advantages of particle swarm optimization algorithm, differential evolution algorithm and simulated annealing algorithm, to maintain the diversity of particles, has very strong practicability.

Cloud computing has revolutionized the information and communication technology industry by enabling on-demand provisioning of elastic computing resources on a pay-as-you-go basis. However, due to the large-scale popularization and application, cloud data centers consume enormous amounts of electrical energy, which results in high operating costs and carbon dioxide emissions. In this paper, twenty-two algorithms are simulated for testing the trade-off between the applications performance and energy consumption during virtual machine management of cloud data centers in CloudSim simulation toolkit. The goal of these algorithms is to reduce energy consumption under ensuring the performace of cloud applications. A large number of simulation experimental results prove the performance of these twenty-two algorithms respectively.