Earticle

As a new service mode in cloud computing, SaaS (software as a service) brings many attractive advantages. Legacy systems can be revived through being reengineered to SaaS. In order to achieve reengineering, the analysis and understanding to legacy systems are essential. For this goal, an improved Bunch clustering system is proposed to implement automatic modularization to object-oriented software systems so as to help engineer understand legacy system, including introduction of modular dependency graph with relationship type information, adaptation to initial partition and adjustment to modularization quality. The experiment results show that the improvement of Bunch clustering system is effective. The improved Bunch clustering system can make the clustering results more stable and consistent to the benchmarks.

Remote Desktop Protocol (RDP) provides remote login and desktop control capabilities that enable a client to completely control and access a remote server. The protocol is implemented by Microsoft Corporation based on ITU-T T.120 family protocols. The major advantage distinguishing the RDP from other remote desktop schemes, such as the frame-buffer approach, is that the protocol is based on preferably sending graphic device interface (GDI) information from a server, instead of full bitmap images. This paper proposes a remote collaboration system based, designed and implemented on RDP architecture. The proposed system consisting of a cross control scheme with bus re-encryption architecture provides the full capabilities of all remote collaborations, such as remote control, application access and sharing among all RDP clients. In addition, it provides the RDP packets recording and replaying scheme to for replaying all of the detailed tracks through which a multiparty conference traveled. The performance modeling and evaluation show the practicality of the system and that it offers a scalable model for modern cloud services.

Access control, what is a core technology in information security, has been widely used cloud web. It is used to distribute objects’ resources to subjects according to some principles. But until now, most traditional access control models are focused on the distributional principles; the security of systems is always ignored. Protecting data and resources in computers from threats and attacks is also an important aspect in access control, especially in cloud web where there are a great deal of threats and attacks. In this paper, we introduce the present situation and related work of access control. Then we analyze the security problems in computer systems with traditional access control model, and get the prisoner’s dilemma in it through Nash equilibrium. To solve this dilemma, we applied repeated-game theory to access control and build a new model RTAC. The ultimate goal of RTAC is to encourage subjects’ honest accesses and discourage subjects’ malicious accesses through Nash equilibrium. At last, we perform some experiments and show the advantages of RTAC over the traditional access control model.

Scientific workflow scheduling problem is a combinatorial optimization problem. In the real application, the scientific workflow generally has thousands of task nodes. Scheduling large-scale workflow has huge computational overhead. In this paper, a parallel algorithm for scientific workflow scheduling is proposed so that the computing speed can be improved greatly. Our method used ant colony optimization approaches on the GPU. Thousands of GPU threads can parallel construct solutions. The parallel ant colony algorithm for workflow scheduling was implemented with CUDA C language. Scheduling problem instances with different scales were tested both in our parallel algorithm and CPU sequential algorithm. The experimental results on NVIDIA Tesla M2070 GPU show that our implementation for 1000 task nodes runs in 5 seconds, while a conventional sequential algorithm implementation runs in 104 seconds on Intel Xeon X5650 CPU. Thus, our GPU-based parallel algorithm implementation attains a speed-up factor of 20.7.

In recent years, using mobile devices, in daily life, has found a special place and because of the applicability of these devices are increasing the number of users day by day. Business companies have integrated them with cloud computing technology and have provided mobile cloud in order to improve using mobile devices and overcome the energy consumption of mobile devices. Therefore, energy efficiency is a fundamental factor for mobile devices and cloud computing has the potential to save energy and power of mobile devices. In mobile cloud computing, computations and storages of mobile devices applications are transferred to cloud data centers and mobile devices are used merely as user interface to access services. The cloud computing will help to reduce energy consumption of mobile devices. In this paper, a new approach is given to reduce energy consumption of based on Learning Automata in mobile cloud computing. Simulation results show that our proposed approach significantly saves energy consumption through determining the appropriate location for application.

This articles tries to explain the fact that how multimedia database deal with various multimedia information in modern times. By analyzing the functional mechanisms and researching its structure, and following two examples, we know that the teaching resources-based multimedia database has made great progress from the conventional method. And what is more, the retrieval system is also taken into consideration, which includes mainly three pathways.

The paper proposed a network scheduling in cloud computing based on intelligence Particle Swarm Optimization algorithm aimed at the disadvantages of cloud computing network scheduling. Firstly, on the basis of cloud model, used intelligence Particle Swarm Optimization algorithm with strong ability of global searching to find the better solution of cloud computing network scheduling then turned the better solution into the initial pheromone of improved Particle Swarm Optimization algorithm, and found out the cloud computing network scheduling and the algorithm’s global optimal solution through improved Particle Swarm Optimization information communications and feedbacks. Finally, made comparison test of the three benchmark function on the basis of MATLAB, the results showed, compared with traditional intelligence Particle Swarm Optimization algorithms, the improved algorithm can preferably allocate the resources in cloud computing model, the effect of prediction model time is more close to actual time, can efficiently limit the possibility of falling into local convergence, the optimal solution’s time of objective function value is shorten which meet the user’s needs more.

Cloud Computing is a distributing computing technology which is used as pay per use basis. Now a day’s Cloud Computing is the most reputed topic due to its ability to offer guaranteed quality of service atmosphere, dynamic IT infrastructures, and conFigureurable software services. But many users could not satisfy on cloud services completely due to their uncovering security purpose for handling large numbers of data. Even the network becomes uncontrollable, when large numbers of user’s request to the server create network congestion and data losses vigorously. Content Delivery Network OR CDN is an eminent solution of this problem. Our objective is to create local and global server and connect the global server to system manager, which is worked over Content Delivery Network to deliver and direct the user request to the nearest global edge server except local server and establish the connection between them and transfer the respective content. For optimization of edge selection process and reduce the load over content delivery network we approach local server concept in this paper.

Cotton is an important strategic material for national well-being and the people’s livelihood. The warehousing environmental conditions will directly affect the storage quality of cotton or economic value, and even have some influence on the whole national economy. In order to improve the relatively out-dated management of Chinese cotton warehousing industry, this paper firstly analyzed the existing problems and the needs of future development in the management of China cotton warehousing industry, and then constructed the IOT architecture suitable for cotton storage based on the advantage of IOT technology, and finally designed detailed IOT application solution of cotton warehousing with specific examples from such technology application layers as sensing layer, network layer, and application layer, which aims to make an useful attempt to enhance the management level of China cotton warehousing.

Predicting the load in a building is essential for the optimal control of heating, ventilating and air-conditioning (HVAC) systems that use Ice Thermal Energy Storage (ITES) technology and also for cost and energy reduction of the non-storage systems. To solve the problems of the low accuracy of prediction by a single method, and most load predictions focusing on short-time prediction that cause reducing the practical significance, the application of the combined prediction method of time series and neural networks is presented in this paper. A case study shows that high accuracy is achieved by using the combined prediction model based on these two methods compared with the time series method in predicting the building load for longer time.

With the growing demand for computing resources and network capacity, providing scalable and reliable computing service on the Internet becomes a challenging issue. Recently more attention has been paid to the Utility Computing concept aiming to provide computing as a utility service similar to water and electricity. While the concept is very challenging in general we focus our attention in this chapter to a restrictive environment web applications, given the omnipresent use of web applications on the Internet, this environment is rich and important enough to warrant careful research. This paper describes the approaches and challenges in building such a computing platform.

Particle swarm optimization (PSO) is a population-based stochastic optimization that has been widely applied to a variety of problems. However, it is easily trapped into the local optima and appears premature convergence during the search process. To address these problems, we propose a new particle swarm optimization by introducing chaotic maps (tent map and logistic map) and Gaussian mutation into the PSO algorithm. On the one hand, the chaotic map is employed to initialize uniform distributed particles so as to improve the quality of the initial population, which is a simple yet very efficient method to improve the quality of initial population. On the other hand, the Gaussian mutation mechanism based on the maximal focus distance is adopted to help the algorithm escape from the local optima and make the particles proceed with searching in other regions of the solution space until the global optimal or the closer-to-optimal solutions can be found. Experimental results on two benchmark functions demonstrate the effectiveness and efficiency of the PSO algorithm proposed in this paper.

Cloud computing has been emerged as a new service model in computing world and giving lot of interest to the researchers to find its benefits. Task scheduling is a major conflict in cloud environment and genetic algorithms are one of the optimization techniques to solve that problem. Virtual machine’s processing elements are important criteria to solve a scheduling problem. In proposed algorithm called upgraded Genetic algorithm, initial population is sorted according to the number of processing elements of each virtual machines. Proposed algorithm is compared with MGA in terms of cost and with MACO in terms of make span. Experiment results shows upgrade genetic algorithm gives better efficiency in term of cost and make span.

With the rapid development of the 3G network, traditional calculation methods are unable to adapt to the data scene that telecom users' Network access behavior's data scale increase rapidly dozens of TB. The cloud techniques such as Hadoop platform are introduced to solve the data storage problem. The appropriate data mining algorithms are designed from the perspective of practical application. This paper improves the traditional decision tree SPRINT algorithms, proposes a parallel computing program and successfully applies to the Hadoop platform.

Cloud computing is an internet based technology. This computing paradigm has increased the utility of network where the potentiality of one node can be used by Other node, cloud provides services on demand to distributive resources such as Database, servers, software, infrastructure etc. in pay per use basis, load balancing is One of the unique and important issues for distributing a larger processing lode to smaller processing nodes for increasing total performance of system, in load balancing method the workload not only distribute across multiple computers but also other resources over the network links to gain optimum resource utilization, minimum average response time and avoid overload condition. Different load balancing algorithms have been launched in order to manage the resources of service provider efficiently and effectively. The objective of this paper is to propose efficient scheduling algorithm that can maintain the load balancing and provide improved strategies through efficient job scheduling that would decrease the average response time and increase the availability of more VMs to allocate new jobs from requesting nodes.

Cloud storage offers clients great convenience to relieve them from heavy burden of storage and management, so an increasing number of clients choose to outsource their data to remote cloud providers. However, for clients, this entails a sacrifice of actual control of these files. Remote servers may suffer from disk failure for uncertain reasons or even delete rarely accessed data to sell these storages to other clients. Therefore, there’s a great necessity for clients to make sure that their data are well stored in remote servers. Numbers of remote integrity checking (RIC) schemes are proposed to solve issues above, including following up work Provable Data Possession (PDP) and Proofs of Retrievability (PoR), which can be applied in cloud auditing. This paper presents state-of-the-art RIC schemes and makes a classification from the perspective of whether they support dynamic verifications, i.e., whether they can still be used to make verifications after clients modify, insert or delete files. In static verification schemes, we delve into the mechanisms and techniques used for integrity checking. For dynamic ones, we discuss the authentication structures that support dynamic operations. We also present several remarks to guide readers to a wide vision of data checking as conclusions and future work.

It is facing resource shortage and environmental pollution around the globe, until the emergence of new renewable energy technology has reinvigorated traditional techniques. Development of renewable energy generation in China needs the joint efforts of all social sectors from policy to technique. Distributed generation combining with battery applied in micro-grid relieves a problem of resource shortage, and ensures load operation when power grid fault urgently. In this paper, photovoltaic is described briefly first. Then photovoltaic-battery-diesel micro-grid is established according to the situation about geographical environment, what is more workflow of the system is analyzed. Control strategy is shown according to condition of micro-grid. At last, micro-grid containing distributed generation established in Liaoning Province is quantitative illustrated in economic benefit. Distributed generation is added into micro-grid enhance the reliability of micro-grid even power grid. That makes a contribution to resource and environmental problem.

Many of industrial plant require high performance and linear operation; higher density position and/or incremental PID can be used to integrate large amounts of control methodology in a single methodology. This work, proposes a developed method to design PID controller (PID) with optimal-tunable gains method using PC-based method. Many industrial processes can be represented by a first order model. The time delay occurs when a sensor or an actuator are used with a physical separation. The method used to design a PID is to design it as Proportional – derivative controller (PDC) and proportional – integral controller (PIC) connected in parallel through a summer. PIC is designed by accumulating the output of PDC. This method contributes to avoid writing a huge number of fuzzy rules and to reduce the memory considerations in digital design.

This paper proposes an information-entropy-based decision-making method for selecting cloud computing service. Firstly, an evaluation index system (EIS-DMSCS) is developed. Secondly, a decision-making method is constructed. Finally, the application example proves the scientificity, effectiveness and feasibility of this method.

In order to solve the problem of uncertain information in enterprises supply chain collaboration, a model of grey system theory is built to predict the coordination order quantity distributor of enterprise; the model has smaller prediction error and model accuracy. The data combined with empirical materials are calculated and simulated, and the simulation results are further improved analysis. The combination of qualitative and quantitative has a strong scientific application, which provides an important reference for distributors to determine the order and manufacturers to determine the optimal production in the supply chain, and the construction model and ideas of simulation analysis also have unique and innovative.

In this paper, we present A Novel Technique for Finding Overlapping Communities in Internet of Things Two concepts named the maximal sub-graph and the clustering coefficient between two neighboring communities are used. In this paper, we have focused on enhancing the Internet of things network further by using the complex networks, complex networks will divide internet of things of users among communities to enhance the services further.

Predicting quality of services (QoS) is an important need when ranking cloud services for selection. QoS values of cloud services usually depend heavily on the user’s and service’s environments. Therefore, personalized QoS value prediction for cloud services is more desirable to users. Collaborative Filtering (CF) has recently been applied to personalized QoS prediction for services on the Web. However, seldom did they take the context information of service users and services into consideration. The following paper presents a CF-based method for predicting QoS values of cloud services. The method exploits not only the QoS information of users and services, but also one type of the most important context information of users and services, i.e., their geographic locations. Experiments conducted on a real dataset show that geographic location information is indeed helpful for improving the QoS prediction performance. The experimental results also demonstrate that the proposed method is significantly better than previous methods in prediction accuracy.

Cloud Computing has been an emergent technology that has opened the space for virtualization, as it provides many computational services and storage services over the Internet with the help of a browser. Cloud computing’s core comprises of services like platform, infrastructure and software as a service. The unpredicted boom in cloud computing is driven by its simple economic benefit. It helps in reducing capital expenses and minimizes operating expenses. This move however, has increased a major concern about the protection of data, as against the traditional system the data is now stored online and is far easily exposed than we realize. This raises a major security issue for data protection. Many techniques for protection of data have been proposed so far. However, the best available option till date is to encrypt user data before storing it over the cloud environment and decrypting it again before handing the data back to the cloud user. In this paper, we introduce a more efficient and stronger encryption process that allows a cloud service provider to protect user data more efficiently.

The solar radiation conditions and environmental factors on the shadow of the power generation resulted in loss of efficiency. In this paper, a simple structure with interleaved isolation converter according to the characteristics of the hardware circuit is proposed in order to improve the conversion efficiency of photovoltaic grid system. First, the hot spot effect and discretization of solar panels according to the characteristics of the micro-inverter with a lot of promotion is analyzed. The least action recursive method is involved, which has high computing precision, and therefore is more suitable for distributed micro-inverter system. The PC56F8245 chip is chosen to overcome the loss of photovoltaic energy and improve the conversion efficiency. The stability and high accuracy has been proved through the related experiments.

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.

At present, the distribution of China’s medical resources is uneven. Every day big hospitals are overwhelmed. The problem that it’s difficult and expensive for ordinary people to see doctors is becoming a major social host issue. And major chronic diseases are threatening human health. In order to optimize medical resource allocation and provide more efficient services for patients with major chronic diseases, this paper takes stroke as the entry point and proposes a new medical service pattern. Namely, taking stroke patients as the center, medical association collaborates to prevent and treat them. Because cloud computing is dynamic and scalable and in order to expand to other major diseases and attract more medical associations to join when the medical service pattern is mature, this paper uses cloud computing to support the pattern. And this paper details the new medical service pattern, the framework and the service of cloud platform of medical association collaborating to prevent and treat stroke. The pattern can consider the prevention, treatment, rehabilitation, and other aspects of stroke. And it can achieve the circular medical service pattern which centered on stroke patients and can provide better service for stroke patients.