Earticle

How to better conduct research resource scheduling has long been a research direction of cloud computing. This paper, aiming at slow convergence and easiness of falling local optimum of ant colony algorithm,has integrated genetic algorithm into the ant colony algorithm and obtained hybrid algorithm (ACA -GA); in the initial solution of the ant colony algorithm, it has adopted selection, crossover and mutation operations of genetic algorithm to obtain an effective initial solution; secondly, it has used the perception threshold of ant colony algorithm path setting to regulate individual selection optimal path; finally, it has improved volatile factor so as to significantly improve the updating efficiency of pheromone. The algorithm in the paper proved that the performance of the algorithm has been also significantly improved through classical test functions. Cloudsim platform shows that, the algorithm above mentioned reduces the time and cost spent in resource scheduling of, hence has some promotional value.

In this paper, a modified sliding mode control is proposed to stabilize a class of large-scale systems with mismatched uncertainties, unknown exogenous disturbances and without the measurements of the states. Based on the Lyapunov method, appropriate linear matrix inequality stability conditions are derived to guarantee the stability of the system. Then, a modified adaptive sliding mode control law is proposed to guarantee the finite time reachability of the system states by using output feedback only. Final, a double-inverted pendulums system connected by a spring is simulated to demonstrate the efficacy of the proposed method.

Mobile Internet due to the limitation of the mobile terminal power supply, transmission and calculation of the need to adopt energy saving strategy, also due to the terminal mobility, mobile Internet topology change. Therefore, mobile Internet cloud computing resources allocation need both energy efficiency and assure the time characteristics of mobile business; Aiming at this problem, this paper presents a maximum energy efficiency optimization, in the restrictive conditions at the same time, guarantee the minimum time delay the business. According to the characteristics of the optimization problems, both the distribution of the improved algorithm is proposed, the algorithm based on particle swarm optimization (pso) algorithm, build the search direction matrix of orientation, the simulation results show that the proposed allocation algorithm can effectively improve the efficiency of energy utilization, and ensure that the time delay of the business requirements.

This paper is showing the review study on cloud architecture that uses donation based resources in a network & helps multiple organizations to collaborate and yet compete with each other. The resources are utilized non intrusively. Organizations collaborate to create a Data-centre, that doesn’t harm their existence or profitability. At the same time, these organizations can compete by spreading to those locations where they carry certain edge over theirs. An ad-hoc cloud in heterogeneous environment helps to venture into remote areas. To achieve this some, ad-hoc cloud architecture is proposed along with issues and strategies. This is being elaborated in this paper with some effective sense.

Smartphone now a days are very common to everyone and it is widely accepted. According to eMarketer [4] more than 2.56 billion people will use Smartphone by 2018 and up to 2015 more than one fourth of the world population is using Smartphone. It is widely because of its multiple functionality which allow us to accomplish more than what we expect. Apart from basic feature it is used for e banking, web browsing etc. Almost all restricted or confidential data is stored in Smartphone which leads to sophisticated attack like Eurograbber which happens through mobile botnet installation. This attack even fails the most successful two factor authentication which is worldwide accepted by banks. The Botnet differentiate itself from common other mobile virus because its infected system is capable to create a link with C&C (command and control centre) controlled by bot master. In this paper we will discuss about Eurograbber attack , the problem scenario and the model to prevent this sophisticated attack.

Multi-threaded Java applications using synchronized or reentrant locks respectively exist performance trade-off. Parallel programmers usually use manual refactoring to test the performance of locks in a particular environment. However, manual refactoring is labor-intensive and error-prone. There is a strong need for automated support to find which one is more suitable to exert the performance of applications. This paper presents a refactoring framework to enable the transformation from synchronized locks to reentrant locks automatically. The framework analyzes global monitors based on Quad intermediate representation and visitor pattern in the Joeq compiler, validates the consistency of analysis results, and performs the transformation. The framework is evaluated on three benchmarks including red-black tree, producer-consumer problem, and SPECjbb2005. The total time is less than 4s for all three benchmarks on common desktop computer, and the successful refactoring results are observed.

Different with traditional approaches that offload computation to a single remote server, the performance of an application can be further enhanced by simultaneously distributing its computing tasks to multiple computing resources around the mobile terminal. How to effectively multiply partition application components is critical for parallel computation offloading. In this paper, an application multi-partitioning scheme is proposed, which optimally offloads application's components to surrounding resources. A graph mapping model is converted and set up to represent components and resources as undirected graphs, and the A* algorithm is employed to efficiently search the optimal mapping from component graph to resource graph, which minimizes computing costs and inter-resource communication costs. Simulation results demonstrate that the performance can be efficiently promoted by our algorithm, which outperforms the traditional approaches to a large degree.

In this paper a new cell is proposed for reflectarray antenna and is used to design the antenna to obtain to maximum gain and efficiency using phase synthesis in a frequency band of 11 GHz up to 11.7 GHz for different beam angles. The proposed cell is a double ring of hexagon which introduces multiple resonances which can provide more than 360 degrees phase variation by changing the loop size. Design method is based on phase-only algorithm where amplitude of the field on the reflectarray surface is forced by the feed. A 1.2 m reflectarray is designed for different beam directions. The results show maximum directivity of 42 dB and maximum efficiency of 73% for the required bandwidth. Focal length is 1.5 m which is set for maximum efficiency.

Geographical knowledge cloud service is a typical online service that provides big spatial data analysis with the function of knowledge discovery or decision-making. The composition of geographical knowledge cloud service imposes stricter requirements for better overall QoS and execution efficiency of the service chain. In this paper, we present a data volume aware ant colony optimization approach called DVA-MOACO algorithm for geographical knowledge cloud service composition. Our algorithm utilizes a multi-index service quality evaluation model, and improves the transition probability while considering the data transfer cost and other QoS constraints simultaneously when ant finding path. Our algorithm could reach the Pareto near optimal solution rapidly with better QoS performance and lower data transfer cost from numerous candidate solutions.

The formation of the collaborative learning group is a challenging problem, especially the principle construction and multi-objective optimization. In this paper, we propose an improved density clustering algorithm, and further implement the group formation based on the proposed algorithm. The proposed algorithm first constructs the distance matrix for learners, and clusters learners into different groups. After the random selection of the initial cluster centers, our algorithm achieves superior clustering result by iteratively adjusting the clustering centers. We conduct sufficient experiments and the experimental results demonstrate that the proposed algorithm well satisfies our aim, that is, inter-group heterogeneity maximization and intra-group homogeneity maximization.

For the problem that the process running is not security in the virtual environment, a multi-level process isolation management scheme is proposed. In this scheme, the processes and the basic runtime environment is defined as the process space. It takes a level hierarchical division strategy to classify the processes from the process space into two levels. One of which is the level of credible processes and another is the level data confidential processes. Under the strategy, the processes operate with different permission to ensure the security of sensitive information, prevent data leaking, operate legally and operate under authority. Finally simulation experiment results show that the method has a good protection to the process running in a virtual environment and it can meet the needs of data confidentiality. At the same time, the overhead of running processes is not obvious.

Transport with minimum time cost and distance remains to be an important research area in intelligent transport systems. Shortest path algorithms are primary methods to address simplified problems, which could not be well applied in high-dimensional real situations. We realized the minimum cost and maximum flow result via classical iterative algorithm based on graph theory, adjacency matrix is well applied to express the relationship between transport nodes, a topological sorting transport map is adopted to verify these approaches.

This Project deals with an fully parallel Embedded Z-source inverter(FPEZ) to control the three phase induction motor. Z-Source inverter is a single-stage converter which performs both buck-boost energy conversions using the LC impedance network. The further advancement in the Z-Source inverter is the partially parallel Embedded Z-Source(PPEZS) inverter that can produce same gain as Z-Source inverter but with lower capacitor voltage and ripples . In order to overcome the drawbacks of PPEZS, Fully parallel EZ-(FPEZS) Source inverter is proposed. The proposed inverters provide a high boost voltage inversion ability, a lower voltage stress across the active switching devices and capacitors with a continuous input current. In addition, they can suppress the startup inrush current, which otherwise might destroy the devices. The proposed system reduces six switch three phase inverter to four switch three phase inverter. This makes system compact which reduces cost and switching losses, line harmonics, improves power factor, reliability and extends the output voltage range. The system is modeled using Proteus and Simulink simulation. This paper presents the operating principles, analysis, and simulation results of FPEZ Source inverter.

With the rapid growth of internet finance, the credit assessing is becoming more and more important. An effective classification model will help financial institutions gain more profits and reduce the loss of bad debts. In this paper, we propose a new Support Vector Machine (SVM) based ensemble model (SVM-BRS) to address the issue of credit analysis. The model combines random subspace strategy and boosting strategy, which encourages diversity. SVM is considered as a state-of-art model to solve classification problem. Therefore, the proposed model has the potential to generate more accuracy classification. Accordingly, this study compares the ANN, LR, SVM, Bagging SVM, Boosting SVM techniques and experience shows that the new SVM based ensemble model can be used as an alternative method for credit assessing.

The applications based on cloud computing platform usually need to use a number of computing resources and storage resources to completing computing tasks, so the fault-tolerant capability of system has become increasingly important. Aiming to solve this problem, an evaluation model of task scheduling is proposed based on cloud system (TSCS). TSCS can effectively model and simulate complex cloud systems due to its strong capabilities of quantitative evaluation and behavioral description resulting from combining the theoretical characteristics of queuing task theory and Petri net. The algorithm solves the problem that meeting customer service satisfaction and load balancing at the same time. In addition, consider single backup task status, for the failure of more than one processor at the same time, present the minimum cost of backup scheduling algorithm, the algorithm to solve the problem that require a lot of backup cost. Experimental results show that TSCS is able to effectively reflect the architecture characteristics of various cloud system at the perspectives of performance, service, etc., and highly simulated various kinds of dynamic service behaviors of cloud system, single workload and multi workloads shows that the proposed policy can finish the user’s queuing task scheduling before deadline as well as obtain approving cost efficient.

Design pattern is a mechanism to show experience in object-oriented design, as well as an appropriate solution which has been provided by experts for particular problems and which can be used over and over throughout the design. Using design patterns helps improve software quality and reusability. There are different patterns for development of data source (database, file, array, etc.) systems but most of them have features that are not appropriate for code generation. We seek in this research new patterns for development of data source systems that can accelerate the development of such systems and reduce costs and are appropriate for code generation as well. This article deals with challenges related to prototype, singleton and MV* (MVC, MVP, MVVM) patterns. As a solution, we will propose two patterns called MVC+ and MVC++ as well as a tool called LCG for code generation. We compare the proposed patterns with similar patterns in terms of efficiency. MVC+ and MVC++ are appropriate for model-driven architecture, code generation and the development of windows form applications and web applications.

Technological advances of location aware devices allow us to locate and track the moving objects accurately and in more complex environments. In order to derive valuable knowledge embedded in data from databases, many domestic and foreign scholars have done a lot of work. In this paper, we review the progress of patterns mining mainly in frequent pattern, periodic pattern and following pattern. And we provide a general perspective for studies on different patterns mining by reviewing and comparing the methods and algorithms in detail, providing a quick understanding of research to the worker and giving effective following patterns mining scheme for the movements of objects. The study on patterns mining in movements not only has a direct bearing on human life but also play an important part in environment protection, traffic and transportation, privacy and security and so on. So, the rest of this paper will present these specific applications and analyzed it in theory.

In mobile ad-hoc network nodes communicate with each other or forward data by making route with the help of other nodes. Infrastructure of the network does not fix topology change due to movability of nodes that’s why attacking condition in this network become high. There are lots of attacks for this network. In this paper we study about denial of service attack and its prevention and detection technique, in our propose work we give a novel solution to detect or prevent denial of service attack we implement our work in NS-2.35. Efficiency of our work we proof with the help of results.

Cloud systems are difficult to assess due to the relevance of modeling and its large-scale, complex structure, highlighting the dynamic and powerful layer. To solve this problem, the cloud system evaluation model is proposed based on queuing Petri network. It can efficiently model and simulate complex cloud system due to its powerful combination of quantitative evaluation and behavior description of the theoretical characteristics of queuing theory and Petri nets. On the basis of the model, we further increase the quantitative analysis and evaluation system cloud systems, and simulation of cloud systems, massively parallel dynamic service environments. Experimental results show that, queuing Petri network can effectively reflect the architectural features of various cloud computing system performance perspective, services.

Before we apply the Time Difference of Arrival (TDOA) to locate sound target, we should have known arrival time of each sensor node at first, we present two algorithms based on the cumulative sum to estimate the arrival time. We analysis influences of location accuracy, caused by five sensors position algorithm and the distance of each node. Taking these several factors, we find the best distance between sound source and node and propose a node organizational method-retest measure method. According to the consequence of first measure, organize node to locate again. The result of simulation shows that these algorithms are in effect and the retest measure can reduce deviation effectively.

Understanding the characteristics of cloud workloads is the key to making optimal configuration decisions and improving the system throughput. However workload characterization of cloud, especially in a large-scale production environment, has not been well studied yet. To gain insights on cloud workloads, we collected a one-week workload trace from a 100-node cloud cluster which hosts 1082 virtual machines. We characterized the workload at the granularity of virtual machines and physical nodes, respectively. We concluded with a set of meaningful observations. The results of workload characterization are representative and generally consistent with cloud cluster for public IaaS service providers, which can help other researchers and engineers understand the performance and VM characteristics of the cloud in their production environments.

Aiming at the problem of replica selection optimization in cloud storage load balancing technology, a new dynamic selection algorithm based on genetic algorithm(GASA in short ) is proposed. According to the principle of genetic algorithm, the model of dynamic selection strategy based on genetic algorithm is constructed, and then the key steps of the replica selection criteria and genetic algorithm are mapped, and then the optimal solution is obtained by using the probability equation. Lastly. simulation results from cloud test bed. which is based on Optorsim. show that GASA can reduce data access latency and bandwidth consumption. and effectively achieve cloud load balancing between storage nodes and improve the speed of data access.

Voice enabled ignition combines the speaker recognition and word recognition aspects of speech recognition. It replaces the function of a key in the starting of the ignition system of a car. An Fpga design incorporates the required components of a generic speech recognition system and uses the unique capabilities of hardware in term of parallelism to improve performance. The compression of speech for storage and playback was facilitated by the usage of the G729 standard for compression of speech.

Scientific CMOS sensor usually manifests V/Q non-linearity in charge-to-voltage conversion. Starting from the mechanism underlying this non-linearity, we build the V/Q non-linearity model to study the influence of modulation transfer function (MTF) and signal-to-noise ratio (SNR). Meanwhile, simulation verification is carried out. The results show that V/Q non-linearity improves SNR but causes the decrease of MTF of the electronic device. We propose the combination of video response curve with photo response non-uniformity (PRNU) noise curve to locate V/Q non-linearity. The validity of this method is proved by simulation verification and physical experiment. The present study provides reference for design optimization and compensation for non-linearity in scientific CMOS sensor.

As chemical industry is a fundamental strategic sector as well as one pillar industry of our national economy, chemical industrial parks of various scales has been built in many places to facilitate the production of chemical enterprises. However, considering the inflammability, explosibility and toxicity of the raw materials and products of such enterprises, accidents are very likely during the production, transportation and storage, forming major threats to human lives, the environment and property. By making use of key technologies of the Internet of Things such as sensing, accessing and networking, massive information storage, analyzing and processing capabilities of big data technology and the data computing methods of cloud computing technology, this paper designs a safety production information management platform for the daily supervision and emergency rescue of chemical industrial parks, proposes the system communication network framework based on the Internet of Things and the three-layer platform technical framework based on cloud computing, describes the technical structure and function of the sensing layer, the transmission layer, the infrastructure layer, the platform layer and the application layer of the platform, and elaborates key technologies including the design thought of uniform big data analyzing platform, regional collaborative emergency management operating mechanism, the combination of RFID technology and sensor network technology and model design of expert decision-making. The platform can realize logistics management of dangerous articles and key environment parameter monitoring of dangerous chemical enterprises, help enterprises in the safety compliance management to the whole production process, allow red light early warning and start responding early warning mechanism and rescue plan automatically, so as to effectively avoid the occurrence of potential accidents and safeguard and improve the economic and social benefits of enterprises.

This paper describes the design intelligent based nonlinear controller for three dimensions joint dental robot manipulators in presence of uncertainty. Three-dimension joint dental robot manipulator has some important challenges such as nonlinearity, multi-input multi-output (MIMO), uncertainty in dynamic formulation, and coupling effect. To eliminate the effect of these challenge linear filter PID controller is introduce in this research. However, this controller has some advantages in certain condition but in uncertainty, it has problems. To improve this technique in presence of uncertainty, minimum rule base PID like fuzzy controller is introduced. This method reduces the rise-time, steady state error and improves the trajectory performance in uncertain condition. Stability and robustness improve in proposed method as well reduce the number of rule base.

Nowadays, Cloud Computing has brought many new and efficient approaches for computation-intensive application areas. One typical area is Cloud based real-time device control system, such as the IoT Cloud Platform. This kind of platform shifts computation load from devices to the Cloud and provides powerful processing capabilities to a simple device. In Swarm robotics, robots are supposed to be small, energy efficient and low-cost, but still smart enough to carry out individual and swarm intelligence. These two goals are normally contradictory to each other. Besides, in real world robot control, real-time on-line data processing is required, but most of the current Cloud Robotic Systems are focusing on off-line batch processing. However, Cloud based real-time device control system may provide a way that leads this research area out of its dilemma. This paper explores the availability of Cloud based real-time control of massive complex robots by implementing a relatively complicated but better performed local collision avoidance algorithm. The Cloud based application and corresponding Cloud driver, which connects the robot and the Cloud, are developed and deployed in Cloud environment. Simulation tests are carried out and the results show that, when the number of robots increases, by simply scaling computation resources for the application, the algorithm can still maintain the preset control frequency. Such characteristics verify that the Cloud Computing environment is a new platform for studying massive complex robots in swarm robotics.