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In order to solve the task allocation and scheduling problem based on the analysis and research of hierarchical Petri net, this paper proposed a grid scheduling model based on hierarchical scheduling model. This model can ensure that the various elements of the loose coupling between the scheduling, to facilitate data resources deployment in grid environment; in addition, this paper proposed thought of the variable structure Petri network, through this technology can dynamic adjust work modeling and resource allocation, and to support the mission of exception handling; finally, to verify the rationality of the scheduling algorithm, and analyzes the performance and the efficiency of resource allocation algorithm.

In this paper, we present our work on information analysis for big data. Big data is generated every day in various fields such as complex physics simulations, genomics, meteorology, as well as biological and environmental research. Traditional data mining applications cannot handle big data well because the data sets are so large and complex. In this paper we present our approach to analyzing the information that is hidden in the big data using various strategies. This proposed approach can assist to improve the performance of existing data analysis technologies, such as data mining approaches in Bioinformatics and other fields.

Due to the complexity of the smart grid and the uncertainty of the distributed generation, there is a lot of uncertain information in smart grid. The theory of rough intuitionist fuzzy set is proposed in this article to deal with the fault diagnosis in the case of uncertain information. Fault position and the fault device can be judged based on current, voltage and the action of protection device and circuit breakers under failure state, including error and incomplete fault signal caused by malfunction, bit error in signal propagation. Diagnosis rules can be extracted from the set of fault samples. Diagnosis rules obtained by this method can be used to establish the fault diagnosis knowledge base, thus provides a feasible method for the fault diagnosis of smart grid.

The Smart Grid is an electrical power infrastructure that makes intelligent decisions about the state of the electrical power system to maintain a stable environment. It is expected that the smart grid will radically add new functionalities to legacy electrical power systems. However, believe that this will in turn introduce many new security risks. With the smart grid’s backbone communication networks and sub networks, there are possible scenarios when these sub networks can become vulnerable to attacks. Ensuring security in these networks is challenging because most devices are resource constrained. In addition, different protocols that are used in these networks use their own set of security requirements. In this article, the securities Requirements of smart grid communication networks are firstly identified. The public key infrastructure (PKI) is a viable solution; it has some difficulties to satisfy the requirements in availability, privacy preservation, and scalability. To complement the functions of PKI, introduce some novel mechanisms so that those security requirements can be met. In particular, propose a mechanism to efficiently resist Denial-of-Service (DoS) attacks, and some suggestions to the security protocol design for different application categories.

It is an importance step for near-duplication detection to perform file classification in the data mining field, in this paper an improved classification course is proposed which consists of training and test course corresponding to its algorithm respectively. It utilizes the MapReduce computing model created by Google to conduct the classification calculation. Specially, the Sogou news data with various data amounts which simulated the massive data set was used for testing effectiveness and a comparative evaluation on execution time and speedup was accomplished on the experimental circumstance. The results obtained shows that the classification course obviously reduces the execution times greatly and gains the ideal speedup ratio when increasing data amounts, achieves the better performance.

Wireless Sensor Networks (WSNs) have characteristics of large size, limited resources, large amount of transmission data, and so on. In order to reduce the redundancy of sensed data and decrease network data traffic. We applied CS to clustered structure, proposed Low-Latency Compressed Sensing model (LLCS) which is based on the spatial-temporal correlation of sensed data, the model is also capable of processing sparse abnormal events which is a crucial feature in WSNs. We analyzed the relationship between compression ratio and sampling rounds and verified the abnormal event processing method. The results of simulation experiments using the real data show that LLCS could reduce data transfer volume significantly and process abnormal readings effectively.

With the development of MEMS and CMOS technologies, the implementation of a huge number of wireless distributed micro-sensors that can be simply and rapidly deployed to form highly redundant, self-configuring, and ad hoc sensor networks. Sensor nodes are generally battery- devices, the critical facets to face concern are how to minimize energy consumption of nodes, so that the lifetime of sensing nodes can be maximized. The first step to achieve this goal is to know completely the sources of energy consumption in wireless sensor networks. In this paper, sources of energy consumption at various communication layers have been studied and investigated. Moreover, the energy consumption for the components of a typical sensor node and the impact of communication protocols stack on the energy consumption are discussed. In the sequel, the sources of energy consumption in each communication layer individually are studied. Then, a survey has been provided for existing energy models and the classification of these models into physical layer, MAC layer and cross-layer energy models.

On demand resource forecasting in cloud computing is an crucial guarantee for achieving effective management of all virtualized resources and reducing data center energy consumption. According to single forecasting model cannot integrate all the valid information which leads to the decline in prediction accuracy. This paper proposed an optimal combination prediction model for cloud computing resource requirement. This model is based on generalized Dice coefficient and the induced ordered weighted geometric mean (IOWGA) operator, as well as improved Elman neural network and grey forecasting model. It is able to accurately reflect the random information and trend information in cloud computing load thus will enhance the overall prediction accuracy. The experiment results show this method is feasible and effective.

This paper proposes an adaptive perturbation and observation (P&O) algorithm for maximum power point tracking (MPPT) control strategy of a grid-connected solar photovoltaic (PV) system under varying atmospheric conditions. This strategy is necessary in order to extract maximum power output from a solar PV panel. The adaptive P&O algorithm is proposed to utilize a variable perturbation step size which depends on power changes. The obtained simulation results are compared with those using the conventional P&O algorithm, which show the effectiveness of the proposed MPPT algorithm in the MPPT of a grid-connected solar PV system.

A major challenge facing cloud computing is virtual resource allocation with dynamic characteristics. Evaluation of a resource allocation strategy using a single aspect can no longer meet the real world demands. We resolve this issue from the perspectives of users and resource providers using a particle swarm algorithm for resource allocation. With this algorithm, we establish an allocation model using the shortest task completion time and the lowest cost as the constraints. The fast convergence rate of the particle swarm algorithm is then used to find the optimal solution for resource allocation. The velocity weight of each particle is self-adaptively adjusted based on the fitness value of each particle, resulting in an improvement in the global optimization and convergence capabilities. Finally, a simulation with the CloudSim platform shows that this algorithm can take into account the completion time and cost, which ensures the minimum cost in the shortest possible time to complete the task to improve resource utilization.

Cloud computing is the most dynamic field of IT industry. It is becoming very famous due to its less resource consuming and higher output. Though Cloud computing is very vast and useful technology but it is not remained untouched from attackers or hackers. The most common attack notified on the cloud environment is DDoS attack. DDoS attacks i.e. Distributed Denial of service attacks happens on a cloud environment in such a way that, two or more than two attackers sends the multiple SOAP requests at the same cloud server and consume all the legitimate resources. From the study it has been concluded that, none a system is developed so far to prevent the DDoS attacks completely because even the detection of DDoS attacks is a major issue and prevention is a very big thing to achieve after detection and mitigation. This paper is focused on underlying the introduction about DDoS attacks and the next part of the paper is followed by the comparative analysis of the different techniques and algorithms used in detecting the DDoS attacks in cloud environment.

As the time of cloud computing comes, Cloud computing is estimated to be one of the most transformative technologies in the history of computing. Cloud organizations, including the providers and customers of cloud services, have yet to establish a well-defined forensic capability. Computer cybercrime has become very prominent forms of crime, but the traditional forensic methods have not met the requirements of the new digital forensics environment. In this paper, upon the use of cloud computing resource sharing, storage distribution and other characteristics and based on the analysis of cloud computing environments difficulties on digital forensics, new digital forensics methods and new digital forensics architecture in the cloud-based platform are proposes to meet rapid forensics needs in the era of cloud computing and to deal with the effectiveness, usefulness, depth issues and real-time and reliability problems.

Multiple Unmanned Underwater Vehicles Is a typical combinatorial optimization problem, to achieve multiple AUV, coordinated, collaborative tasks to complete complex jobs subsea. Through analyzing the ant colony optimization algorithm, the paper proposed An Meta-heuristic ant colony optimization algorithm the Implementation to solve the multi AUVs to achieve the task allocation problem, and had simulation test based on the consolidated analyze the advantages of multiple unmanned underwater vehicle .results show that the ant colony optimization algorithms in solving multi-task allocation problem of multiple unmanned underwater vehicle showed a good performance.

The cloud system is considered as the hub of hosted services in which a particular user can access the cloud system remotely by using applications or by using the browsers. Compared to desktop devices, mobile device have inherent constraints such as limited processing power, memory, and battery capacity. With the propagation of mobile cloud applications, researches are looking for new solutions to address these limitations. One of the major solution is mobile cloud computing, which uses cloud infrastructure to enhance the capabilities of mobile device. This paper focuses on an emerging technology known as mobile offloading and mashup. The offloading uses the quality of services concept to remove mobile application limitations. Cloud services are the application programming platform where users can create new applications and mashup their functionalities. The paper describes the quality of services parameters used for offloading in mobile applications and also describes the existing approaches for mobile cloud computing.

The heartbeat detection is an important technology to detect faults of cluster nodes. This paper proposes a Double-layer Heartbeat Detection Model and a Double-layer Heartbeat Detection Algorithm (DLHB) orienting to embedded heterogeneous clusters to obtain high availability of embedded heterogeneous clusters. This algorithm divides the nodes in the embedded heterogeneous cluster into different areas by their physical positions. In each node area, a node is selected as the master control node. The inter-area detection uses heartbeat ring mechanism. The intra-area detection can only be made among master control nodes. The experiment results show that, DLHB is more accurate in prediction compared with DHB, and can detect faults concurrently occurring on multiple nodes with less time compared with the heartbeat ring algorithm.

With rapidly growing influence and demand for cloud computing services, issues involving control, migration, security, availability of data, trust matters directly related to sensitive information stored can no longer be treated lightly. Through our work as explained in this paper, we discuss these topics, and also review several algorithms which are used in cloud computing. These algorithms are implemented in a cloud computing architecture to balance load to improve efficiency. Weighted active monitoring load balancing algorithm, dynamic load balancing algorithm, static algorithms, and ant colony algorithm are all used for load balancing in distributed computing systems. In this paper, we propose a load balancing technique based on Tree Parity Machine. Our approach is to design a Tree Parity Machine based model that would distribute workload evenly among all nodes. The technique proposed by us is not complicated. It has been conceived such that it can work efficiently with training sets that have been proven to produce intended results. Our Tree Parity Machine based model would predict the demand and, accordingly, allocate necessary resources. Thus, it would be able to maintain active servers to meet existing demands. As a result, consumption of energy can be significantly lowered. The conservative approach of over-provisioning is, thus, avoided.

With the continuous development of economy and social progress, the social requirement of power network is more and more high. This paper, as a basis research of distributed generation, proceed mainly from the impact of distributed generation on power grid, detailed fine analysis of the influence of all kinds of distributed generation on power network adverse, including power system stability, power quality, power supply reliability effect. In order to avoid these adverse effects, we combining the distributed grid technology and smart micro grid, put distributed generation as a branch of the micro grid that can be ideal to connect grid and can avoid most of the problems mentioned above. Distributed intelligent micro grid system uses variety of new energy supply, it is a comprehensive model of power electronic technology, distributed generation, renewable energy power technology and energy storage technology .The paper provides a reference for the use of future distributed power generation.

In order to fully utilize DG and improve system reliability, the author puts forward the rash recovery strategy for distribution network with DG. It adopts the heuristic method by taking DG as the source point of research for distribution network power recovery. Based on simplifying the network through improving branch method with the consideration of purposes of network damage to minimum extent and switch action time, it conducts reconstruction after the crash. It optimizes the power supply recovery strategy according to the load transfer condition after the crash in order to minimize the damage to distribution network caused by blackout. It conducts example test at IEEE33 node and IEEE69 node to verify the validity of this scheme.

Road traffic on public roads around the world is a vital problem and is seemly a major pretend to conclusion makers. Urban region have a great stack of traffic jams. Many superimposed concepts of road traffic Gas lit, Wireless sensor networks have appeared in the past few years. The living methods for traffic management are not effective. At that place require for a hefty and scalable high-execution computing. Cloud computing is turning a good engineering to provide a potent and scalable computing at low cost. Therefore Sensor-Cloud is seemly popular in recent years. This paper attempts to review these concepts and discusses their ending alignment with other phase of networks like Sensor-cloud network. The key obstructions to the successful acceptance of Sensor-could have been distinguished and directions for the existing and finally the conclusion have been discussed.

In order to solve issues of grid network as a broadband wireless access network, such as high data transmission rate and long-distance signal coverage and so on, this paper proposes two heuristic algorithms with O (m3) time complexity, based on the user neighbors’ information and location information. The algorithm will meet transferring the minimum relay deployment problem required by users into the minimum clique partition problem of adjacency graph and gives specific algorithm steps. Experimental results show that: the proposed algorithm ensures network performance and reduces network cost.

Research the Incident Vehicle Routing Problem with Soft Time-windows (IVRP-STW). Associated logistics scheduling problem with soft time windows using the logistic function optimization method to improve the chaotic particle algorithm, compared with the GA and the standard PSO algorithm. Simulation results show that such optimization method can effectively improve the global search of the particles in the particle swarm and the ability of local search is effective in resolving such problems.

Regarding to the theories and techniques of cloud computing having been developed and applied in scientific computing field, tasks can be conveniently managed by the cloud platform on the basis of standardized scheduling system with cost (resources consumed) recorded. However, there are two issues which drag the customers’ attention: 1) When will the tasks expect for termination (response time) under a specific resource scheduling; 2) What is the best scheduling solution by considering cost. In order to reply these two questions, a Kriging based forecasting and scheduling system has been proposed in this paper. With the cooperation between the scientific designer and the cloud designer, the design variables for evaluating the cloud applications can be achieved; Kriging surrogate model is then introduced to simulate the approximate functional relationship between the design variables and the response time of the tasks; Sequential quadratic programming optimization algorithm then provides the best scheduling solution for the tasks if cost constraints are to be met. Two real scientific computing cloud applications have been testified on an OpenStack cloud platform, with consequences described in details. The work in this paper has put forward a novel way for the designers and the customers on predictable and reasonable scheduling strategies for the various resource-intensive scientific computing cloud applications with surrogate models and optimization algorithms.

With the rapid development of network information technology, manufacturing presents a development trend of networking, virtualization, intelligence and servitization. Cloud manufacturing (CMg) with a new type of service-oriented manufacturing model is put forward and excellently satisfies the development requirements of modern manufacturing enterprises. With considering the complex characteristics of cloud manufacturing, firstly, we construct Cloud manufacturing resource scheduling system architecture to schedule service task and allocate manufacturing resource, which includes user requirements, task plan, global task scheduling, service collaboration allocation, virtualization and cloud manufacturing resource. Secondly, we propose a comprehensive resource allocation model based on auctions theory to satisfy different users’ request, and then the greedy method is used to search the optimal bid in the bid set. Finally, we implement some simulation experiments, and simulation results show that the solution algorithm of CARA’s winner determination problem gets the same solution as the M-HEU algorithm with less time complexity, and it is more applicable, efficient and effective for online multi-resource allocation.

To solve the cloud computing resource scheduling problem in IaaS platform, a scheduling model based on ant colony algorithm was proposed. In this model, pheromone changes dynamically according to the best route searched by ants. This model automatically updates pheromones and guides ants to search the global best route. Experiment results show that the proposed model is of better ability in energy consumption in the IaaS cloud computing platform.

The main objective of this work is to present energy storage technologies that are the best suited to mitigate wind variability and help balancing between generation and consumption. This work is dedicated to the study of storage technologies that can facilitate integration of wind energy as well as other resource of energy. Nevertheless, it is important to keep in mind that a single storage device can perform simultaneously several functions and consequently be used for several applications. For example, an energy storage system associated with wind generation could be used both for “frequency regulation reserve” and “wind energy time shifting”. Several cost-benefit studies show that a storage device has to perform simultaneously several functions to be cost effective.

The virtual resource scheduling is an important topic in the field of cloud computing. Based on particle swarm scheduling algorithm, this paper introduces cellular automata theory to construct a new cellular particle swarm scheduling algorithm. This approach through mathematical modeling for virtual resource scheduling of cloud computing and complete the final search configuration based on directional optimization objective function. Experimental results show that the proposed method has more excellent scheduling performance, in the case of changes in resources, can be also kept stable scheduling balance.

With development of scale, diversity and complexity of network traffic, the drawbacks of traditional machine learning methods on traffic classification is gradually exposed, especially the false positive problem in large-scale real network traffic classification is particularly serious. In this paper, aiming at reducing the false positive rate of network traffic classification, an effective network traffic classification method --- CMM method. CMM method contains three steps, including dividing the training set into clusters, forming sub-classifiers, and classifier integration in accordance with the principle of minimization and maximization. In this paper, we firstly demonstrate the effectiveness of this method in reducing the false positive rate. Secondly, we conduct experiments in large-scale national backbone network, such as the SSL protocol classification and experimental results verify the effectiveness of this method in large-scale the actual network traffic classification.

Cloud computing is a concept that provides services on demand and follows pay-as-you-use approach. Mobile devices that uses cloud computational power and Internet accessibility composed to create a new stream which is called mobile cloud computing and provides services like anytime and anywhere for the enterprise. Enterprise architecture will take care of organizational principles that align the business functional principles with information technology. In this paper, we have looked into the usage of mobility through the power of cloud computing for corporate sector. A critical review and gap analysis between existence models is also provided.