Earticle

Mobile Internet is a mainstream access and communication technology, due to access to Internet anytime and anywhere, the business will varied, and bring mass data, but the data processing has different characteristics, the delay and energy consumption are also different. Therefore, it is necessary to apply to different data mining methods in the cloud platform, so as to adapt to different business applications, this paper proposes an improved Apriori algorithm, theory and simulation can prove that the method is effective.

Flexible AC Transmission System (FACTS), under different conditions, are being incorporated in a power system for the improvement of active power flow along with voltage at each bus. Among the many FACTS devices, Interline Power Flow Controller (IPFC) is one of its kind, that has the capability of controlling multi-transmission systems. In this paper, an Improved Power Injection Model (IPIM) of Interline Power Flow Controller (IPFC) has been introduced. This model was included to Newton-Raphson (NR) method of load flow solution. The proposed model is tested on the standard test systems. The results of standard 5-bus system, IEEE-14 bus system is presented for the purpose of demonstration.

Cloud computing is a facsimile of legalizing ubiquitous, expedient, on-demand network access to a shared pool of configurable computing resources that can be rapidly furnished and released with negligible management effort. It relies on sharing computing resources rather than having local servers or personal devices to handle applications. The resource allocation, still lack on sustaining tools that enable developers to compare different resource allocation strategies in cloud computing. In this paper we initiate the concept of “skewness” to measure the bumpy utilization of a server. By minimizing skewness, we can improve the overall utilization of servers in the face of multidimensional resource constraints. Here we use skewness metric to combine VMs with different resource characteristics suitably so that the capacities of servers are well utilized.

Based on the the characteristics of maximal function, the algorithm for solving Min- max problem was researched in this paper. A new differential approximation function for the nondifferentiability of the objection function was also constructed. At same time, the property of the new differential approximation function is discussed and the processes of proves show that using the new approximation function to solve the nonlinear unconstrained min-max problem is feasible and effective. The preliminary numerical example shows that the algorithm is effective, and that have a large of convergence characteristics.

This paper shows another way to deal with speed control of DC Motor. Two nonlinear controllers, one of sliding mode control and the other Fuzzy gain scheduling PID, characterize another control structure. Essentially, in this configuration procedure, the SMC and Fuzzy gain scheduling PID controller have been consolidated by a switching system which is chosen by the Fuzzy supervisory system. The sliding-mode controller acts essentially in a transient state while the fuzzy gain scheduler PID controller acts in the steady state. The new structure epitomizes the favorable circumstances that both nonlinear controllers offer: a sliding-mode controller provides the fast rise time and fuzzy gain scheduling PID controllers lessening the chattering in the steady state. The simulation results show that utilizing the new hybrid SMC fuzzy gain scheduling PID controller gives "better" system response as far as transient and steady state are concerned when compared with the SMC or fuzzy gain scheduling PID controller’s applications. The controller parameters are tuned with the help of fuzzy logic.

In Quorum based cross datacenter distributed storage systems, data replication is a widely used technology. Such systems usually choose weak consistency, means they prone to select less number of replicas in read request in order to improve the performance, which lead to the possibility of getting stale data. In this paper, we propose an adaptive replica selection algorithm to make sure the system does not exceed specific stale read ratio while achieving good performance. Firstly, we choose a suitable distribution model from many candidates to estimate the time interval between current read request and nearest write request before it. Then we use Monte-Carlo method to simulate the processing order of read request and write request in different replicas. At last we use those estimated values to determine minimal number of replicas each read request needs to select in order to achieve a specific consistency level. We conduct a comprehensive experiments and the result shows our algorithm is effective.

Based on the second generation wavelet and information entropy, in this paper, we recognize the partial discharge pattern using the second generation wavelet (SGWT) and adaptive BP. Firstly, feature extraction of discharge signals are obtained using the SGWT and information entropy. Then, the extracted features are feed into the training BP network. The learning algorithm employed the conjugate gradient methods and the adaptive adjustment to train the error for BP network. Finally, we get the optimum training network, and the simulation results verified the feasibility of the algorithm.

Clustering is one of the main techniques used to increase the scalability of wireless sensor networks (WSNs). Furthermore, clustering can help to improve the energy efficiency of resource limited ad hoc network and increase the lifetime of sensor network. In this paper, we present a fuzzy clustering algorithm which improves the energy efficiency of LEACH protocol by using a better cluster head selection solution. For this propose, we apply the number of live neighboring nodes and remaining energy of network nodes to select better nodes for clustering. By creating more symmetric clusters, our scheme reduces the energy consumption of the sensors and this finally increases the lifetime of the sensor network. Finally, we simulate our solution in MATLAB software and compare it with the LEACH clustering protocol in various scenarios.

Aiming at the present situation of lacking of scientific and reasonable schemes in farm machinery emergency scheduling and allocating, farm machinery emergency scheduling and allocating mode leading by management department is proposed and the optimization model with the goal of cost as low as possible and completing emergency tasks within the prescribed period is established in the paper. According to the characteristics of farm machinery emergency scheduling and allocating, the scheduling and allocating algorithm with priority rules and the scheduling and allocating algorithm based on the non-cooperative game theory are proposed. Compared with two algorithms, the scheduling cost and the allocating amounts calculated by the algorithm based on the non-cooperative game theory are less than that of the algorithm with priority rules. It is concluded that the scheduling and allocating algorithm based on the non-cooperative game theory in the paper is more effective and reasonable.

With the popularity of new storage technologies, hybrid active storage system provides an efficient way to improve the performance of high-performance computing applications. However, current active storage efforts have neglected the storage performance gap between heterogeneous servers, largely affecting the overall system performance. In this paper, we propose SDD, a Skewed Data Distribution scheme for hybrid active storage systems. In contrast to traditional even data distribution schemes, SDD distribute data on servers with skewed amount of data based on their performance. We have implemented a prototype of our proposed data layout scheme in a parallel I/O system, and demonstrated its benefits with a typical data processing application. Experimental results show our proposed data placement scheme can significantly improve the overall active storage system performance.

This research presents novel Particle swarm optimization inspired by gravitational based search method to solve active power dispatch problem in electrical power system planning. The proposed PSO utilizes the operator of social thinking coupled with search capacity of gravity inspired algorithm to formulate and develop technique for active power dispatch problem to satisfy power demand requirements. Optimal scheduling of generators and system constraints to match load demand and losses is successfully done with proposed method. Total operating cost is minimized satisfying various bounds of system with proposed method. Exploration and convergence efficiency are evaluated to checklist the computational efficiency and robustness of the proposed technique. The suggested technique is tested and evaluated on different test systems comprises three, five, six test systems. Test results are compared with other techniques presented in literature .Investigations shows promising results which further benchmark the effectiveness of proposed method to solve complex optimization non linear problems.

Traditional location-based query measures the distance of all data points in various methods, but it is not applicable for the condition in which the weight of all sections in the road network are altered dynamically. In order to solve the new application problems brought by the real-time traffic status, a dynamic monitoring algorithm is designed in this paper, and it mainly updates the weight changes of road network with temporary network extension tree incrementally. On this basis, a continuous reverse K-nearest-neighbor (RKNN) query method is proposed, and it is proved to be efficient by the experimental results.

With the full swing of computer technology, evacuation models are constructed and simulated via computers to study the various phenomena and behaviors during the evacuation process. They open unique potential opportunity to evaluate the architectural design and provide the decision support for the emergency evacuation plans. This paper details a capacity constrained route planner (CCRP) algorithm, which is used to optimize evacuation path, and the distance map based on sub-goal cellular automata (CA) is also proposed to simulate the evacuation scenarios. Two evacuation scenarios are further discussed, and the experimental results strongly suggested that the sub-goal CA model could simulate the evacuation scenarios and evaluate the emergency evacuation plans effectively and finally help us penetrate to the essence of reality.

With the accelerated process of industrialization and urbanization, traditional agriculture begins to transform to modern agricultural mode. In order to develop modern agriculture, it is not only to reform the existing economic system, but also to increase public investment in rural areas, to create a good economic environment for agricultural development. In this paper, the author analyzes the performance of public investment in rural areas by using DEA network model, the data was selected from 25 provinces. Based on the empirical analysis, the result shows that the efficiency of rural public investment is low, and the difference between regions is obvious. By constructing the SFA model, we find that the technical efficiency and scale efficiency has been significantly improved after adjust initial input amount. Therefore, the government should increase the rural public investment, and pay attention to the efficiency of the funds use.

This paper introduces a new innovative mobile application architecture called "Container-Cloud", which could be probably an inevitable trend in complex cloud, connected devices and network ecosystem in IoE(Internet of Everything) era. The significance of C-C is comparably to Java for web, and android for mobile. There are some innovations in platform operations, in service operations, in developing and in use. The "Container-Cloud" architecture can solve those application silo, enable applications developed, debugged and deployed transparently in cloud, allows mobile applications to be generated on demand, and be migrated and scheduled automatically among cloud, and creates an opportunity for implementation of "context-aware" application in cloud. The paper also gives some "context-aware" application examples based on C-C support in IoE era.

Hadoop HDFS is an open source project from Apache Software Foundation for scalable, distributed computing and data storage. HDFS has become a critical component in today's cloud computing environment and a wide range of applications built on top of it. However, the initial design of HDFS has introduced a single-point-of-failure, since HDFS contains only one active namenode, if this namenode experiences software or hardware failures, the whole HDFS cluster is unusable, this is a reason why people are reluctant to deploy HDFS for an application whose requirement is high availability. In this paper, we present a solution to enable the high availability for HDFS's namenode through efficient metadata replication. Our solution has 3 major advantages than existing ones: We utilize multiple active namenodes, instead of one, to build a cluster to serve requests of metadata simultaneously; We implement a pub/sub system to handle the metadata replication process across these active namonodes efficiently; We also propose a novel replication algorithm to deal with the network delay when the namonodes are deployed in different areas. Based on the solution we build a prototype called NCluster and integrate it with HDFS. We evaluate NCluster to exhibit its feasibility and effectiveness. The experimental results show that our solution performs well with low replication cost, good throughput and scalability.

This paper present three versions of Cuckoo Search Algorithm (CSA) including conventional Cuckoo Search Algorithm (CSA), modified CSA (MCSA) and adaptive CSA (ACSA) for solving the fixed head short-term hydrothermal scheduling (ST-HTS) problem where the reservoir volume constraints and nonconvex fuel cost function of thermal unit as well as the power losses in transmission line are taken into account. Among the applied methods, ACSA is first developed in the study by performing two modifications on second new solution generation via the action of an alien egg to be abandoned. In the ACSA, all initial solutions or all solutions at the end of the previous iteration are evaluated and sorted into two kinds of solution, good solutions with lower fitness function and bad solutions with higher fitness function. The implementation of the first new solution generation first via Lévy flights in the ACSA is carried out similarly to that in MCSA. However, at the second new solution generation the ACSA evaluates the current solutions to choose the best one and use the information of the best one with a random solution to generate the second new solutions via the action of an alien egg to be abandoned. In addition, the probability of an alien egg discovery is considered an adaptive variable, which is set to the largest value at the beginning and decreased as the iteration is increased. Due to the adaptive value of the parameter, the ACSA can search an optimal solution but the trial runs are significantly decreased compared to CSA and MCSA. The performance of the ACSA is validated by testing on two systems and comparing with CSA, MCSA and other existing methods available in the paper.

In an educational laboratory, a large amount of computational experiment is often required. It is not realistic to prepare a large number of computers and other computing devices in the laboratory. Computer equipment will be limited without a large amount of computational experiments, so it is needed to make full use of computational resources. Cloud computing can be used to load the computing tasks to each computing unit, so as to make full use of network computing resources. Therefore, this paper proposes a kind of educational experiment platform based on cloud computing, which can improve the utilization of educational resources. The experimental results show the effectiveness of the method and the construction platform.

Big data analytical systems, such as MapReduce, have become main issues for many enterprises and research groups. Currently, multi-query which translated into MapReduce jobs is submitted repeatedly with similar tasks. So, exploiting these similar tasks can offer possibilities to avoid repeated computations of MapReduce jobs. Therefore, many researches have addressed the sharing opportunity to optimize multi-query processing. Consequently, the main goal of this work is to study and compare comprehensively two existed sharing opportunity techniques using predicate-based filters; MRShare and relaxed MRShare. The comparative study has been performed over TPC-H benchmark and confirmed that the relaxed MRShare technique significantly outperforms the MRShare for shared data in terms of predicate-based filters among multi-query.

This paper establishes a theoretical framework by defining a set of concepts useful for classifying graphs and computing the canonical labeling Cmax(G) of a given undirected graph G, which including the partition tree PartT(G), maximum partition tree MaxPT(G), centre subgraph Cen(G), standard regular sequence SRQ(G), standard maximum regular sequence SMRQ(G), and so on. The implementations of algorithms 1 to 5 show how to calculate them accordingly. The worst time complexities of algorithms 1, 2, 4, and 5 are O(n2) respectively. The time complexity of Algorithm 3 is O(n). By Theorem 3, all leaf nodes of PartT(G) and MaxPT(G) are the regular subgraphs. By Theorem 4 and 5, there exists only one Cen(G) in G. Regular Partition Theorem 6 shows that there exists just one corresponding PartT(G), SRQ(G), MaxPT(G), and SMRQ(G). One can use Classification Theorem 7 to category graphs. Theorem 8 and 9 establish the link between the Cen(G) and the calculation of the first node u1 added into MaxQ(G) corresponding to the canonical labeling Cmax(G) of G. Further, it utilizes the Cen(G) to calculate the first node u1 added into MaxQ(G). The proposed methods can be extended to deal with the directed graphs and weighted graphs.

In order to meet the need of rapid growth of the micro-class resources, it is urgently need to provide a highly reliable and cost-effective hosting platform. The architecture design of micro-lesson resource service platform based on PaaS pattern and the micro-lesson resource scheduling model based on MapReduce are given in this paper. Comparative experiment results show that, the three indicators of average response time, average load rate of server, and standard deviation of server load rate are better than that of traditional server clustering and OpenStack resource scheduling algorithm.

Nowadays, different types of bandwidth eater are growing rapidly. Cloud computing as an Internet computing has propagate day by day to provide different type of accommodations and resources to web utilizer. Cloud computing employs Internet resources to execute sizably voluminous-scale tasks. Ergo, to cull felicitous node to execute a task is able to enhance the performance of astronomically immense-scale cloud computing environment. There are several different nodes in a cloud computing system. Namely, each node has different capability to execute task; hence, only consider the CPU remaining of the node is not enough when a node is opted to execute a task. Consequently, how to select an efficient node to execute a task is very consequential in a cloud computing.In this paper, we propose a scheduling algorithm, Load Balancing through Arranging Task with Completion Time, LBATCT which combines minimum completion time and load balancing strategies. For the case study, LBATCT can provide efficient utilization of computing resources and maintain the load balancing in cloud computing environment.

With the development of science and technology, information collection, transmission, processing and wireless sensor network are inseparable. In the existing wireless sensor network clustering algorithm, the typical LEACH algorithm because of its uncertainty can lead to system life is short. This paper proposes a clustering algorithm based on load balancing, the algorithm fully considering the energy consumption of cluster head nodes, and the balance of the total energy of the network. Through simulation experiments, the algorithm system life is longer, the network energy consumption efficiency is higher.

With the rapid development of regional economy, the economic relationship of the urban system is more active, and it is developing in an unprecedented way, structure, speed and scale, and gradually showing the characteristics of the network connection. Based on the theoretical and empirical analysis, this paper analyzes the related issues of regional economic coordinated development from the analysis of the resources and environment carrying capacity. In the empirical part, we measure the environmental carrying capacity of resources in Xi'an City based on three indexes as the supply of natural resources, ecological environment tolerance and economic social conditions support. The result shows that index score as 0.0034, 0.5286 and 0.6070, at the same time, resources and environment carrying capacity has coordination relationship with population, capital and industrial development.

In this paper, we propose an analysis method of spatial and temporal characteristics of education network traffic based on flexible multiscale entropy (FMSE). As an improved method, flexible multiscale entropy has a significant improvement in stability and accuracy over multiscale entropy (MSE). We analyze network traffic in different time scales, space scales and traffic levels by building network traffic time-space analysis model and using flexible multiscale entropy as a method to quantify complexity of different network traffic subsequences. The results show that there are distinct characteristics in the complexity of different levels of network traffic. We also find that the existence of a large number of small network traffic flows has a significant influence on the complexity of network traffic.

In vehicular ad hoc network or VANET, the safety related messages broadcast by vehicles is used by other vehicles to make critical decisions. This may trap a vehicle taking wrong decision with dire consequences and authenticated vehicles or malicious vehicles may also broadcast wrong messages. This necessitates those messages of vehicles to be trusted after authentication process of vehicles, especially in the absence of centralized authority. In this paper, token based trust computation based on network connectivity duration of vehicles in VANET is proposed. The proposed technique is based on travelling time association among vehicles or network connectivity of vehicle at the time of driving on the road. We present simulations of the proposed technique that verify its correctness and reliability in the presence of infrastructure and absence of infrastructure. Results show that the efficacy of the proposed scheme and makes it viable for secure and lightweight trusted communication in a VANET.

As a new marketing way, more and more enterprises begin to pay more attention to the direct marketing channel, in order to gain more market share and economic profit. In this paper, we introduced two parameters into to the pricing model of e-business supply chain, as direct channel service level and retail channel service level. Then construct the pricing model under stackelberg game, bertrand game and centralized decision mode. Through the comparison and analysis of the pricing models under different decision-making environment, we get the pricing strategy that manufacturers and retailers should take when they make service cooperation. Because manufacturers always get more profits in the stackleberg game, so manufacturers tend to choose the stakelberg game with the retailer.

The development of the Internet has brought great opportunities for the sales of sports goods, so that enterprises should make the right online marketing strategy. This paper takes the consumer perception as the research content, study on the influence of different brand positioning factors on consumers by using empirical analysis. The results show that the factors affecting of brand can be divided into four categories as quality, product, service and social attributes. Among them, the quality is the most significant and the cumulative contribution rate is 79.532. Based on factor analysis method, the author identifies the main advantages and disadvantages of these brands, thus companies can effectively formulate the corresponding brand marketing strategy.