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To improve the quality of knowledge service selection in a cloud manufacturing environment, this paper proposes a cloud manufacturing knowledge service optimization decision method based on users' psychological behavior. Based on the characteristic analysis of cloud manufacturing knowledge service, establish the optimal evaluation index system of cloud manufacturing knowledge service, use the rough set theory to assign initial weights to each evaluation index, and adjust the initial weights according to the user's multiattribute preference to ensure that the consequences are allocated correctly. The system can help counselors acquire psychological knowledge in time and identify counselors with suicidal tendencies to prevent danger. This paper collected some psychological information data and built a knowledge graph by creating a dictionary and generating entities and relationships. The Han language processing word segmentation tool generates keywords, and CHI (Chi-square) feature selection is used to classify the problem. This feature selection is a statistical premise test that is acceptable when the chi-square test results are distributed with the null hypothesis. It includes the Pearson chi-square test and its variations. The Chi-square test has several benefits, including its distributed processing resilience, ease of computation, broad information gained from the test, usage in research when statistical assumptions are not satisfied, and adaptability in organizing information from multiple or many more group investigations. For improving question and answer efficiency, compared with other models, the BiLSTM (bidirectional long short-term memory) model is preferred to build suicidal tendencies. The Han language processing is a method that is used for word segmentation, and the advantage of this method is that it plays a key role in the word segmentation tool and generates keywords, and CHI (Chi-square) feature selection is used to classify the problem. Text classifier detects dangerous user utterances, question template matching, and answer generation by computing similarity scores. Finally, the system accuracy test is carried out, proving that the system can effectively answer the questions related to psychological counseling. The extensive experiments reveal that the method in this paper's accuracy rate, recall rate, and F1 value is much superior to other standard models in detecting psychological issues. [ABSTRACT FROM AUTHOR]

This papers studies a high-performance node-level service grid model, which aims to solve the problem that the current pod-level service grid model affects the service operation and consumes many computing resources. The main method of the node-level service grid model is to improve pod-accompanied service grid sidecar with the node-accompanied service grid sidecar sharing of multiple pods, combined with the cut-through of user mode protocol stack and scaling of node-level service grid sidecar. By the performance comparison of pod-level service grid model and node-level service grid, we can conclude that node-level service grid model can isolate pod services without affecting service operation, significantly reduce memory consumption without multiplying with the number of pods, and largely reduce end-to-end network delay about 30% but the overall CPU consumption as the same as that of the pod service grid model. It indicates that the node service grid model can obtain better business benefits than the pod service grid model in container cloud, cloud service providers can provide grid services for more tenants with less memory resources and network latency, and adding grid services has no impact on the operation of user applications. [ABSTRACT FROM AUTHOR]

With the in-depth development of the application of virtual reality technology, people have higher and higher requirements for the complexity and realism of virtual scene, which far exceeds the real-time processing ability of computer graphics hardware. Back to this, there is an urgent need to solve the contradiction between the complexity of the scene and the real-time interaction. In this paper, the real-time visualization of real-time distributed virtual reality is studied in many aspects. Starting with the analysis of the characteristics of real-time visualization technology of distributed virtual reality, this paper studies the system structure of real-time distributed virtual reality, puts forward the idea of realizing real-time distributed virtual reality visualization, introduces grid scientific computing model, and constructs a three-dimensional vertical visualization platform of distributed virtual reality. The visual analysis of virtual reality is carried out for the corresponding slope and corresponding geological conditions; the comprehensive analysis of spatial distribution is carried out by using three-dimensional visualization, the relevant contents of real-time display, and the generation and real-time display of realistic graphics. In order to increase the realism of 3D image, visibility judgment, and blanking technology, the level of detail model and texture mapping is used. The simulation results show that the grid scientific computing model is effective and can support the three-dimensional visual analysis of distributed virtual reality. [ABSTRACT FROM AUTHOR]

The traditional architecture of e-commerce data management needs to adapt to the new and more complex environment, which needs to provide massive data management, compatibility of different types of data, and better user experience. Cloud computing technology is a synthesis of parallel, distributed, and grid computing and is one of the future directions of information technology development. In this paper, we use cloud computing data management technology to study the data management of e-commerce. It is hoped that the current e-commerce data management can be improved with the help of cloud computing technology. This paper starts from analyzing the current e-commerce representatives Taobao.com and Jingdong Mall to find out the data characteristics of these two enterprises, analyze the existing data problems, and find the aspects that can be improved. Using this open-source cloud computing implementation solution, we solve the storage problem of large files and unstructured small files in the e-commerce system. [ABSTRACT FROM AUTHOR]

Cloud computing has received a resounding welcome. It was created following methodical and thorough study in web services, distributed computing, utility computing, and virtualization, and it offers several benefits, including lower costs, less space required, and easier management. These advantages bring in a significant number of new users to the cloud platform every day. In addition, because cloud computing is an Internet-based computing paradigm, it must deal with the issue of overwhelming demands through effective load-balancing. A very small number of studies only focus on load-balancing problems in cloud computing platforms, while the majority of load-balancing research is accessible in many domains, including parallel, distributed, and grid computing. Infrastructure as a Service (IaaS), Software as a Service, and Platform as a Service are the three basic categories under which cloud computing falls. For these models, there are notable differences in the load-balancing techniques used. This work compared the outcome with the current method and presented a hybrid agent-based load-balancing approach for the IaaS platform. [ABSTRACT FROM AUTHOR]

Mobile grid computing has been a popular topic for researchers due to mobile and IoT devices' ubiquity and their evergrowing processing potential. While many scheduling algorithms for harnessing these resources exist in the literature for standard grid computing scenarios, surprisingly, there is little insight into this matter in the context of hybrid-powered computing resources, typically found in Dew and Edge computing environments. This paper proposes new algorithms aware of devices' power source for scheduling tasks in hybrid environments, i.e., where the battery- and non-battery-powered devices cooperate. We simulated hybrid Dew/Edge environments by extending DewSim, a simulator that models battery-driven devices' battery behavior using battery traces profiled from real mobile devices. We compared the throughput and job completion achieved by algorithms proposed in this paper using as a baseline a previously developed algorithm that considers computing resources but only from battery-dependent devices called Enhanced Simple Energy-Aware Schedule (E-SEAS). The obtained results in the simulation reveal that our proposed algorithms can obtain up to a 90% increment in overall throughput and around 95% of completed jobs in hybrid environments compared to E-SEAS. Finally, we show that incorporating these characteristics gives more awareness of the type of resources present and can enable the algorithms to manage resources more efficiently in more hybrid environments than other algorithms found in the literature. [ABSTRACT FROM AUTHOR]

Task scheduling is an essential component of any distributed system because it routes tasks to appropriate resources for execution, such as grids, clouds, and peer-to-peer networks. Common scheduling algorithms include downsides, such as high temporal complexity, non-simultaneous processing of input tasks, and longer program execution times. Exploration-based scheduling algorithms prioritize tasks using a variety of methods, resulting in long execution times on heterogeneous distributed computing systems. As a result, task prioritization becomes a bottleneck in such systems. It is appropriate to prioritize tasks with the shortest execution time using faster algorithms. The genetic algorithm (GA) is one of the evolutionary approaches used to solve complex problems quickly. This paper proposes a parallel GA with a MapReduce architecture for scheduling jobs on cloud computing with various priority queues. The fundamental aim of this study is to employ a MapReduce architecture to minimize the total execution time of the task scheduling process in the cloud computing environment. The proposed method accomplishes task scheduling in two stages: first, the GA was used in conjunction with heuristic techniques to assign tasks to processors, and then the GA was used in conjunction with the MapReduce framework to assign jobs to processors. In our experiments, we consider heterogeneous resources that differ in their ability to execute various tasks, as well as running a job on different resources with varying execution durations. The results show that the proposed method outperforms other algorithms such as particle swarm optimization, whale optimization algorithm, moth-flame optimization, and intelligent water drops. [ABSTRACT FROM AUTHOR]

The increasing desire for distributed computing systems has attracted huge interest in memory and computing resources. The cloud provides on‐demand access to provide a flexible allocation of resources for reliable services. Therefore, there should be a provision in which resources are accessible to request users to satisfy user needs. In classical techniques, the allocation of resources by satisfying power and Quality‐of‐Services is a challenging aspect. This paper devises a novel technique for optimal resource allocation, namely, Exponentially Spider Monkey Optimization (E‐SMO). Here, the proposed E‐SMO is devised by combining Exponential Weighted Moving Average and Spider Monkey Optimization (SMO). Besides, the fitness function is newly devised considering resource utilization and resource cost. After that, the cloud resources employ a switching strategy to reduce power consumption to prevent the switching of redundant servers. For optimal switching, the proposed E‐SMO is utilized with other fitness factors that compute the number of applications assigned in the physical machine using the switching state is in OFF condition. Thus, the server switching model is incorporated to activate or deactivate the server when not in use for effective resources utilization. The proposed E‐SMO algorithm outperformed other methods with the maximal resource utilization of 0.874, the minimal resource cost of 0.302, and the minimal power usage of 0.275. [ABSTRACT FROM AUTHOR]

Next generation communications (NGC) represents advanced communication environments featuring objects that are focused on users. It is crucial to protect information and digital forensics for NGC. In this paper, we aim to address network security for NGC, including IEEE standard 802.16 that are associated with NGC utilization. The IEEE standard 802.16 is a wireless air interface technology in Wireless Metropolitan Access Network (WMAN). Now in the new era of wireless networking, it is the solution for 'last mile' problem. Point-to-MultiPoint (PMP) network architecture network mode and mesh network mode are two optional networking modes proposed in this standard. However, the security problems in its original protocol may be becoming the most serious obstacle in its marketable producing process. In this paper, explanation and analysis of the security mechanism are based on PMP and mesh networking modes, respectively. This paper focuses on authentication & authorization, which is the most important part but not explicitly defined in original security design of IEEE standard 802.16. Two improved schemes focused on authentication & authorization based on the two modes are proposed to strengthen 802.16 WMAN security, respectively. The relevant analysis and simulation results prove that our schemes are effective. Copyright © 2009 John Wiley & Sons, Ltd. In this paper, we modified the Wireless Key Management Infrastructure (WKMI, see Figure 10) proposed before for Point-to-MultiPoint (PMP) to adapt the mesh mode's requirements. Because UDP is not a reliable transport protocol, the proposed suitable protocol shall be RUDP (reliable UDP). This paper focuses on Authentication & Authorization, which is the most important part but not explicitly defined in original security design of IEEE standard 802.16. Two improved schemes focused on Authentication & Authorization based on the two modes are proposed to strengthen 802.16 WMAN security, respectively. The relevant analysis and simulation results prove that our schemes are effective. [ABSTRACT FROM AUTHOR]

The IoT brings digital and intelligent changes, which can change many ways of seeing the world. Among them, the display of rare works of art is easily overlooked. This paper studies the artwork display system based on the IoT technology and uses the Spring framework under the uRLLC communication protocol to design the artwork display system through the design of the database and the controller. This article takes the artistic work display system as the research object, studies the operating mode of the system model, conducts system testing in an indoor environment, and compares communication with traditional distributed computing technology. Based on the experimental results, it can be seen that the real-time performance of this system is far superior to traditional cloud computing systems. [ABSTRACT FROM AUTHOR]

Automatic reasoning is a very challenging research area in the field of artificial intelligence. Automatic inference based on lattice-valued logic is an important basic research content in the field of intelligent information processing. It is of great academic significance to deal with incomparability, linguistic valued information, and formal verification of the correctness of corresponding software and hardware systems in various applications. Based on the extensive work of α-resolution automated reasoning in linguistic truth-valued lattice-valued logic, this paper discusses the structure and its α-resolvability of the generalized literal. For further improving the efficiency, a topology graph deduction is proposed as well as its soundness, completeness, and universal algorithm. The use of distributed computing and resource sharing is the core idea of this deduction. In the decision process, as long as one satisfiability solution is found by subtask, the decision result can be given directly, shortening the decision time. The algorithm reduces the time complexity from factorial level to square. [ABSTRACT FROM AUTHOR]

Smart grid technology has attracted a lot of attention in the communication and power engineering communities because of its high efficiency and environmental friendly power production and delivery. Smart grid communication networks act as not only a bridge between the end power users and the utility but also a backbone of the entire smart grid system. It is a challenging task to deploy smart grid communication networks because of interference-intensive environment. The goal of this paper is to explore various issues on deployment of smart grid communication networks. The first half of this paper provides a survey on smart grid communication networks. The background of smart grid communication networks is introduced, and the IEEE 802.15.4g standard for smart utility networks is reviewed. In addition, the issues on network topology and routing protocols of smart utility networks are addressed, followed by the discussions on optimizing gateway deployment. Various methods for addressing coexistence issues in smart grid communication networks are presented. The second half of this paper is to propose a channel discovery algorithm exploiting the inherent CSMA/CA mechanism in the protocols. Because of the use of CSMA/CA mechanism and up-to-date channel status table, the proposed algorithm can quickly find an available channel for transmission when interferences occur. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Discourse coherence is strongly associated with text quality, making it important to natural language generation and understanding. However, existing coherence models focus on measuring individual aspects of coherence, such as lexical overlap, entity centralization, rhetorical structure, etc., lacking measurement of the semantics of text. In this paper, we propose a discourse coherence analysis method combining sentence embedding and the dimension grid, we obtain sentence-level vector representation by deep learning, and we introduce a coherence model that captures the fine-grained semantic transitions in text. Our work is based on the hypothesis that each dimension in the embedding vector is exactly assigned a stated certainty and specific semantic. We take every dimension as an equal grid and compute its transition probabilities. The document feature vector is also enriched to model the coherence. Finally, the experimental results demonstrate that our method achieves excellent performance on two coherence-related tasks. [ABSTRACT FROM AUTHOR]

Measurements gathered by smart metres and collected through the automatic metering infrastructure of smart grids can be accessed by numerous external subjects for different purposes, ranging from billing to grid monitoring. Therefore, to prevent the disclosure of personal information through the analysis of energy consumption patterns, the metering data must be securely handled. Peer-to-peer networking is a promising approach for interconnecting communication nodes among the automatic metering infrastructure to efficiently perform data collection while ensuring privacy and confidentiality, but it is also prone to various security attacks. This paper discusses the impact of the most relevant peer-to-peer attack scenarios on the performance of a protocol for privacy preserving aggregation of metering data. The protocol relies on communication gateways located in the customers' households and interconnected by means of a variant of the Chord overlay. We also propose some countermeasures to mitigate the effects of such attacks: we integrate a verifiable secret sharing scheme based on Pedersen commitments in the aggregation protocol, which ensures data integrity, with compliance checks aimed at identifying the injection of altered measurements. Moreover, we introduce Chord auxiliary routing tables to counteract the routing pollution performed by dishonest nodes. The paper evaluates the computational complexity and effectiveness of the proposed solutions through analytical and numerical results. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper uses a multidimensional big data matrix model to optimize the analysis and conduct a systematic construction of the enterprise performance evaluation system. The adoption of new research methods and perspectives to promote the study of the use of performance information is of great significance to achieve the effectiveness, science, and sustainability of corporate performance management. To solve the problem of objectivity and scientificity of performance information use, this part attempts to analyze performance information use from the perspective of the multidimensional big data matrix, focusing on the techniques and methods in the process of promoting performance information use from the multidimensional big data matrix and tries to construct a system model of enterprise performance information use from two dimensions: the use of performance information sources and the use of performance information results. Based on multiple theoretical hypotheses, a theoretical and empirical basis is provided for the division of demand dimensions of enterprise performance evaluation system. Through social capital theory, three dimensions of network social capital, cognitive social capital, and structural social capital are hypothesized, and the logistic regression method is applied for empirical study. The results show that these three dimensions have significant effects on the knowledge demand of enterprise performance evaluation systems. It is verified that the multidimensional big data matrix can enhance the quality of performance information sources and improve the objectivity of performance information. In the performance information source use dimension, the analysis verified that the collection and preprocessing technology of big data can realize the automation, real-time, and diversification of information collection and preprocessing, and enhance the objectivity of performance information. Big data helps to improve the quality and effectiveness of performance information results use. In the dimension of using performance information results, the distributed computing and analysis processing technology of big data can assist the decision support system, and the use of information can be shifted from micromanagement to decision support, to realize the scientific use of performance information and improve the quality of enterprise management decisions. [ABSTRACT FROM AUTHOR]

Cloud computing is the most prominent established framework; it offers access to resources and services based on large-scale distributed processing. An intensive management system is required for the cloud environment, and it should gather information about all phases of task processing and ensuring fair resource provisioning through the levels of Quality of Service (QoS). Virtual machine allocation is a major issue in the cloud environment that contributes to energy consumption and asset utilization in distributed cloud computing. Subsequently, in this paper, a multiobjective Emperor Penguin Optimization (EPO) algorithm is proposed to allocate the virtual machines with power utilization in a heterogeneous cloud environment. The proposed method is analyzed to make it suitable for virtual machines in the data center through Binary Gravity Search Algorithm (BGSA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). To compare with other strategies, EPO is energy-efficient and there are significant differences. The results of the proposed system have been evaluated through the JAVA simulation platform. The exploratory outcome presents that the proposed EPO-based system is very effective in limiting energy consumption, SLA violation (SLAV), and enlarging QoS requirements for giving capable cloud service. [ABSTRACT FROM AUTHOR]

Authentication is the primary and mandatory process for any Information and Communication Technology (ICT) application to prove the legitimacy of the genuine user. It becomes more important and crucial for public platforms like e-governance platforms. The Government of India is transforming the country into Digital India through various e-governance initiatives based on ICT. For authentication, National e-Authentication Framework (NeAF) was proposed by the Indian government which is a policy framework for authentication. This framework does not provide any technical and unified solution for authentication systems while it is based on centralized verification data. In this paper, we proposed a solution for the authentication which provides the unified authentication solution for the Indian e-governance system with existing infrastructure. This solution also provides the features such as scalability, security, and transparency based on distributed computing and working on multiserver architecture. This solution also fulfills the need of the current Indian government to provide multiple e-governance services through a single smart card. [ABSTRACT FROM AUTHOR]

ABSTRACT By offering indisputable advantages over traditional grid including remote readings and load balancing, smart grid is regarded as the modernization of the electricity delivery system. In this paper, we propose a privacy-preserving and secure multi-dimensional aggregation scheme for smart grid communications in digital communities. By integrating privacy homomorphism encryption with aggregation signature scheme, data authentication and integrity protection are performed and proved without disclosing any fine-grained user consumption data. The construction of member list allows the fault tolerance of our scheme against accidental errors. In addition, the batch verification technique is adopted to reduce the computation cost of the operation center, and each user's computation cost is independent of the number of collected data types. Security analysis and performance evaluation demonstrate that the proposed scheme can resist various security threats and preserve identity privacy and has significantly less communication overhead and computation cost than other existing approaches. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The performance and economics of grid-connected photovoltaic (PV) systems are affected by the array spacing. Increasing the array spacing implies reducing the impact of shading, but at the same time, it increases the land purchase/preparation costs and the wiring costs. A number of technical and economic factors are involved when selecting an optimum array spacing. Designers of PV plants often set the row-to-row spacing based on simplified rules, losing the opportunity of improving the profitability of their projects. In this paper, a comprehensive methodology for optimizing the array spacing is proposed. It is based on annual shading energy calculations and incorporates a PV energy yield model together with an economic model focused on investment costs. The method is applied to the climatic conditions in Aguascalientes, Mexico, as a case study. A sensitivity analysis allowed the impact of the technical and economic parameters involved on the optimum interrow distance to be quantified. According to the results, the most relevant technical parameters are the module tilt (often considered by the PV designers), the ratio of plant width to plant length, and the module efficiency. The main economic parameters are the land-related costs and the costs per kWp. The comparison of this methodology to a conventional rule based on the winter solstice condition shows differences in the array spacing for the same location when the multiple technical and economic parameters are considered. Therefore, the proposed method will be useful for PV designers to improve the energetic and economic behavior of their systems. [ABSTRACT FROM AUTHOR]

As the cloud data centers size increases, the number of virtual machines (VMs) grows speedily. Application requests are served by VMs be located in the physical machine (PM). The rapid growth of Internet services has created an imbalance of network resources. Some hosts have high bandwidth usage and can cause network congestion. Network congestion affects overall network performance. Cloud computing load balancing is an important feature that needs to be optimized. Therefore, this research proposes a 3-tier architecture, which consists of Cloud layer, Fog layer, and Consumer layer. The Cloud serves the world, and Fog analyzes the services at the local edge of network. Fog stores data temporarily, and the data is transmitted to the cloud. The world is classified into 6 regions on the basis of 6 continents in consumer layer. Consider Area 0 as North America, for which two fogs and two cluster buildings are considered. Microgrids (MG) are used to supply energy to consumers. In this research, a real-time VM migration algorithm for balancing fog load has been proposed. Load balancing algorithms focus on effective resource utilization, maximum throughput, and optimal response time. Compared to the closest data center (CDC), the real-time VM migration algorithm achieves 18% better cost results and optimized response time (ORT). Realtime VM migration and ORT increase response time by 11% compared to dynamic reconFigure with load (DRL) with load. Realtime VM migration always seeks the best solution to minimize cost and increase processing time. [ABSTRACT FROM AUTHOR]

The development and application of new energy have been paid more and more attention by governments of various countries since the 21st century. It is of practical engineering significance to study reactive power compensation of wind turbine distribution network. In view of the problems of poor grid quality and increased active power loss caused by a large amount of reactive power transmission during the operation of wind farms, based on the improved genetic algorithm, aiming at the minimum compensation capacity and the minimum voltage deviation of load nodes, a multiobjective optimization scheme of reactive power in wind farms is proposed, and an example is analyzed in Matlab. The results verify the effectiveness of the improved genetic algorithm in reactive power optimization of wind motor. [ABSTRACT FROM AUTHOR]

A computational fluid dynamics (CFDs) method utilizing unstructured grid technology has been employed to compute vortical flow around a 65° delta wing with sharp leading edge, which is specially known as the geometry of the second international vortex flow experiment (VFE-2). In VFE-2, 65° delta wings with different leading edges had been broadly investigated by experiments, which resulted in a special database for CFDs codes validation. The emphasis of this paper is to investigate the effectiveness of an adjoint-base grid adaptation method for unstructured grid in capturing concentrated vortices generated at sharp edges or flow separation lines of lifting surfaces flying at high angles of attack. Earlier experiences in vortical flow simulations had indicated that the vortex behavior is highly dependent on the local grid resolution both on body surface and space. The adjoint-based adaptation method used here is hoped to save grid points with a reasonable grid resolution for vortical flow simulations. The basic idea is to construct a new adaptive sensor in a grid adaptation process with the intent to tell where the elements should be smaller or larger by introducing an adjoint formulation to relate the estimated functional error to local residual errors of both the primal and adjoint solutions. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of supporting fault tolerance for adaptive and time-critical applications in heterogeneous and unreliable grid computing environments. Our goal for this class of applications is to optimize a user-specified benefit function while meeting the time deadline. Our first contribution in this paper is a multi-objective optimization algorithm for scheduling the application onto the most efficient and reliable resources. In this way, the processing can achieve the maximum benefit while also maximizing the success-rate, which is the probability of finishing execution without failures. However, for the cases where failures do occur, we have developed a hybrid failure recovery scheme to ensure that the application can complete within the pre-specified time interval. Our experimental results show that our scheduling algorithm can achieve better benefit when compared to several heuristics-based greedy scheduling algorithms, while still having a negligible overhead. Benefit is further improved when we apply the hybrid failure recovery scheme, and the success-rate becomes 100%. [ABSTRACT FROM AUTHOR]

Grid brings the power of many computers to scientists. However, the development of Grid-enabled applications requires knowledge about Grid infrastructure and low-level API to Grid services. In turn, workflow management systems provide a high-level environment for rapid prototyping of experimental computing systems. Coupling Grid and workflow paradigms is important for the scientific community: it makes the power of the Grid easily available to the end user. The paradigm of data driven workflow execution is one of the ways to enable distributed workflow on the Grid. The work presented in this paper is carried out in the context of the Virtual Laboratory for e-Science project. We present the VLAM-G workflow management system and its core component: the Run-Time System (RTS). The RTS is a dataflow driven workflow engine which utilizes Grid resources, hiding the complexity of the Grid from a scientist. Special attention is paid to the concept of dataflow and direct data streaming between distributed workflow components. We present the architecture and components of the RTS, describe the features of VLAM-G workflow execution, and evaluate the system by performance measurements and a real life use case. [ABSTRACT FROM AUTHOR]

At present, the explosive growth of data and the mass storage state have brought many problems such as computational complexity and insufficient computational power to clustering research. The distributed computing platform through load balancing dynamically configures a large number of virtual computing resources, effectively breaking through the bottleneck of time and energy consumption, and embodies its unique advantages in massive data mining. This paper studies the parallel k-means extensively. This article first initializes random sampling and second parallelizes the distance calculation process that provides independence between the data objects to perform cluster analysis in parallel. After the parallel processing of the MapReduce, we use many nodes to calculate distance, which speeds up the efficiency of the algorithm. Finally, the clustering of data objects is parallelized. Results show that our method can provide services efficiently and stably and have good convergence. [ABSTRACT FROM AUTHOR]

Background. To accurately evaluate sports social discipline's academic influence in China, a model of academic influence evaluation of sports social discipline in China based on bibliometrics is proposed. Objective. A statistical model of the academic influence of sports social discipline is constructed, the word frequency statistics method is used to measure the literature, and the semantic characteristic quantity of the sports social discipline academic influence is extracted, using the literature method and logical analysis method to analyze sports' social value in the Internet era from healthy sports. The combination of sports and medical treatment can comprehensively promote physical and mental health. Methods. The dual semantic feature decomposition method is used to analyze sports social discipline's academic influence. The statistical analysis model of sports social discipline academic influence is established. The principles of fuzzy pattern recognition include the principle of maximum membership degree and proximity degree. Results. The comprehensive relative closeness of the distribution of literature statistics on sports and social disciplines' academic influence is constructed. The linear programming analysis of literature statistics is carried out using the standardized grid computing method. Conclusions. The combination of association rule feature extraction and semantic feature extraction is used to realize the quantitative calculation of literature statistics and academic influence. The simulation results show that the statistical analysis of the academic influence of sports and social discipline by this method is accurate, and the level of confidence is high. [ABSTRACT FROM AUTHOR]

Collaborative and distributed visualization applications generally require access to high-performance and high-end resources that are both expensive to own and maintain. Often there are also a limited number of such resources available. The development of Grid infrastructure allows these resources to be shared by visualization service providers and users. However, effective use of such resources necessitate the development of mechanisms which take into account the particular constraints needed to satisfy particular application resource demands as some visualization applications may be dependent on requiring data to be stored and delivered in a timely manner. Therefore, these applications must adhere to stringent real-time constraints and Quality-of-Service (QoS) requirements. In this paper we proposed a novel approach to matching visualization service requests with service advertisements using QoS attributes. Five attributes—Response time, Availability, Execution price, Memory capacity, and Number of CPUs—are selected as the quality criteria for the visualization services, and for each one, we provide a definition, indicate its granularity, and provide rules to compute its value for a given service. The idea behind QoS-based matchmaking for visualization is an attempt to provide visualization service requesters with an approach to locate services, which have been selected by function properties, utilizing non-functional properties in order to get “good” quality services. The matchmaking approach proposed is concerned with computing the similarity between service requests and services based on above QoS attributes. The paper gives the equations of the similarities for Response time, Availability, Execution price, Memory capacity, Number of CPUs, and details the matching algorithm based on these equations for similarity. A prototype of the visualization service discovery system was developed, which is based on QoS and extends our previous prototype of the semantic portal for the discovery of the visualization service which provides multiple techniques to search available visualization services. But these techniques are all based on functional attributes, not on QoS ones. The enhanced system introduces the computation of QoS similarity and the capability of QoS feedback to users, which will be faster in finding qualified visualization Web Services. The paper discusses the prototype's architecture and how it works. The paper finally presents an experiment, the objective of which is to observe the performance of the search with QoS attributes. It can be seen from the experiment that the time for QoS matching is acceptable, and the search based on nonfunctional attributes allows the user to locate more appropriate visualization Web Services. [ABSTRACT FROM AUTHOR]

Grid computing facilitates the resource sharing through the administrative domains which are geographically distributed. Scheduling in a distributed heterogeneous environment is intrinsically very hard because of the heterogeneous nature of resource collection. Makespan and tardiness are two different measures of scheduling, and many of the previous researches concentrated much on reduction of makespan, which measures the machine utilization. In this paper, we propose a hybrid scheduling algorithm for scheduling independent grid tasks with the objective of reducing total weighted tardiness of grid tasks. Tardiness is to measure the due date performance, which has a direct impact on cost for executing the jobs. In this paper we propose BG_ATC algorithm which is a combination of best gap (BG) search and Apparent Tardiness Cost (ATC) indexing algorithm. Furthermore, we implemented these two algorithms in two different phases of the scheduling process. In addition to that, the comparison was made on results with various benchmark algorithms and the experimental results show that our algorithm outperforms the benchmark algorithms. [ABSTRACT FROM AUTHOR]

Sensor grid is a platform that combines wireless sensor networks and grid computing with the aim of exploiting the complementary advantages of the two systems. Proper integration of these distinct systems into effective, logically single platform is challenging. This paper presents an approach for practical sensor grid implementation and management. The proposed approach uses a metamodeling technique and performance analysis and tuning as well as a middleware infrastructure that enable practical sensor grid implementation and management. The paper presents our implementation and analysis of the sensor grid. [ABSTRACT FROM AUTHOR]

The dynamic localization is a kind of technology by which the mobile robot tries to localize the position by itself. According to the dynamic localization failure of mobile robots in indoor network blind areas, an autonomous-dynamic localization system which dynamically chooses beacon node and establishes grids is proposed in this paper. This method applies received signal strength indication (RSSI) for distance measurement. Furthermore, the proposed grid-based improved maximum likelihood estimation (GIMLE) fulfills the localization. Finally, the localization error correction is implemented by Kalman filter. The approach combines the classical Kalman filter with the other localization algorithms. The purpose is to smooth and optimize the results of the algorithms, in order to improve the localization accuracy. In particular, in network blind spots, the Kalman filter provides better performance than the other algorithms listed in the paper. Experimental results show the accuracy, adaptivity, and robustness of the dynamic self-localization of mobile robots. [ABSTRACT FROM AUTHOR]

This paper attempts at developing simple, efficient, and fast converging load flow analysis techniques tailored to autonomous microgrids. Two modified backward forward sweep techniques have been developed in this work where the largest generator is chosen as slack generator, in the first method and all generator buses are modeled as slack buses in the second method. The second method incorporates the concept of distributed slack bus to update the real and reactive power generations in the microgrid. This paper has details on the development of these two methodologies and the efficacy of these methods is compared with the conventional Newton Raphson load flow method. The standard 33-bus distribution system has been transformed into an autonomous microgrid and used for evaluation of the proposed load flow methodologies. Matlab coding has been developed for validating the results. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate the short-term wind power forecasting. STWPF is a typically complex issue, because it is affected by many factors such as wind speed, wind direction, and humidity. This paper attempts to provide a reference strategy for STWPF and to solve the problems in existence. The two main contributions of this paper are as follows. (1) In data preprocessing, each encountered problem of employed real data such as irrelevant, outliers, missing value, and noisy data has been taken into account, the corresponding reasonable processing has been given, and the input variable selection and order estimation are investigated by Partial least squares technique. (2) STWPF is investigated by multiscale support vector regression (SVR) technique, and the parameters associated with SVR are optimized based on Grid-search method. In order to investigate the performance of proposed strategy, forecasting results comparison between two different forecasting models, multiscale SVR and multilayer perceptron neural network applied for power forecasts, are presented. In addition, the error evaluation demonstrates that the multiscale SVR is a robust, precise, and effective approach. [ABSTRACT FROM AUTHOR]

Bandwidth-aware workflow scheduling is required to improve the performance of a workflow application in a multisite Grid environment, as the data movement cost between two low-bandwidth sites can adversely affect the makespan of the application. Pegasus WMS, an open-source and freely available WMS, cannot fully utilize its workflow mapping capability due to unavailability of integration of any bandwidth monitoring infrastructure in it. This paper develops the integration of Network Weather Service (NWS) in Pegasus WMS to enable the bandwidth-aware mapping of scientific workflows. Our work demonstrates the applicability of the integration of NWS by making existing Heft site-selector of Pegasus WMS bandwidth aware. Furthermore, this paper proposes and implements a new workflow scheduling algorithm--Level based Highest Input and Processing Weight First. The results of the performed experiments indicate that the bandwidth-aware workflow scheduling algorithms perform better than bandwidth-unaware algorithms: Random and Heft of Pegasus WMS. Moreover, our proposed workflow scheduling algorithm performs better than the bandwidth-aware Heft algorithms. Thus, the proposed bandwidth-aware workflow scheduling enhances capability of Pegasus WMS and can increase performance of workflow applications. [ABSTRACT FROM AUTHOR]

The last years have been characterized by an increasing interest in the grid and cloud computing that allow the implementation of high performance computing structures in a distributed way by exploiting multiple processing resources. The presence of mobile terminals has extended the paradigm to the so called pervasive grid networks, where multiple heterogeneous devices are interconnected to form a distributed computing resource. In such a scenario, there is the need of efficient techniques for providing reliable wireless connections among network nodes. This paper deals with the proposal of a suitable resource management scheme relying on a routing algorithm able to perform jointly the resource discovery and task scheduling for implementing an efficient pervasive grid infrastructure in a wireless ad hoc scenario. The proposed solutions have been considered within two different parallelization processing schemes, and their effectiveness has been verified by resorting to computer simulations. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

For programming and executing complex applications on grid infrastructures, scientific workflows have been proposed as convenient high-level alternative to solutions based on general-purpose programming languages, APIs and scripts. GridSpace is a collaborative programming and execution environment, which is based on a scripting approach and it extends Ruby language with a high-level API for invoking operations on remote resources. In this paper we describe a tool which enables to convert the GridSpace application source code into a workflow representation which, in turn, may be used for scheduling, provenance, or visualization. We describe how we addressed the issues of analyzing Ruby source code, resolving variable and method dependencies, as well as building workflow representation. The solutions to these problems have been developed and they were evaluated by testing them on complex grid application workflows such as CyberShake, Epigenomics and Montage. Evaluation is enriched by representing typical workflow control flow patterns. [ABSTRACT FROM AUTHOR]

We improved the computational grid and schemes in the VOF (volume of fluid) method with the standard k - ε turbulent model in our previous study to evaluate CCFL (countercurrent flow limitation) characteristics in a full-scale PWR hot leg (750mm diameter), and the calculated CCFL characteristics agreed well with the UPTF data at 1.5MPa. In this paper, therefore, to evaluate applicability of the VOF method to different fluid properties and a different scale, we did numerical simulations for full-scale airwater conditions and the 1/15-scale air-water tests (50mm diameter), respectively. The results calculated for full-scale conditions agreed well with CCFL data and showed that CCFL characteristics in theWallis diagram were mitigated under 1.5MPa steam-water conditions comparing with air-water flows. However, the results calculated for the 1/15-scale air-water tests greatly underestimated the falling water flow rates in calculations with the standard k - ε turbulent model, but agreed well with the CCFL data in calculations with a laminar flow model. This indicated that suitable calculation models and conditions should be selected to get good agreement with data for each scale. [ABSTRACT FROM AUTHOR]

The newer context-aware applications require many inputs and amongst them the location information is one of the most important. In the near future, we see the potential in using wireless sensors deployed inside buildings to support in generating the location information besides their other routine tasks. This paper records the efforts involved in designing and prototyping a centralized indoor positioning system for tracking a target's position. We present an in-depth discussion on the RSSI based positioning algorithms both, range-based and range-free. Considering that the signal strength can be distorted heavily due to multi-path and shadowing, we have proposed the weighing schemes to leverage the credibility of the measured RSSI values. For online tracking, we have also proposed boundary selection and local grid scanning to lower the searching time, and the RSSI data dissemination and collection schemes to reduce traffic overhead. Evaluations have been done in both the field measurements and with an RSSI generator. The generator has been developed to simulate and replace the real measurements for the ease of algorithm design and testing, thus avoiding repetitive field measurements. The results show that positioning systems with adequate accuracy can be built with our proposed schemes. We expect that these proposed schemes to be integrated in multitude of systems with context-aware applications, which use location information. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Spatial cluster analysis is an important data-mining task. Typical techniques include CLARANS, density- and gravity-based clustering, and other algorithms based on traditional von Neumann's computing architecture. The purpose of this paper is to propose a technique for spatial cluster analysis based on DNA computing and a grid technique. We will adopt the Adleman-Liptonmodel and then design a flexible grid algorithm. Examples are given to show the effect of the algorithm. The new clustering technique provides an alternative for traditional cluster analysis. [ABSTRACT FROM AUTHOR]

The Hamiltonian path problem for general grid graphs is known to be NP-complete. In this paper, we give necessary and sufficient conditions for the existence of Hamiltonian paths in L-alphabet, C-alphabet, F-alphabet, and E-alphabet grid graphs. We also present linear-time algorithms for finding Hamiltonian paths in these graphs. [ABSTRACT FROM AUTHOR]

The aim of this paper is to discuss efficient adaptive parallel solution techniques on unstructured 2D and 3D meshes. We concentrate on the aspect of parallel a posteriori mesh adaptation. One of the main advantages of unstructured grids is their capability to adapt dynamically by localised refinement and derefinement during the calculation to enhance the solution accuracy and to optimise the computational time. Grid adaption also involves optimisation of the grid quality, which will be described here for both structural and geometrical optimisation. [ABSTRACT FROM AUTHOR]

Today, Cloud computing proposes an attractive alternative to building large-scale distributed computing environments by which resources are no longer hosted by the scientists' computational facilities, but leased from specialised data centres only when and for how long they are needed. This new class of Cloud resources raises new interesting research questions in the fields of resource management, scheduling, fault tolerance, or quality of service, requiring hundreds to thousands of experiments for finding valid solutions. To enable such research, a scalable simulation framework is typically required for early prototyping, extensive testing and validation of results before the real deployment is performed. The scope of this paper is twofold. In the first part we present GroudSim, a Grid and Cloud simulation toolkit for scientific computing based on a scalable simulation-independent discrete-event engine. GroudSim provides a comprehensive set of features for complex simulation scenarios from simple job executions on leased computing resources to file transfers, calculation of costs and background load on resources. Simulations can be parameterised and are easily extendable by probability distribution packages for failures which normally occur in complex distributed environments. Experimental results demonstrate the improved scalability of GroudSim compared to a related process-based simulation approach. In the second part, we show the use of the GroudSim simulator to analyse the problem of dynamic provisioning of Cloud resources to scientific workflows that do not benefit from sufficient Grid resources as required by their computational demands. We propose and study four strategies for provisioning and releasing Cloud resources that take into account the general leasing model encountered in today's commercial Cloud environments based on resource bulks, fuzzy descriptions and hourly payment intervals. We study the impact of our techniques to the overall execution time, overall cost and cost per unit of saved time with respect to various instance types offered by the Amazon EC2. [ABSTRACT FROM AUTHOR]

The development of grid and workflow technologies has enabled complex, loosely coupled scientific applications to be executed on distributed resources. Many of these applications consist of large numbers of short-duration tasks whose runtimes are heavily influenced by delays in the execution environment. Such applications often perform poorly on the grid because of the large scheduling overheads commonly found in grids. In this paper we present a provisioning system based on multi-level scheduling that improves workflow runtime by reducing scheduling overheads. The system reserves resources for the exclusive use of the application, and gives applications control over scheduling policies. We describe our experiences with the system when running a suite of real workflow-based applications including in astronomy, earthquake science, and genomics. Provisioning resources with Corral ahead of the workflow execution, reduced the runtime of the astronomy application by up to 78% (45% on average) and of a genome mapping application by an order of magnitude when compared to traditional methods. We also show how provisioning can benefit applications both on a small local cluster as well as a large-scale campus resource. [ABSTRACT FROM AUTHOR]

Computational grids assemble multisite and multiowner resources and represent the most promising solutions for processing distributed computationally intensive applications, each composed by a collection of communicating tasks. The execution of an application on a grid presumes three successive steps: the localization of the available resources together with their characteristics and status; the mapping which selects the resources that, during the estimated running time, better support this execution and, at last, the scheduling of the tasks. These operations are very difficult both because the availability and workload of grid resources change dynamically and because, in many cases, multisite mapping must be adopted to exploit all the possible benefits. As the mapping problem in parallel systems, already known as NP-complete, becomes even harder in distributed heterogeneous environments as in grids, evolutionary techniques can be adopted to find near-optimal solutions. In this paper an effective and efficient multisite mapping, based on a distributed Differential Evolution algorithm, is proposed. The aim is to minimize the time required to complete the execution of the application, selecting from among all the potential ones the solution which reduces the use of the grid resources. The proposed mapper is tested on different scenarios. [ABSTRACT FROM AUTHOR]

The grid vision of a single computing utility has yet to materialize: while many grids with thousands of processors each exist, most work in isolation. An important obstacle for the effective and efficient inter-operation of grids is the problem of resource selection. In this paper we propose a solution to this problem that combines the hierarchical and decentralized approaches for interconnecting grids. In our solution, a hierarchy of grid sites is augmented with peer-to-peer connections between sites under the same administrative control. To operate this architecture, we employ the key concept of delegated matchmaking, which temporarily binds resources from remote sites to the local environment. With trace-based simulations we evaluate our solution under various infrastructural and load conditions, and we show that it outperforms other approaches to inter-operating grids. Specifically, we show that delegated matchmaking achieves up to 60% more goodput and completes 26% more jobs than its best alternative. [ABSTRACT FROM AUTHOR]

The techniques of formal experimental design and analysis are powerful tools for scientists and engineers. However, these techniques are currently underused for experiments conducted with computer models. This has motivated the incorporation of experimental design functionality into the Nimrod tool chain. Nimrod has been extensively used for exploration of the response of models to their input parameters; the addition of experimental design tools will combine the efficiency of carefully designed experiments with the power of distributed execution. This paper describes the incorporation of one type of design, the fractional factorial design, and associated analysis tools, into the Nimrod framework. The result provides a convenient environment that automates the design of an experiment, the execution of the jobs on a computational grid and the return of results, and which assists in the interpretation of those results. Several case studies are included which demonstrate various aspects of this approach. [ABSTRACT FROM AUTHOR]

Scientific workflows are a means of conducting in silico experiments in modern computing infrastructures for e-Science, often built on top of Grids. Monitoring of Grid scientific workflows is essential not only for performance analysis but also to collect provenance data and gather feedback useful in future decisions, e.g., related to optimization of resource usage. In this paper, basic problems related to monitoring of Grid scientific workflows are discussed. Being highly distributed, loosely coupled in space and time, heterogeneous, and heavily using legacy codes, workflows are exceptionally challenging from the monitoring point of view. We propose a Grid monitoring architecture for scientific workflows. Monitoring data correlation problem is described and an algorithm for on-line distributed collection of monitoring data is proposed. We demonstrate a prototype implementation of the proposed workflow monitoring architecture, the GEMINI monitoring system, and its use for monitoring of a real-life scientific workflow. [ABSTRACT FROM AUTHOR]

The over-arching aim of Grid computing is to move computational resources from individual institutions where they can only be used for in-house work, to a more open vision of vast online ubiquitous `virtual computational' resources which support individuals and collaborative projects. A major step towards realizing this vision is the provision of instrumentation – such as telescopes, accelerators or electrical power stations – as Grid resources, and the tools to manage these resources online. The GRIDCC project attempts to satisfy these requirements by providing the following four co-dependent components; a flexible wrapper for publishing instruments as Grid resources; workflow support for the orchestration of multiple Grid resources in a timely manner; the machinery to make reservation agreements on Grid resources; and the facility to satisfy quality of service (QoS) requirements on elements within workflows. In this paper we detail the set of services developed as part of the GRIDCC project to provide the last three of these components. We provide a detailed architecture for these services along with experimental results from load testing experiments. These services are currently deployed as a test-bed at a number of institutions across Europe, and are poised to provide a 'virtual lab' to production level applications. [ABSTRACT FROM AUTHOR]

The Computational Grid is a promising platform for the efficient execution of parameter sweep applicationsGrid middleware that embeds a scheduler for performance and targets Grid resources transparently. In this paper we describe a user-level Grid middleware project, the AppLeS Parameter Sweep Template (APST), that uses application-level scheduling techniques [1] and various Grid technologies to allow the efficient deployment of parameter sweep applications over the Grid. We discuss several possible scheduling algorithms and detail our software design. We then describe our current implementation of APST using systems like Globus [2], NetSolve [3] and the Network Weather Service [4], and present experimental results. [ABSTRACT FROM AUTHOR]

Active power filter (APF) is the most popular device in regulating power quality issues. Currently, most literatures ignored the impact of grid impedance and assumed the load voltage is ideal, which had not described the system accurately. In addition, the controllers applied PI control; thus it is hard to improve the compensation quality. This paper establishes a precise model which consists of APF, load, and grid impedance. The Bode diagram of traditional simplified model is obviously different with complete model, which means the descriptions of the system based on the traditional simplified model are inaccurate and incomplete. And then design exact feedback linearization and quasi-sliding mode control (FBL-QSMC) is based on precise model in inner current loop. The system performances in different parameters are analyzed and dynamic performance of proposed algorithm is compared with traditional PI control algorithm. At last, simulations are taken in three cases to verify the performance of proposed control algorithm. The results proved that the proposed feedback linearization and quasi-sliding mode control algorithm has fast response and robustness; the compensation performance is superior to PI control obviously, which also means the complete modeling and proposed control algorithm are correct. [ABSTRACT FROM AUTHOR]

There is increased worldwide wind power generation, a large percentage of which is grid connected. The doubly fed induction generator (DFIG) wind energy conversion system (WECS) has many merits and, as a result, large numbers have been installed to date. The DFIG WECS operation, under both steady state and fault conditions, is of great interest since it impacts on grid performance. This review paper presents a condensed look at the various applied solutions to the challenges of the DFIG WECS including maximum power point tracking, common mode voltages, subsynchronous resonance, losses, modulation, power quality, and faults both internal and from the grid. It also looks at approaches used to meet the increasingly stringent grid codes requirements for the DFIG WECS to not only ride through faults but also provide voltage support. These are aspects of the DFIG WECS that are critical for system operators and prospective investors and can also serve as an introduction for new entrants into this area of study. [ABSTRACT FROM AUTHOR]