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This specialist meeting (Dec. 2-4, 2004 in Santa Barbara, CA) focused on four sub-themes, each of which presents significant issues for interoperability and spatial webs:• Syntactic interoperability, and the adequacy of current metadata standards;• Semantic interoperability, and technologies for overcoming differences of meaning;• Accuracy, and the ability of data sets of different accuracy to interoperate; and• Spatial support, and technologies for re-sampling and interpolation.The report summarizes discussions among expert participants, and provides access to presentations and position papers.

Grid computing provides high computing power, enormous data storage, and collaboration possibilities to its users. Grid computing is currently in the midst of evolving standards, inheriting and customizing from those developed in the high performance, distributed, and recently from the Web services community. Grid computing is widely regarded as a technology of immense potential in both industry and academia. Which this interest, security become necessery to provide authentication, authorization, resource protection, secure communication, data integrity, trust management and network security. This paper tells about progression of the security system on grid computing by conducting a survey of existing paper and applications have been developed.

Internet of Things (IoT) powered by high level synthesis (HLS) provides huge opportunity of progress in the area of hardware design. However, the present era of hardware design involves globalization which poses security threat to the design integrators that rely on third party intellectual property (IP) cores for increasing design productivity at reduced design time. This paper presents for the first time in the literature, a low cost optimized hardware Trojan secured HLS approach for hardware (or application specific core) designs that is based on single or nested loop control data flow graphs (CDFG) applications. The paper presents multiple novel vendor allocation schemes (each with its own attribute of delay and area) as security constraint that yield Trojan secured schedules at behavioral level. Demonstration of the proposed approach on a nested loop case study asserts our proposed theory. Results on standard benchmarks indicate significant reduction in final solution cost compared to a similar approach. [ABSTRACT FROM AUTHOR]

The paper examines quality and quantity of scientific research in India as reflected in publications output on grid computing on a series of bibliometric indicators. Data were extracted from Scopus database consisting of 1340 publications in grid computing research from India in 10 years during 2008-17 citation impact of 4.33 citations per paper and international collaborative publication share of 12.16%. The paper profiles global publication output and share of 10 most productive countries in grid computing research, 15 most productive Indian organizations and 15 most productive authors on a series of indicators including publications output, number of citations, the relative citation index, citations per paper, h-index and share of international collaborative papers during 2008-17. Computer science, among top 3 subjects, accounted for the largest publication share (90.15%), followed by engineering (26.79%) and mathematics (13.66%). The 15 most leading organizations and authors together contributed 31.57% and 14.25% as their share of Indian publication output and 44.93% and 16.17% as their share of Indian citation output respectively during 2008-17. The most productive Indian organizations were Anna University, Chennai (74 papers) and Thapar University, Patiala (48 papers) The most productive authors were I. Chana of Thapar University, Patiala (21 papers) and N. Mukherjee of Jadavpur University, Kolkata (18 papers). The 15 most productive journals contributed 43.92% share to the Indian journal publication output during 2008-17. The most productive journals were Applied Engineering Research (16 publications), Journal of Grid Computing (14 publications) and Future Generation Computer Systems (13 publications). [ABSTRACT FROM AUTHOR]

Summary: Advances in computer networking, storage technologies, and high‐performance computing are helping global communities of researchers to address increasingly ambitious problems in Science collaboratively. E‐Science is the "science of this age"; it is realized through collaborative scientific enquiry which requires the utilization of non‐trivial amounts of computing resources and massive data sets. Core to this is the integrated set of technologies collectively known as e‐Infrastructures. In this paper, we explore the e‐Science and the e‐Infrastructure knowledge base through co‐citation analysis of existing literature. The dataset for this analysis is downloaded from the ISI Web of Science and includes over 12,000 articles. We identify prominent articles, authors, and articles with citation bursts. The detection of research clusters and the underlying seminal papers provide further insights. Our analysis is an important source of reference for academics, researchers, and students starting research in this field. [ABSTRACT FROM AUTHOR]

This research paper explores the innovative integration of green plant landscape design with advanced computer image processing in cloud, grid, and cluster computing environments to enhance urban air quality purification. The study begins by highlighting the critical need for improving air quality in urban areas, considering the rising levels of pollution and its impact on public health and the environment. The methodology involves the use of sophisticated image processing techniques to analyze various sensors on air quality measures and plant species their effectiveness in air purification, facilitated by the computational power of cloud, grid, and cluster computing. A diverse range of green plants was selected, and their air purification capabilities were assessed through a series of computer-simulated models. These models were developed using complex algorithms to predict the plants' performance in real-world urban settings. The research uniquely combines landscape architecture with technology, emphasizing the role of green spaces in urban areas for environmental sustainability. The results demonstrate that certain plant species are more effective than others in purifying urban air. The study provides a comprehensive ranking of these plants based on their purification capabilities, growth requirements, and suitability for various urban landscapes. The paper concludes by proposing practical guidelines for urban landscape designers and policymakers, recommending the strategic incorporation of specific green plants in urban areas to maximize air purification. Additionally, it highlights the potential of leveraging advanced computing technologies in environmental research and urban planning. This research contributes to the fields of environmental science, urban planning, and computer science by showcasing how multidisciplinary approaches can address pressing environmental issues. It opens avenues for further research in the optimization of urban green spaces using advanced computing techniques. The results demonstrate that certain plant species are more effective than others in purifying urban air. The study provides a comprehensive ranking of these plants based on their purification capabilities, growth requirements, and suitability for various urban landscapes. The paper concludes by proposing practical guidelines for urban landscape designers and policymakers, recommending the strategic incorporation of specific green plants in urban areas to maximize air purification. [ABSTRACT FROM AUTHOR]

Hundreds of papers on job scheduling for distributed systems are published every year and it becomes increasingly difficult to classify them. Our analysis revealed that half of these papers are barely cited. This paper presents a general taxonomy for scheduling problems and solutions in distributed systems. This taxonomy was used to classify and make publicly available the classification of $109$ scheduling problems and their solutions. These $109$ problems were further clustered into ten groups based on the features of the taxonomy. The proposed taxonomy will facilitate researchers to build on prior art, increase new research visibility, and minimize redundant effort. [ABSTRACT FROM PUBLISHER]

Computational biology allows and encourages the application of many different parallelism-based technologies. This special issue brings together high-quality state-of-the-art contributions about parallelism-based technologies in computational biology, from different points of view or perspectives, that is, from diverse high-performance computing applications. The special issue collects considerably extended and improved versions of the best papers, accepted and presented in PBio 2015 (the Third International Workshop on Parallelism in Bioinformatics, and part of IEEE ISPA 2015). The domains and topics covered in these seven papers are timely and important, and the authors have done an excellent job of presenting the material. [ABSTRACT FROM AUTHOR]

The ICTs industry is witnessing the rise of a groundbreaking technology known as cloud computing. This innovation is built upon the Internet, offering a virtual infrastructure for computers. This virtual setup encompasses databases, servers, storage, software, networking, processing power, office applications, and artificial intelligence. What sets cloud computing apart is its capacity to deliver services without the need for physical proximity to the underlying computer hardware. This approach brings with it numerous advantages over traditional computing methods, including grid computing. Also Cloud Computing introducing new challenges for environmental protection. This paper offers an extensive survey, overview and analysis of the fundamental concepts, historical evolution, components of cloud computing, diverse cloud services and categories, cloud integration, the merits of this technology, its focal areas, security considerations, and the implications of adopting cloud computing. This comprehensive analysis is based on the examination of more than 26 research papers and articles. The findings of this survey shed light on the transformation that the IT industry undergoes before and after embracing cloud computing. [ABSTRACT FROM AUTHOR]

The field of Supply Chain Management (SCM) is experiencing rapid strides in the use of Industry 4.0 technologies and the conceptualization of new supply chain configurations for online retail, sustainable and green supply chains, and the Circular Economy. Thus, there is an increasing impetus to use simulation techniques such as discrete-event simulation, agent-based simulation, and hybrid simulation in the context of SCM. In conventional supply chain simulation, the underlying constituents of the system like manufacturing, distribution, retail, and logistics processes are often modelled and executed as a single model. Unlike this conventional approach, a distributed supply chain simulation (DSCS) enables the coordinated execution of simulation models using specialist software. To understand the current state-of-the-art of DSCS, this paper presents a methodological review and categorization of literature in DSCS using a framework-based approach. Through a study of over 130 articles, we report on the motivation for using DSCS, the modelling techniques, the underlying distributed computing technologies and middleware, its advantages and a future agenda, and also limitations and trade-offs that may be associated with this approach. The increasing adoption of technologies like Internet-of-Things and Cloud Computing will ensure the availability of both data and models for distributed decision-making, which is likely to enable data-driven DSCS of the future. This review aims to inform organizational stakeholders, simulation researchers and practitioners, distributed systems developers and software vendors, as to the current state-of-the art of DSCS, and which will inform the development of future DSCS using new applied computing approaches. [ABSTRACT FROM AUTHOR]

The article focuses on TopoSUB, a tool use for numerical modeling in composite topography at sub-grid levels. It says that the functions of TopoSUB are shown to estimate the results generated by a distributed numerical land surface model (LSM). It cites various features of the tool that limits its functions such as the lateral mass transfer and flow modeling. It adds that the tool may also be used along with grid-computing infrastructure.

A large amount of high-value data are stored in smart grid data centers, but since the resource diversity of a single data center is limited, data sharing becomes especially important in conducting an effective data mining process. However, traditional data-sharing models often use centralized schemes without authentication of the shared objects, making it difficult to establish trust relationships and ensure data privacy. This makes it difficult to break through the problem of data islands. To solve the above problems, this paper proposes a blockchain-based data-sharing incentive model for edge smart grid scenarios. First, the model uses blockchain and proxy re-encryption technology to achieve the security and traceability of the smart grid data-sharing process. Secondly, the data-sharing incentive algorithm is designed using game theory to maximize the willingness of data owners to share data. Finally, the model in this paper is compared and analyzed with other existing data-sharing models, and the designed performance test shows that the scheme in this paper has significant advantages over the other literature schemes in terms of functionality and computational overhead, and the increase in costs is not significant, and the model can meet the requirements for large-scale data sharing in edge smart grid scenarios. [ABSTRACT FROM AUTHOR]

Proceedings of the 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT 2019); December 5, 2019:166-172; IEEE. Parallel and distributed computing has been the basis to many emerging areas, such as smart networks, cloud computing, big data analysis, and blockchain technology. We hope that this special issue provides valuable information and references for pursuit of research in the field of parallel and distributed computing and its relevant areas. [Extracted from the article]

The article presents an overview of papers that appeared in the November 2003 issue of the journal "The International Journal of High Performance Computer Applications." The papers included are from the Third International Workshop on Grid Computing. The first paper by researcher Craig A. Lee investigates distributed query processing on the Grid using a prototype query language and a bioinformatics case study. The next paper by researcher Jim Smith et al looks at the throughput behavior for Monte Carlo simulations on the Grid. By contrast, the paper by researcher Marcio Faerman et al. looks at guided searches through parameter spaces. The paper by researcher William H. Bell et al. evaluates data replication algorithms by simulating the EU DataGrid TestBed 1 sites including their network topology. Coupling existing, parallel numerical codes on the Grid is an issue addressed by researcher Christian Péerez et al. The paper by researcher Gregor von Laszewski et al. investigates computational thermo-chemistry where enthalpy diagrams and thermochemical reaction tables are used to manage the associated computations through a Grid framework. Last, but not least, researcher Gabrielle Allen et al. report on GridLab and the Grid Application Toolkit that will define a set of generic, high-level APIs for building Grid applications.

With the rapid construction and implementation of smart grid, lots of studies have been conducted to explore how to ensure the security of information privacy. At present, most privacy-preserving data aggregation schemes in smart grid achieve privacy data protection through homomorphically encrypted data aggregation. However, these data aggregation schemes tend to rely on a trusted third party (TTP), and fail to efficiently handle the case of a meter failure. Besides, they are less flexible for overall user management, and resistance to collusion attacks needs to be improved. In this paper, we propose an efficient and robust privacy-preserving data aggregation scheme without TTP, called EFTA. Overall, the scheme eliminates the reliance on a TTP, combines with Shamir threshold secret sharing scheme to increase overall fault tolerance, supports flexible and dynamic user management, and effectively defends against entity initiated collusion attacks. According to security and performance analysis results, the scheme proposed in this paper meets the multiple security requirements of smart grid, and is more efficient in terms of overall overhead compared to the existing privacy-preserving data aggregation schemes. [ABSTRACT FROM AUTHOR]

Under the envisioned smart grid paradigm, there is an increasing demand for a fast, accurate, and efficient power flow solution for distribution system operation and control. Various solution techniques have been proposed, each with its own unique formulation, solution methodology, advantages, and drawbacks. Motivated by challenges associated with the integration of renewable distributed energy resources and electric vehicles into distribution systems and further by the speed and convergence limitations of existing tools, this paper presents a novel graph‐based power flow solution for smart grid's real‐time operation and control, named Flow‐AugmentationPF algorithm. The proposed method formulates a power flow problem as a network‐flow problem and solves it by using a maximum‐flow algorithm, inspired by the push‐relabel max‐flow technique. The performance of the proposed algorithm is tested and validated using several benchmark networks of different sizes, topologies, and parameters and compared against the most commonly used solution techniques and commercial software packages, namely PSS/E and PSCAD. The proposed formulation is simple, accurate, fast, yet computationally efficient, as it is based on matrix‐vector multiplication, and is also scalable, considering the formulation works as a graph‐based method, which, inherently, allows for parallel computation for added computational speed. [ABSTRACT FROM AUTHOR]

We consider two finite and disjoint sets of homogeneous robots deployed at the nodes of an infinite grid graph. The grid graph also comprises two finite and disjoint sets of prefixed meeting nodes located over the nodes of the grid. The objective of our study is to design a distributed algorithm that gathers all the robots belonging to the first team at one of the meeting nodes belonging to the first type, and all the robots in the second team must gather at one of the meeting nodes belonging to the second type. The robots can distinguish between the two types of meeting nodes. However, a robot cannot identify its team members. This paper assumes the strongest adversarial model, namely the asynchronous scheduler. We have characterized all the initial configurations for which the gathering problem is unsolvable. For the remaining initial configurations, the paper proposes a distributed gathering algorithm. Assuming the robots are capable of global-weak multiplicity detection , the proposed algorithm solves the problem within a finite time period. The algorithm runs in |$\Theta (dn)$| moves and |$O(dn)$| epochs, where |$d$| is the diameter of the minimum enclosing rectangle of all the robots and meeting nodes in the initial configuration, and |$n$| is the total number of robots in the system. [ABSTRACT FROM AUTHOR]

We consider the problem of finding a "treasure" at an unknown point of an n-dimensional infinite grid, n ≥ 3 , by initially collocated finite automaton (FA) agents. Recently, the problem has been well characterized for 2 dimensions for deterministic as well as randomized FA agents, both in synchronous and semi-synchronous models (Brandt et al. in Proceedings of 32nd International Symposium on Distributed Computing (DISC) LIPCS 121:13:1–13:17, 2018; Emek et al. in Theor Comput Sci 608:255–267, 2015). It has been conjectured that n + 1 randomized FA agents are necessary to solve this problem in the n-dimensional grid (Cohen et al. in Proceedings of the 28th SODA, SODA '17, pp 207–224, 2017). In this paper we disprove the conjecture in a strong sense: we show that three randomized synchronous FA agents suffice to explore an n-dimensional grid for anyn. Our algorithm is optimal in terms of the number of the agents. Our key insight is that a constant number of FA agents can, by their positions and movements, implement a stack, which can store the path being explored. We also show how to implement our algorithm using: four randomized semi-synchronous FA agents; four deterministic synchronous FA agents; or five deterministic semi-synchronous FA agents. We give a different, no-stack algorithm that uses 4 deterministic semi-synchronous FA agents for the 3-dimensional grid. This is provably optimal in the number of agents and the exploration cost, and surprisingly, matches the result for 2 dimensions. For n ≥ 4 , the time complexity of the stack-based algorithms mentioned above is exponential in distance D of the treasure from the starting point of the agents. We show that in the deterministic case, one additional finite automaton agent brings the time down to a polynomial. We also show that any algorithm using 3 synchronous deterministic FA agents in 3 dimensions must travel beyond Ω (D 3 / 2) from the origin. Finally, we show that all the above algorithms can be generalized to unoriented grids. More specifically, six deterministic semi-synchronous FA agents are sufficient to locate the treasure in an unoriented n-dimensional grid. [ABSTRACT FROM AUTHOR]

The original vision on what later became LifeWatch ERIC started about a quarter of a century ago in 1996. In those days, the promise of digital technologies entered biodiversity and ecosystem research. Not only by digitiing relevant information, but also with applications to process such data. While several (inter)national initiatives embarked on specific topics, there was also an idea that the upcoming view on grid computing provided attractive solutions for federated data sources, together with a strong computing capacity. This paper presents the history from conception to early actions, until actual preparations towards a research infrastructure on the European scale. [ABSTRACT FROM AUTHOR]

It is crucial to evaluate and anticipate stability under various conditions, as the ability to stabilize a smart grid (SG) is one of its key features for assessing the effectiveness of its design. Intelligent approaches to stability forecasting are necessary to mitigate inadvertent instability in SG design. This is particularly crucial with the expansion of residential and commercial infrastructures, along with the growing integration of renewable energies into these grids. Predicting the stability of SGs is currently a major challenge. The concept of an SG encompasses a broad range of emerging technologies in which artificial intelligence (AI) plays a crucial role and is increasingly being utilized in light of the limitations of conventional methods. It empowers informed decision-making and adaptable responses to fluctuations in customer energy needs, unexpected power outages, rapid changes in renewable energy generation, or any unforeseen crises within an SG system. In this paper, we propose a symmetric approach to enhance SG stability by integrating various machine learning (ML) and deep learning (DL) algorithms, where symmetry is observed in the balanced application of these diverse computational techniques to predict and ensure the grid's stability. These algorithms utilized a dataset containing the simulation results of the SG stability. The learning phase of these algorithms is based on imprecise and unreliable data. To overcome this limitation, the fusion of classifiers can be a powerful approach to modeling inaccurate and uncertain data, providing more robust and reliable predictions than individual classifiers. Voting and Dempster–Shafer (DS) methods, two commonly used techniques in ensemble learning, were employed and compared. The results show that the use of the fusion of distinct classifiers with voting theory achieves an accuracy of 99.8% and outperforms several other methods including the DS method. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method to ensure service integrity for distributed computation outsourcing in different contexts, such as volunteer computing, grid computing, MapReduce computing, and crowdsourcing. We propose replicating the computations among multiple workers and manipulating the input of the replicated computations by mixing or adding noise. The idea of manipulating the inputs of the replicated computations, which is introduced in this paper for the first time, prevents attacks made by cheater or malicious workers, even in the case of collusion among these workers. By mixing inputs, collusive cheater workers are prevented from easily detecting replication; thus, they do not return random or partial results as output. Adding noise to the inputs prevents cheater and malicious workers from detecting replication, even in the case of collusion among workers. Therefore, they do not return incorrect output. To evaluate the proposed method, we adopt a game-theoretic approach. We also provide a simulation environment to evaluate the overhead of the proposed method. We demonstrate that the proposed method can guarantee high accuracy with low overhead, even when malicious workers constitute the majority of the workers. For instance, the proposed method can ensure high accuracy with 61.27 percent overhead in the case of 60 percent malicious workers. [ABSTRACT FROM AUTHOR]

Recently, computational Grids have proven to be a good solution for processing large-scale, computation intensive problems. However, the heterogeneity, dynamics of resources and diversity of applications requirements have always been important factors affecting their performance. In response to these challenges, this work first builds a Grid job scheduling architecture that can dynamically monitor Grid computing center resources and make corresponding scheduling decisions. Second, a Grid job model is proposed to describe the application requirements. Third, this paper studies the characteristics of commercial interconnection networks used in Grids and forecast job transmission time. Fourth, this paper proposes an application-aware job scheduling mechanism (AJSM) that includes periodic scheduling flow and a heuristic application-aware deadline constraint job scheduling algorithm. The rigorous performance evaluation results clearly demonstrate that the proposed application-aware job scheduling mechanism can successful schedule more Grid jobs than the existing algorithms. For successful scheduled jobs, our proposed AJSM method is the best algorithm for job average processing time and makespan. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, there are many routing protocols proposed for data dissemination to mobile sinks and delivers the data to sink mobility has been considered as an excellent work to balance the nodes for the consumption of energy. For more benefits, the data dissemination to the mobile sink is a challenging task for the sensor nodes due to the active network topology pretentious by the sink motion. Nodes need to reconstruct their routes to the newest location of the sink for efficient data delivery which destabilizes the energy protection goal. A Virtual Grid-Based Efficient Data Dissemination (VGEDD) protocol is presented in this paper. It is a routing approach which minimizes the sensor node cost of reconstruction of routes. This approach maintains the best route nearby to the sink node's latest location. In this paper, a set of rules are proposed for communication that manages the routes reform method, thus it needs only a limited number of nodes to rearrange their data delivery routes. Performance analysis shows that Virtual Grid-Based Efficient Data Dissemination (VGEDD) protocol demonstrate improved network lifetime and reduced routes reform cost when compared with existing method. In this paper, we also analyse the developing techniques for lifetime improvement of mobile sink in Wireless Sensor Networks (WSN). [ABSTRACT FROM AUTHOR]

In order to process the big set of product and process data and to extract the necessary knowledge from the intermediate set of new data, it is necessary to employ the modern computing facilities, such as Artificial Intelligence (AI) schemes, knowledge discovery, machine-learning procedures and deep-learning procedures. In the current digital era, the data captured during a product development process will grow from a large set of existing data sources to a new set of intermediate big data in every stream from product requirements, to conceptual design into manusfctutring details. Big Data Analytics b • Data Stream Algorithms • Models of Computation for Massive Data • Data Mining Challenges with Big Data • Techniques for Data Visualization • HDFS (Hadoop Distributed File System) • Big Data Analytics and Big Data governance • Big data Analytics and Applications • Data, Text, Web Mining, & Visualization • Data & Knowledge Engineering B 3. [Extracted from the article]

This paper is the guest editors message to the Special Issue on Parallel Evolutionary Algorithms on Grid and Cloud. Although this special issue includes extended version of selected papers presented at the last three EvoPar Conferences, the reviewing and selection process was made under a strict peer reviewing process and after a call for papers open for submission from out of the conference. We briefly introduce the accepted papers in this message. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue concerning grid computing, including one pertaining to the problem of the data sources discovery and selection, and another on the management of data in Network Enabled Servers environment.

One benefit of a computational Grid is the ability to run high-performance applications over distributed resources simply and securely. We demonstrated this benefit with an experiment in which we studied the protein-folding process with the CHARMM molecular simulation package over a Grid managed by Legion, a Grid operating system. High-performance applications can take advantage of Grid resources if the Grid operating system provides both low-level functionality as well as high-level services. We describe the nature of services provided by Legion for high-performance applications. Our experiences indicate that human factors continue to play a crucial role in the configuration of Grid resources, underlying resources can be problematic, Grid services must tolerate underlying problems or inform the user, and high-level services must continue to evolve to meet user requirements. Our experiment not only helped a scientist perform an important study, but also showed the viability of an integrated approach such as Legion's for managing a Grid. Copyright © 2004 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

With the continuous expansion of the power grid, the forms of faults are becoming increasingly complex, with a wide range of impacts and long maintenance cycles, posing increasingly severe challenges for power grid operators. In the current power system (PS), due to system limitations, there is a large amount of data, and the current computing system is limited by hardware and computing power, making it difficult to satisfy the requirements of real-time computing, comprehensive analysis, and expansion. This paper analyzed the technical basis of the knowledge graph (KG) of power grid operation and maintenance (O&M), and clarified the key role of model driving in the current large-scale smart grid (SG), which can effectively improve the efficiency and reliability of data processing in the SG. This paper also analyzed the model-driven PG O&M KG platform, and analyzed the role of particle swarm optimization (PSO) algorithm in PG operation and fault maintenance. After applying the PSO algorithm in this article, in the experimental results section, the fault rate of Transformer 5 was 2.1% lower than that of Transformer 4. The knowledge mapping model of grid O&M and the particle swarm algorithm in this paper can significantly reduce the failure rate of the grid, which has wide extension value. [ABSTRACT FROM AUTHOR]

The optimal utilization of assets for cloud/grid resources is a strategy that may be employed to increase the profit of service providers. In this paper, we empirically investigate the Continuous Double Auction (CDA), the Stable Continuous Double Auction (SCDA), and the Threshold Price Double Auction (TPDA) strategies, as well as the Preston McAfee Double Auction (PMDA) strategies to demonstrate how the use of grid/cloud computing might have a highly positive impact on the allocation of computing resources. The CDA, SCDA, PMDA, and TPDA strategies are analyzed in terms of their suitability for their end-users while taking into account; prospective resources, deadline consumption, budget spending, resource-derived profits, and immediate resource allocation. According to the measured results, SCDA performs efficient budget spending, CDA is good in resource allocation, with TPDA and PMDA demonstrating the highest performance with respect to deadline consumption. [ABSTRACT FROM AUTHOR]

The paper surveys the state of the art of task scheduling in Desktop Grid computing systems. We describe the general architecture of a Desktop Grid system and the computing model adopted by the BOINC middleware. We summarize research papers to bring together and examine the optimization criteria and methods proposed by researchers so far for improving Desktop Grid task scheduling (assigning tasks to computing nodes by a server). In addition, we review related papers, which address non-regular architectures, like hierarchical and peer-to-peer Desktop Grids, as well as Desktop Grids designed for solving interdependent tasks. [ABSTRACT FROM AUTHOR]

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 paper addresses the issue of placing a simple polygon (upon translation and rotation) on an isometric triangular grid such that the polygon contains the maximum number of triangles in its closure. This solves the problem left open in two recent papers titled “On the problem of the automated design of large-scale robot skin” and “An improved algorithm for the automated design of large scale robot skin” published in the IEEE Transactions on Automation Science and Engineering . Based on the properties of the grid, an improved algorithm is also presented. We also present some experimental results describing the use of this algorithm. [ABSTRACT FROM AUTHOR]

This paper discusses the load-balanced task allocation problem in the grid transaction processing system to maximize reliability. Finding the solution of optimal load-balanced task allocation is known to be an NP-hard. This paper presents a load-balanced task allocation based cuckoo search-ant colony optimization (LBTA_CSACO) method for this problem. The LBTA_CSACO is based on the cooperative behavior of cuckoo search and ant colony optimization to nd a collection of task allocation solutions. This paper also models the reliability of the system with the help of the availability of the system. The motive of this work is to maximize the reliability of the system. After the simulation, six existing algorithms are taken for the comparison of the results; Honey Bee Optimization (HBO), Ant Colony Optimization (ACO), Hierarchical Load Balanced Algorithm (HLBA), Dynamic and Decentralized Load Balancing (DLB), and Randomized respectively. [ABSTRACT FROM AUTHOR]

This paper studies the distributed linearly separable computation problem, which is a generalization of many existing distributed computing problems such as distributed gradient coding and distributed linear transform. A master asks ${\mathsf {N}}$ distributed workers to compute a linearly separable function of ${\mathsf {K}}$ datasets, which is a set of ${\mathsf {K}}_{\mathrm{ c}}$ linear combinations of ${\mathsf {K}}$ equal-length messages (each message is a function of one dataset). We assign some datasets to each worker in an uncoded manner, who then computes the corresponding messages and returns some function of these messages, such that from the answers of any ${\mathsf {N}}_{\mathrm{ r}}$ out of ${\mathsf {N}}$ workers the master can recover the task function with high probability. In the literature, the specific case where ${\mathsf {K}}_{\mathrm{ c}}=1$ or where the computation cost is minimum has been considered. In this paper, we focus on the general case (i.e., general ${\mathsf {K}}_{\mathrm{ c}} $ and general computation cost) and aim to find the minimum communication cost. We first propose a novel converse bound on the communication cost under the constraint of the popular cyclic assignment (widely considered in the literature), which assigns the datasets to the workers in a cyclic way. Motivated by the observation that existing strategies for distributed computing fall short of achieving the converse bound, we propose a novel distributed computing scheme for some system parameters. The proposed computing scheme is optimal for any assignment when ${\mathsf {K}}_{\mathrm{ c}}$ is large and is optimal under the cyclic assignment when the numbers of workers and datasets are equal or ${\mathsf {K}}_{\mathrm{ c}}$ is small. In addition, it is order optimal within a factor of 2 under the cyclic assignment for the remaining cases. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused significant harm globally, prompting us to prioritize prevention measures. Effective hand-washing is one of the most critical and straightforward measures that can help prevent the spread of this virus. Medical staff's hands are considered a major source of hospital infection. Effective hand-washing can prevent up to 30% of diarrhea-related illnesses, which is crucial in preventing nosocomial infections (Tartari et al. in Clin Microbiol Infect 23(9):596–598, 2017). This paper proposes an electronic-based real-time hand-washing identification framework called Alpha Hand Washing (ALPHA HW). The system uses camera-based identification, edge computing, and deep learning to automatically identify correct hand-washing behaviors, thereby facilitating effective hand-washing (Bertasius et al. in: Computer vision and pattern recognition, 2015). We achieved an accuracy of 78.0% mAP and a speed of 52 FPS in detecting scenes using specific monitoring datasets in hospitals by constructing the complex recognition system into a grid computing problem. Leveraging edge computing, our system achieves real-time identification with low memory consumption and high-efficiency computation. Alpha HW presents scientific and financial values in epidemic prevention and control that can facilitate popularization to reduce virus spread (Bewley et al. in 2016 IEEE international conference on image processing, 2016). [ABSTRACT FROM AUTHOR]

This paper is concerned with developing an efficient numerical algorithm for the fast implementation of the sparse grid method for computing the d-dimensional integral of a given function. The new algorithm, called the MDI-SG (multilevel dimension iteration sparse grid) method, implements the sparse grid method based on a dimension iteration/reduction procedure. It does not need to store the integration points, nor does it compute the function values independently at each integration point; instead, it reuses the computation for function evaluations as much as possible by performing the function evaluations at all integration points in a cluster and iteratively along coordinate directions. It is shown numerically that the computational complexity (in terms of CPU time) of the proposed MDI-SG method is of polynomial order O (d 3 N b) (b ≤ 2) or better, compared to the exponential order O (N (log N) d − 1) for the standard sparse grid method, where N denotes the maximum number of integration points in each coordinate direction. As a result, the proposed MDI-SG method effectively circumvents the curse of dimensionality suffered by the standard sparse grid method for high-dimensional numerical integration. [ABSTRACT FROM AUTHOR]

The electric vehicle (EV) industry is quickly growing in the present scenario, and will have more demand in the future. A sharp increase in the sales of EVs by 160% in 2021 represents 26% of new sales in the worldwide automotive market. EVs are deemed to be the transportation of the future, as they offer significant cost savings and reduce carbon emissions. However, their interactions with the power grid, charging stations, and households require new communication and control techniques. EVs show unprecedented behavior during vehicle battery charging, and sending the charge from the vehicle's battery back to the grid via a charging station during peak hours has an impact on the grid operation. Balancing the load during peak hours, i.e., managing the energy between the grid and vehicle, requires efficient communication protocols, standards, and computational technologies that are essential for improving the performance, efficiency, and security of vehicle-to-vehicle, vehicle-to-grid (V2G), and grid-to-vehicle (G2V) communication. Machine learning and deep learning technologies are being used to manage EV-charging station interactions, estimate the charging behavior, and to use EVs in the load balancing and stability control of smart grids. Internet of Things (IoT) technology can be used for managing EV charging stations and monitoring EV batteries. Recently, much work has been presented in the EV communication and control domain. In order to categorize these efforts in a meaningful manner and highlight their contributions to advancing EV migration, a thorough survey is required. This paper presents existing literature on emerging protocols, standards, communication technologies, and computational technologies for EVs. Frameworks, standards, architectures, and protocols proposed by various authors are discussed in the paper to serve the need of various researchers for implementing the applications in the EV domain. Security plays a vital role in EV authentication and billing activities. Hackers may exploit the hardware, such as sensors and other electronic systems and software of the EV, for various malicious activities. Various authors proposed standards and protocols for mitigating cyber-attacks on security aspects in the complex EV ecosystem. [ABSTRACT FROM AUTHOR]

In the process of crowdsensing, tasks allocation is an important part of the precise as well as the quality of feedback results. However, during this process, the applicants, the publisher and the authorized agency may be aware of the location of each other, and then threaten their privacy. Thus, in order to cope with the problem of privacy violation during the process of tasks allocation, in this paper, based on the basic idea of homomorphic encryption, an encrypted grids matching scheme is proposed (short for EGMS) to provide privacy preservation service for each entity that participates in the process of crowdsensing. In this scheme, the grids used for tasks allocation are encrypted firstly, so the process of task matching by applicants and publishers is also in an encrypted environment. Next, locations used for allocation as well as locations that applicants can provide services are secrets for each other, so that the location privacy of applicants and publishers can be preserved. Finally, applicants of task feedback results of each grid that they located in, and the publisher gets these results, and the whole process of crowdsensing is finished. In the last part of this paper, four types of security analysis are given to prove the security between applicants and the publisher. Then several groups of experimental verification that simulates the task allocation are used to test the security and efficiency of EGMS, and the results are compared with other similar schemes, so as to further demonstrate the superiority of our proposed scheme. [ABSTRACT FROM AUTHOR]

Grid computing technology is a technique to share various resources like memory, storage, processor that are idle, to solve very huge and complex problems. These resources are shared among various heterogeneous computers, which form a virtual organization. The computers involved in communication for sharing resources might be from the same domain or from different domains. The risk is more in communication among computers from different domains since they are unknown to each other and there are more chances to misuse the resources. To avoid the above mentioned problems, various trust models are prepared to assess the computers for their trustworthiness in communication. This paper proposes a trust model, which uses a regression model to predict the behavior of client and service provider for further communication in a more secure manner. This regression model is developed based on the data collected through a survey; also, a reliability test was conducted to check the consistency of collected data, which was found to be good. This paper also shows the usage of randomized algorithm for trust model in grid computing proposed by us, based on the various parameter values collected through the survey. [ABSTRACT FROM AUTHOR]

Due to the explosive increase in the production and sharing of digital visual media such as photos, animations, films and video clips, the need to intentionally or unintentionally create mosaic blocks to effectively cover user-designated regions within images has grown. In this paper, a method using boundary characteristics to effectively detect the grid-type mosaic blocks existing within the input image is proposed. Initially, the Canny edge is detected from the input image. Then, the boundary characteristics of mosaic blocks are extracted from the detected edges, and the candidate regions which may contain mosaic blocks are detected. After this stage, geometric characteristics are used to eliminate non-mosaic blocks and select actual mosaic blocks. In the experiment performed in this paper, it was observed that the method using boundary characteristics achieved more robust detection of the grid-type mosaic blocks from various input images than other existing methods. The mosaic-detection method proposed in this paper is expected to be valuable for applications in various fields. [ABSTRACT FROM AUTHOR]

Power distribution and battery thermal management are important technologies for improving the energy efficiency of plug-in hybrid electric vehicles (PHEVs). In response to the global optimization of integrated energy thermal management strategy (IETMS) for PHEVs, a dynamic programming algorithm based on adaptive grid optimization (AGO–DP) is proposed in this paper to improve optimization performance by reducing the optimization range of SOC and battery temperature, and adaptively adjusting the grid distribution of state variables according to the actual feasible region. The simulation results indicate that through AGO–DP optimization, the reduction ratio of the state feasible region is more than 30% under different driving conditions. Meanwhile, the algorithm can obtain better global optimal driving costs more rapidly and accurately than traditional dynamic programming algorithms (DP). The computation time is reduced by 33.29–84.67%, and the accuracy of the global optimal solution is improved by 0.94–16.85% compared to DP. The optimal control of the engine and air conditioning system is also more efficient and reasonable. Furthermore, AGO–DP is applied to explore IETMS energy-saving potential for PHEVs. It is found that the IETMS energy-saving potential range is 3.68–23.74% under various driving conditions, which increases the energy-saving potential by 0.55–3.26% compared to just doing the energy management. [ABSTRACT FROM AUTHOR]

Distributed e-Infrastructure is a key component of modern BIG Science. Service discovery in e-Science environments, such as Worldwide LHC Computing Grid (WLCG), is a crucial functionality that relies on service registry. In this paper we re-formulate the requirements for the service endpoint registry based on our more than 10 years experience with many systems designed or used within the WLCG e-Infrastructure. To satisfy those requirements the paper proposes a novel idea to use the existing well-established Domain Name System (DNS) infrastructure together with a suitable data model as a service endpoint registry. The presented ARC Hierarchical Endpoints Registry (ARCHERY) system consists of a minimalistic data model representing services and their endpoints within e-Infrastructures, a rendering of the data model embedded into DNS-records, a lightweight software layer for DNS-record management and client-side data discovery. Our approach for the ARCHERY registry required minimal software development and inherits all the benefits of one of the most reliable distributed information discovery source of the internet, the DNS infrastructure. In particular, deployment, management and operation of ARCHERY is fully relying on DNS. Results of ARCHERY deployment use-cases are provided together with performance analysis. [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]