Abstract: Abstract Allocating resources is crucial in large-scale distributed computing, as networks of computers tackle difficult optimization problems. Within the scope of this discussion, the objective of resource allocation is to achieve maximum overall computing efficiency or throughput. Cloud computing is not the same as grid computing, which is a version of distributed computing in which physically separate clusters are networked and made accessible to the public. Because of the wide variety of application workloads, allocating multiple virtualized information and communication technology resources within a cloud computing paradigm can be a problematic challenge. This research focused on the implementation of an application of the LSTM algorithm which provided an intuitive dynamic resource allocation system that analyses the heuristics application resource utilization to ascertain the best extra resource to provide for that application. The software solution was simulated in near real-time, and the resources allocated by the trained LSTM model. There was a discussion on the benefits of integrating these with dynamic routing algorithms, designed specifically for cloud data centre traffic. Both Long-Short Term Memory and Monte Carlo Tree Search have been investigated, and their various efficiencies have been compared with one another. Consistent traffic patterns throughout the simulation were shown to improve MCTS performance. A situation like this is usually impossible to put into practice due to the rapidity with which traffic patterns can shift. On the other hand, it was verified that by employing LSTM, this problem could be solved, and an acceptable SLA was achieved. The proposed model is compared with other load balancing techniques for the optimization of resource allocation. Based on the result, the proposed model shows the accuracy rate is enhanced by approximately 10–15% as compared with other models. The result of the proposed model reduces the error percent rate of the traffic load average request blocking probability by approximately 9.5–10.2% as compared to other different models. This means that the proposed technique improves network usage by taking less amount of time due, to memory, and central processing unit due to a good predictive approach compared to other models. In future research, we implement cloud data centre employing various heuristics and machine learning approaches for load balancing of energy cloud using firefly algorithms.

Abstract: The constantly developing sphere of information and communication technologies (ICT) has led to the need of creating a concept which is different from the existing ones that interact objects on the global Internet - "Internet of Things" (IoT). The main drivers of digital transformation are innovative technologies such as Artificial Intelligence, Intelligent Apps and Analytics, Intelligent Things, Digital Twins, Cloud and Edge computing. Fog Computing allows you to provide services with faster response and higher quality and expand computing capabilities to the network boundaries that are suitable for providing IoT services in Smart Grid networks. Grid networks use Internet of Things (IoT) applications to manage networks and conduct intelligent monitoring, and in order to expand the computing capabilities of the network to the boundaries of the network itself and ensure better provision of IoT services, it is necessary to pay attention to the concept of fog computing. This article proposes a computational paradigm for providing latency-sensitive services and intelligent management of IoT applications in the Grid networks.

Abstract: Cloud computing offers users a new way to access the computing resources, such as data storage, software, and computing power on demand whenever and wherever they need. It is among the rapidly growing fields in the information and communication technologies (ICT) paradigm that has greatly impacted our daily lives. Recently, smart grids which are a smarter and more enhanced version of traditional electricity system counterpart have been benefited from the integration of cloud computing. This paper introduces cloud computing and smart grids, presents how the electricity grid evolution led to the creation of the smart grid, and showcases the way both fields have been augmented. It further argues how smart grid technologies play a noteworthy role in Saudi Arabia’s Vision 2030.

Abstract: In 2020 we deployed QPACE 4, which features 64 Fujitsu A64FX model FX700 processors interconnected by InfiniBand EDR. QPACE 4 runs an open-source software stack. For Lattice QCD simulations we ported the Grid LQCD framework to support the ARM Scalable Vector Extension (SVE). In this contribution we discuss our SVE port of Grid, the status of SVE compilers and the performance of Grid. We also present the benefits of an alternative data layout of complex numbers for the Domain Wall operator.

Abstract: In the IT industry, we are now in the era of Cloud Computing Technology. Cloud computing, which is based on the Internet, has the most powerful computation architecture. It is made up of a collection of connected and integrated hardware, software, and internet infrastructure. It has a number of features atop grid computing and other types of computing. In this work, I provide a summary of cloud computing evaluations based on a review of more than 30 cloud computing articles. The outcome of this study represents the state of the IT industry before and after cloud computing. Cloud computing is defined as the provision of resources such as network, storage, and servers on demand or on a pay-per-use basis over the internet. Although cloud computing is assisting the Information Technology business, there is still a need for more study and development in this area. In this work, we have contributed an advanced overview focused on the cloud computing idea and the most advanced research issues.

Abstract: AbstractR programming language is commonly used for statistical computing, data science and stochastic simulation. Existing packages for R allow to run parallel code on various parallel architectures, however, the support for distributed (volunteer) computing is rather weak. This article describes a new R package RBOINC that allows to run parallel code on desktop grid systems via the BOINC open source system for grid computing, which is a promising approach for parallel stochastic simulation. Among the possible ways to utilize the new package, an approach to parallel regenerative stochastic simulation within the generalized semi-Markov processes framework is suggested.KeywordsDistributed ComputingVolunteer ComputingBOINCR SoftwareGeneralized Semi-Markov Processes

Abstract: The IT industries are using cloud computing technology today. The most efficient structure for cloud computing is one that is entirely Internet-based. It is made up of a combination of networked, integrated, and software- and hardware-based components. It offers many advantages over both grid computing and other types of computing. In this essay, I've even provided a brief evaluation of cloud computing based on reading more than 30 cloud computing articles. The findings of this evaluation signalise the evolution of the IT sectors before and after cloud computing.

Abstract: Cloud computing is a relatively mature business computing model, which is gradually developed from technologies such as distributed computing, parallel processing, and grid computing. Similarly, with the continuous emergence of cloud computing applications, people’s understanding of cloud computing is also constantly changing. This paper designs a private cloud platform called “UCloudStack” based on cloud computing technology. The platform can provide a complete set of cloud resource management capabilities such as unified management of core services such as virtualization, SDN network, and distributed storage, resource scheduling, monitoring logs, and operation and maintenance, helping the digital transformation of government and enterprises.

Abstract: The Large Hadron Collider (LHC) demands a huge amount of computing resources to deal with petabytes of data generated from High Energy Physics (HEP) experiments and user logs, which report user activity within the supporting Worldwide LHC Computing Grid (WLCG). An outburst of data and information is expected due to the scheduled LHC upgrade, viz., the workload of the WLCG should increase by 10 times in the near future. Autonomous system maintenance by means of log mining and machine learning algorithms is of utmost importance to keep the computing grid functional. The aim is to detect software faults, bugs, threats, and infrastructural problems. This paper describes a general-purpose solution to anomaly detection in computer grids using unstructured, textual, and unsupervised data. The solution consists in recognizing periods of anomalous activity based on content and information extracted from user log events. This study has particularly compared One-class SVM, Isolation Forest (IF), and Local Outlier Factor (LOF). IF provides the best fault detection accuracy, 69.5%.

Abstract: This paper reviews the development of computational fluid dynamics, especially computational aerodynamics. This paper summarizes the achievements of CFD in grid technology, analyzes the existing problems and perplexities, and prospects its development trend. The CFD grid technology includes structured grid, unstructured grid, hybrid grid and overlapping grid.

Abstract: Abstract: The world of computer networks and information technology is evolving at an enormous rate. In which cloud computing is one of the few methods to provide users with the data, resources in a more efficient way. Cloud computing is seen as a more advanced version of computer grid inclusive of virtualization and resources sharing. Distributed computing over the internet is another new innovative technique using storage and the internet as one. "The Cloud" is an idea used to portray the virtual idea of advanced stockpiling, which can mean the information is put away on workers genuinely positioned in numerous geological areas. Cloud computing is the method for getting to a common pool of configurable assets that can be quickly given, utilized and delivered with insignificant exertion with respect to clients or specialist organizations. Cloud computing is arising at the combination of three significant patterns—administration direction, virtualization and normalization of storing through the Internet

Abstract: Open Science Grid (OSG) is a consortium that enables many scientific breakthroughs by providing researchers with access to shared High Throughput Computing (HTC) compute clusters in support of large-scale collaborative research. To meet the demand on campus, Georgia Institute of Technology (GT)’s Partnership for an Advanced Computing Environment (PACE) team launched a centralized OSG support project, powered by Buzzard, an NSF-funded OSG cluster. We describe Buzzard’s unique multi-tenant architecture, which supports multiple projects on a single CPU/GPU pool, for the benefit of other institutions considering a similar approach to support OSG on their campuses.

Abstract: Abstract Task scheduling is an important activity in parallel and distributed computing environment like grid because the performance depends on it. Task scheduling gets affected by behavioral and primary uncertainties. Behavioral uncertainty arises due to variability in the workload characteristics, size of data and dynamic partitioning of applications. Primary uncertainty arises due to variability in data handling capabilities, processor context switching and interplay between the computation intensive applications. In this paper behavioral uncertainty and primary uncertainty with respect to tasks and resources parameters are managed using Type-2-Soft-Set (T2SS) theory. Dyna-Q-Learning task scheduling technique is designed over the uncertainty free tasks and resource parameters. The results obtained are further validated through simulation using GridSim simulator. The performance is good based on metrics such as learning rate, accuracy, execution time and resource utilization rate.

Abstract: The current COVID-19 global pandemic caused by the SARS-CoV-2 betacoronavirus has resulted in over a million deaths and is having a grave socio-economic impact, hence there is an urgency to find solutions to key research challenges. Much of this COVID-19 research depends on distributed computing. In this article, I review distributed architectures -- various types of clusters, grids and clouds -- that can be leveraged to perform these tasks at scale, at high-throughput, with a high degree of parallelism, and which can also be used to work collaboratively. High-performance computing (HPC) clusters will be used to carry out much of this work. Several bigdata processing tasks used in reducing the spread of SARS-CoV-2 require high-throughput approaches, and a variety of tools, which Hadoop and Spark offer, even using commodity hardware. Extremely large-scale COVID-19 research has also utilised some of the world's fastest supercomputers, such as IBM's SUMMIT -- for ensemble docking high-throughput screening against SARS-CoV-2 targets for drug-repurposing, and high-throughput gene analysis -- and Sentinel, an XPE-Cray based system used to explore natural products. Grid computing has facilitated the formation of the world's first Exascale grid computer. This has accelerated COVID-19 research in molecular dynamics simulations of SARS-CoV-2 spike protein interactions through massively-parallel computation and was performed with over 1 million volunteer computing devices using the Folding@home platform. Grids and clouds both can also be used for international collaboration by enabling access to important datasets and providing services that allow researchers to focus on research rather than on time-consuming data-management tasks.

Abstract: High-performance computing is changing computing. With emerging technologies like grid computing, cloud computing applications have changed the way we compute and communicate. Cloud computing has made computing huge amounts of data on the fly possible and uses flexible resources according to the requirement for real-time applications. Cloud computing comes with a pay per use model so that users pay for only those resources that they have used. Inside cloud there lie many issues related to efficient and cost-effective models to improve cloud performance and complete the client task with the least cost and high performance. Cloud is meant to deal with the most computationally intensive services; they require real-time computing which can only be achieved if the computational resources can compute it in the least time. Cloud can accomplish this using an efficient scheduling algorithm. This chapter focuses on task scheduling policy which aims to improve the performance in real-time with the least execution time, network cost and cost-effective performance parameters. The proposed model is inspired by the Big Bang–Big Crunch algorithm in astronomy. The proposed algorithm aims to improve the performance by reducing the scheduling delays and network delays with the least resource cost to complete the task at the least cost to the user with high quality of service.

Abstract: Nowadays, data analysis is an integral part of large-scale scientific and applied experiments. Opportunities of modern computing environments allow us to move from operating with traditional storage systems within solving data-intensive problems to the in-memory data grid technologies. Such technologies improve the performance and scalability of data processing compared to external databases because of a faster random access memory and other hardware advancements. The considered data grid enables applications to cache data in the memory. Based on our practical experience, we discuss the advantages of applying the in-memory data grid technology to analyze the energy system vulnerability. The complexity of this problem is determined by considering possible combinations of simultaneous failures of energy system elements. We use open source-based Apache Ignite to support high-performance computing and data distribution. The study aims to evaluate the impact of the data grid scaling on the problem-solving quality criteria. We used the resources of the public access Irkutsk Supercomputer Center to carry out experiments.KeywordsHPC-clusterData processingIn-memory data gridExperimental analysis

Abstract: With the development of society, the power system is becoming larger and larger, which makes the analysis and processing of power data more and more complex. It is necessary to introduce modern data technology to make the power system develop rapidly in the new era and better serve people's production and life. From the perspective of cloud computing technology, this paper analyzes the smart grid big data processing in detail.

Abstract: Grid computing can be thought of as large-scale distributed cluster computing and distributed parallel network processing. Users can obtain enormous computing power through network technology, which is challenging to get from a single computer. Job scheduling in grid computing is a critical issue that affects the overall grid system capability. In traditional scheduling, jobs are placed in queues, waiting for the availability of resources. Reservations reject if the required resources not obtained at the specified time. The impact that arises is the reduced use of resources. The scheduling algorithm and the parameters used to perform the work may vary, such as execution time, delivery time, and the number of resources. There is no guarantee when the job will execute using the scheduling algorithm. Therefore, it is necessary to improve resource utilization in the grid system and ensure that jobs will be carried out. This paper proposes a reservation scheduling strategy for MPI work, First Come First Serve Left Right Hole (FCFS-LRH). MPI jobs execute simultaneously, using more than one resource for implementation. When Completed, user MPI jobs will be scheduled on virtual compute nodes and mapped to actual compute nodes. The experimental results show that the increase in resource utilization strongly influenced by time flexibility.

Abstract: This thesis is addressing the authorization problem in healthgrid environments, which is still an obstacle in front of using grid computing for medical applications. Current grid authorization systems depend on the traditional access control approaches, in which users are granted static rights. These approaches were designed for closed systems, i.e. the computing system itself enforces the security policy. Grid computing is characterized as open computing system, which means there exist different authorities and multiple agents participating in the authorization process. Therefore, the adoption of classical access control models in a grid computing environment is not adequate. In this work, access control in grid computing is tackled from a new perspective. The authorization process is handled as a multi-player game and a new authorization model the Grid Usage Control (G-UCON) is developed as a solution outline for a suitable access control model for grid computing environments. G-UCON utilizes the notions of game theory, multi-agent systems, and open systems in the design of a new access control model. These are the minimal requirements for a basis access/usage control model for a grid computing environment. The Alternating-time Temporal Logic is used to write the G-UCON specifications because it is more suitable to capture the specifications of games over open systems. Various examples at the end of this work demonstrate how G-UCON can capture different complex situations which appear in healthgrid environments. Using G-UCON, special authorization requirements in the health applications can be modeled. Examples are: disclosure risk control and maintaining k-anonymity. Currently, there is no tool available which can validate the special grid delegation mechanism. Therefore, a formal validation of G-UCON is left for future work. Nevertheless, it is a common practice to supply new authorization models with examples until a formal validation and/or an implementation appears which normally happens some years later.

Abstract: This paper studies the supply chain construction and optimization model based on grid computing and process data mining algorithms. C2B supply chain management mode causes enterprises to rely too much on suppliers. In order to get rid of dependence on suppliers and establish partnerships with more suppliers is an effective way for e-commerce enterprises to reduce business risks, and selecting capable suppliers has a certain positive effect on promoting the overall function of the supply chain. Hence, in this paper, the model is based on this and the computin model is integrated. Using grid computing technology to the connect computers all over the world, and integrate computer resources all over the world into fully shared resources through high-speed Internet, can be said to be a new change based on supercomputer and Internet technology. The integration model is tested and verified from different aspects.

Abstract: Grid Computing merupakan salah satu sistem komputasi paralel terdistribusi dengan menggunakan banyak sumber daya komputasi yang dikelolah secara bersama dan terpisah secara geografis. Grid computing saat ini menjadi suatu bentuk solusi untuk melakukan komputasi dalam skala besar.Penelitian ini menggunakan beberapa komputer yang dibagi menjadi dua klaster yang terpisah. Menggunakan pustaka OpenMPI untuk lingkungan komputasi paralel dan Sun Grid Scheduler sebagai gridengine, Globus Toolkit sebagai middleware, Gridsphere dan Vine Toolkit sebagai portal grid. Penelitian ini dapat digunakan sebagai panduan untuk membangun sistem komputasi paralel pada infrastruktur grid computing dan mengetahui cara kerja sistem grid computing untuk dapat dilakukan pengembangan sistem untuk mendukung proses pembelajaran.

Abstract: We are at dawn of a new era - an era where multiple strong market and technological transformations have called for reexamination of our current electric grid. It has opened the door for new thinking about the existing grid. People have been talking about the “grid of the future” for a few years now. What should this grid look like? What should be in it and why ^[1] ? And how do we get there?