Economic-based Distributed Resource Management and Scheduling for Grid Computing

Clusters, Grids, and peer‐to‐peer (P2P) networks have emerged as popular paradigms for next generation parallel and distributed computing. They enable aggregation of distributed resources for solving large‐scale problems in science, engineering, and commerce. In Grid and P2P computing environments, the resources are usually geographically distributed in multiple administrative domains, managed and owned by different organizations with different policies, and interconnected by wide‐area networks or the Internet. This introduces a number of resource management and application scheduling challenges in the domain of security, resource and policy heterogeneity, fault tolerance, continuously changing resource conditions, and politics. The resource management and scheduling systems for Grid computing need to manage resources and application execution depending on either resource consumers' or owners' requirements, and continuously adapt to changes in resource availability.The management of resources and scheduling of applications in such large‐scale distributed systems is a complex undertaking. In order to prove the effectiveness of resource brokers and associated scheduling algorithms, their performance needs to be evaluated under different scenarios such as varying number of resources and users with different requirements. In a Grid environment, it is hard and even impossible to perform scheduler performance evaluation in a repeatable and controllable manner as resources and users are distributed across multiple organizations with their own policies. To overcome this limitation, we have developed a Java‐based discrete‐event Grid simulation toolkit called GridSim. The toolkit supports modeling and simulation of heterogeneous Grid resources (both time‐ and space‐shared), users and application models. It provides primitives for creation of application tasks, mapping of tasks to resources, and their management. To demonstrate suitability of the GridSim toolkit, we have simulated a Nimrod‐G like Grid resource broker and evaluated the performance of deadline and budget constrained cost‐ and time‐minimization scheduling algorithms. Copyright © 2002 John Wiley & Sons, Ltd.

Computational grades have emerged as a new approach to solving large-scale problems in the fields of science, engineering, and commerce. The computing grid is a hardware and software infrastructure that provides affordable, reliable, comprehensive, and affordable access to the computational abilities of others. A computational grid is associated with a set of resources on a large scale. Computational grades have emerged as a new approach to solving large-scale problems in the fields of science, engineering, and commerce. The computing grid is a hardware and software infrastructure that provides affordable, reliable, comprehensive, and affordable access to the computational abilities of others. A computational grid is associated with a set of resources on a large scale. The purpose of this thesis is to provide a method that optimizes resource management and scheduling in at least one direction. The main focus of the research is on the time criterion, the deadline for doing things and receiving the response from the grid are parameters that can be examined. From a more general perspective, the aim of the research is to get the answer as quickly as possible from the calculation grid. The proposed algorithm improves the scheduling and resource management of the grid in the direction of improvement, and the structure and form of this problem have not yet been resolved. The proposed solution has been considered hypothesis and removed some of the definitions of grid scheduling, such as cost, quality of service, architectures, and others, but ultimately heed it and timed the grid in some ways.

In Grid computing, resource management and fault tolerance services are important issues. The availability of the selected resources for job execution is a primary factor that determines the computing performance. The failure occurrence of resources in the grid computing is higher than in a tradition parallel computing. Since the failure of resources affects job execution fatally, fault tolerance service is essential in computational grids. And grid services are often expected to meet some minimum levels of quality of service (QoS) for desirable operation. However Globus toolkit does not provide fault tolerance service that supports fault detection service and management service and satisfies QoS requirement. Thus this paper proposes fault tolerance service to satisfy QoS requirement in computational grids. In order to provide fault tolerance service and satisfy QoS requirements, we expand the definition of failure, such as process failure, processor failure, and network failure. And we propose resource scheduling service, fault detection service and fault management service and show implement and experiment results.

In 1998, Ian Foster and Carl Kesselman, together with thirty distinguished experts in high-performance computing and networking, laid the foundations of a new computing model called GRID. Their vision was introduced in the book, The GRID: Blueprint for a New Computing Infrastructure (see review, SCPE Vol. 3, No. 3 ). The editors wrote: … A computational grid is a hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities… allowing new classes of applications to emerge… as yet, we have only a preliminary understanding of what these new applications will look like. Over the next five years, Grid computing became the hottest R&D field. Academia and national labs built experimental infrastructures, new startups were established to develop Grid technologies, and the HPC giant vendors joined the party. Each one pushed in a different direction based on what and how he believed Grid computing should look. It was only a matter of time until people started to ask what the Grid is all about. A survey of ten Grid experts produced ten different answers. The question remains without a clear answer, leaving many people confused. Ian Foster picked up the gloves. In his article, What is the GRID? A three point checklist (GRIDtoday, July 22, 2002: Vol. 1, No. 6), he redefines the Grid model to reflect the trades and changes in the field during the past five years. According to Foster, a Grid is a system that: Coordinates resources that are not subject to centralized control. Uses standard, open, general-purpose protocols and interfaces. Delivers a nontrivial quality of service. But Foster's new definition could not encompass all of the Grid's approaches. Moreover, Foster explained by example what the Grid is not: A cluster management system such as Sun's Sun Grid Engine, Platform's Load Sharing Facility, or Viridian's Portable Batch System can, when installed on a parallel computer or local area network, deliver quality of service guarantees and thus constitutes a powerful Grid resource. However, such a system is not a Grid itself, due to its centralized control of the hosts that it manages Within a week the responses started to come in. The first one was from Wolfgang Gentzsch, Director of Grid computing at Sun Microsystems (GRIDtoday, August 5, 2002: Vol. 1, No. 8). Gentzsch wrote: I very much like Ian's two earlier definitions. Combined, they may provide a very generic Grid definition. The new checklist, however, reduces the potential of a wide and rich variety of Grids, with different architectures, operational models, and applications, and may miss the chance to becoming widely accepted as a standard definition of the Grid. Why do we need a definition for the Grid? Can the Grid be defined? Is a definition is necessary to the Grid's development? The Grid is an open vision and thus cannot be defined. An attempt to define the Grid can only reach the foreseen technology horizon, and this horizon is only one to two years ahead. Grid computing is a paradigm in shift. New architectures and standards are changing the shape and direction of the Grid's development. In the past year one such prominent architecture was Open Grid Services Architecture (OGSA). The OGSA is the first attempt to bridge the gap between two cyberspace worlds: the Internet and the Grid. The Internet and the Grid are two different computing models. There are people who believe that the Grid is the next generation of the Internet and the World Wide Web, saying that the Great Global Grid is still many years away. Others state that the Grid will complement, not replace, the Internet as we know it. In 1998, when the Grid blueprint was published, a new Internet language appeared called eXtensible Markup Language (XML). XML is a development tool for a new Internet computing model known as Web Services. At that time, nobody understood that there is a relationship between Web Services and the Grid. Three years past before Steve Tuecke from Argonne National Laboratory started to write a spec for Grid Services. Later, people from IBM joined the mission and together they created OGSA. The OGSA specification outlines interfaces to grid computing software that comply with Web Services standards. If adopted, Grid services such as job scheduling, authentication, failure-detection, staging of applications and data, and migration of applications and data, will all be accessed through standard Web Services architecture. OGSA is basically where Grid meets Web Services and maybe the first step towards full integration of the Internet and the Grid. The Internet appeared a decade ago. In the beginning, the Net offered primitive applications: a mailing system, ftp services, and a web of home pages. Nobody asked, What is the Internet? The years passed and new emerging Internet technologies were developed. Today we can talk on the phone over the Net, chat with friends, and video conference with colleagues on the other side of the world. New computing and business models were developed: e-commerce, e-learning, e-science, and e-you-name-it. And nobody asks what the Internet is. The Grid is a super-model, a bag of networking models associated with many types of architectures, software packages, applications, and standards. Today, when people talk about the Grid they mention Data Grids, Science Grids, and Campus Grids. And at the same time they mention Farm computing, Peer-to-peer computing, and Utility computing, among others. Ten years from now many of today's technologies will disappear and new ones will be developed. I don't know how Grid computing will look a decade from now, but I know that we will have g-commerce, g-learning, g-science, and g-chat. Nobody will ask what the Grid is. It will be obvious, just as the Internet is today. Ami Marowka The Computer Aided Design Laboratory School of Engineering and Computer Science Hebrew University of Jerusalem

Starting from the actual business needs of power grid production, operation, and management, extract common key technologies of artificial intelligence, build an electric power artificial intelligence platform, provide sample labeling and management, model training, and model reasoning for business personnel, model developers, and application developers, and support power inspection, scheduling, safety supervision, marketing and other businesses through online service invocation. This paper studies the core issues of task scheduling and resource management of the electric power artificial intelligence platform. To begin with, it introduces its capabilities and overall architecture and analyzes its service objects, task types and processing processes. Then container technology and Kubernetes are employed to achieve resource-elastic management for multiple heterogeneous resources; a task scheduling method is put forward based on load balancing; last but not least, the functional components, deployment and application of the artificial intelligence platform are presented. By building the basic support capabilities of electric power artificial intelligence, reducing the threshold of artificial intelligence model training, gathering high-quality samples and high-quality models, unified model services are provided for business applications, thus accelerating the implementation of artificial intelligence applications and promoting the digital transformation and intelligent upgrade of the power grid.

This chapter introduces the application of the Hierarchical Genetic Strategy-based Grid scheduler (HGS-Sched) to the energy-aware independent batch scheduling problem in Computational Grids (CGs). The Dynamic Voltage Scaling (DVS) methodology is used for both scaling the power supply of the grid resources and reducing the cumulative power energy utilized by the grid computing machines. Two implementations of HGS-Sched—with elitist and struggle replacement mechanisms respectively—are defined and empirically evaluated. The effectiveness of the hierarchical schedulers are compared with the quality of single-population Genetic Algorithms (GAs) and Island GA models for four CG significant scenarios in static and dynamic modes. The simulation results show that meta-heuristic grid schedulers can significantly reduce the energy consumption in the system as well as be easily adapted to various scheduling scenarios.

Grids and peer-to-peer (P2P) networks have emerged as popular platforms for the next generation parallel and distributed computing. In these environments, resources are geographically distributed, managed and owned by various organizations with different policies, and interconnected by wide-area networks or the Internet. This introduces a number of resource management and application scheduling challenges in the domain of security, resource and policy heterogeneity, fault tolerance, and dynamic resource conditions. In these dynamic distributed computing environments, it is hard and challenging to carry out resource management design studies in a repeatable and controlled manner as resources and users are autonomous and distributed across multiple organizations with their own policies. Therefore, simulations have emerged as the most feasible technique for analyzing policies for resource allocation. This paper presents emerging trends in distributed computing and their promises for revolutionizing the computing field, and identifies distinct characteristics and challenges in building them. We motivate opportunities for modeling and simulation communities and present our discrete-event grid simulation toolkit, called GridSim, used by researchers world-wide for investigating the design of utility-oriented computing systems such as Data Centers and Grids. We present various case studies on the use of GridSim in modeling and simulation of Business Grids, parallel applications scheduling, workflow scheduling, and service pricing and revenue management.

Summary form only given. Grids and peer-to-peer (P2P) networks have emerged as popular platforms for the next generation parallel and distributed computing. In these environments, resources are geographically distributed, managed and owned by various organizations with different policies, and interconnected by wide-area networks or the Internet. This introduces a number of resource management and application scheduling challenges in the domain of security, resource and policy heterogeneity, fault tolerance, and dynamic resource conditions. In these dynamic distributed computing environments, it is hard and challenging to carry out resource management design studies in a repeatable and controlled manner as resources and users are autonomous and distributed across multiple organizations with their own policies. Therefore, simulations have emerged as the most feasible technique for analyzing policies for resource allocation. This paper presents emerging trends in distributed computing and their promises for revolutionizing the computing field, and identifies distinct characteristics and challenges in building them. We motivate opportunities for modeling and simulation communities and present our discrete-event grid simulation toolkit, called GridSim, used by researchers world-wide for investigating the design of utility-oriented computing systems such as data centers and grids. We present various case studies on the use of GridSim in modeling and simulation of business grids, parallel applications scheduling, workflow scheduling, and service pricing and revenue management.

Due to the complexity caused by distributed computing and resource heterogeneity, the resource management and application scheduling in grid systems are complex. This paper proposes a flexible auction-based resource allocation strategy with a proportional share model for economic-based Grid systems. Instead of maximizing resource utility, this proposed strategy aims to complete jobs by meeting their deadline and budget requirements with the lowest resource costs. The experimental results show that the proposed strategy could complete jobs before the deadline with lower budgets.

This paper presents the design of the automated resource management tool (ARMT), a software-based network management tool that automates the potentially complex manual task of global resource management in a mission-oriented, intranet environment. It allows the network operator to specify high level policies defining the network quality of service (QoS) that must be offered to the different applications sharing the IP network, under each operating mode of the network. The tool automatically handles resource conflicts in accordance with the high-level QoS policies defined by the network operator. It also monitors the network to detect events that may lead to deviations from the specified service policy and automatically implements proactive actions to ensure that the network delivers the required QoS. ARMT implements an innovative policy-driven QoS control technique which ensures that multimedia intranet applications will have an adaptive environment that permits them to coexist with other mission applications. The paper presents our experience with the implementation of a rapid prototype of the ARMT in a laboratory testbed environment.

The complex resources and tasks in grid computing environment make it very difficult to validate the methods of resource management and task scheduling. This paper proposes a task scheduling model for grid computing, designs and realizes a target genetic algorithm based on task scheduling method, combining resource management and task scheduling then gives attention to quality of service. The experimental results are perfect

Management of cloud resources and social change in a multi-tier environment: A novel finite automata using ant colony optimization with spanning tree

In modern cloud computing environments, customers increasingly depend on on-demand resource provisioning to handle dynamic workloads. However, fluctuations in job arrival rates can result in prolonged queue times, which negatively affect overall system performance. Although existing scheduling algorithms provide efficient job management, they often fail to account for the combined impact of queue delays and the need for flexible resource provisioning-particularly in business-critical applications. In order to tackle these issues, the paper proposes a new Optimized Job Scheduling and Resource Scaling (OJSRS) algorithm designed to improve job execution efficiency and support elastic resource management in cloud environments. The OJSRS algorithm integrates two key components: Tree-based Job Scheduling (TJS) and Automated Resource Scaling and Scheduling (ARSS). The TJS component constructs a hierarchical structure that concurrently maps incoming jobs to the most suitable Virtual Machines (VMs), thereby minimizing queue delays. Meanwhile, ARSS adjusts resource allocation dynamically, increasing or decreasing capacity according to workload requirements and cloud service provider policies, enabling responsive and adaptive provisioning. Experimental results show that the OJSRS algorithm increases resource utilization by approximately 5-10% and accelerates job completion through proactive resource scaling. This approach provides a significant performance advantage for cloud-native business applications that require both efficiency and scalability.

ABSTRAK Tujuan dari penelitian ini adalah untuk merancang dan mengimplementasikan aplikasi jadwal di SMKN 1 Lemahsugih dengan harapan dapat meningkatkan efektivitas pembelajaran di kelas. Metode yang digunakan mencakup perancangan aplikasi menggunakan Visual Basic versi 6.0 yang terintegrasi dengan database Microsoft Access. Penelitian ini dimulai dengan observasi mendalam terhadap masalah penjadwalan yang ada, seperti kesalahan guru dalam memasuki kelas dan keterlambatan yang sering terjadi. Selain itu, wawancara dengan staff kurikulum dilakukan untuk mengevaluasi dan mendiskusikan potensi perbaikan sistem. Aplikasi yang dikembangkan bertujuan untuk memperbaiki sistem informasi penjadwalan dan mempermudah pengelolaan jadwal pelajaran serta kegiatan sekolah. Hasil penelitian menunjukkan bahwa aplikasi ini berhasil meningkatkan manajemen jadwal, memperbaiki koordinasi antara guru dan siswa, serta mempermudah proses administrasi sekolah. Meskipun terdapat beberapa tantangan dalam implementasi, aplikasi ini terbukti memberikan manfaat signifikan dalam meningkatkan efektivitas proses belajar mengajar di SMKN 1 Lemahsugih. Kata kunci : Apalikasi Jadwal, manajemen waktu, efektivitas pembelajaran ABSTRACT The purpose of this study is to design and implement a scheduling application at SMKN 1 Lemahsugih with the aim of enhancing classroom learning effectiveness. The methodology includes designing the application using Visual Basic 6.0 integrated with a Microsoft Access database. The research began with an in-depth observation of existing scheduling issues, such as teachers entering the wrong classrooms and frequent delays. Additionally, interviews with curriculum staff were conducted to evaluate and discuss potential system improvements. The developed application aims to improve the scheduling information system and facilitate the management of class schedules and school activities. The results indicate that the application successfully enhances schedule management, improves coordination between teachers and students, and simplifies school administration. Despite some challenges in implementation, the application has proven to provide significant benefits in improving the effectiveness of the teaching and learning process at SMKN 1 Lemahsugih. Keywords: Scheduling Application, Time Management, Learning Effectiveness

Parallel and distributed computing has been the basis to many emerging areas, such as smart networks, cloud computing, big data analysis, and blockchain technology. Without the development of parallel and distributed computing technologies and various types of systems, it is not possible to meet the requirement on efficiency, accuracy, scalability, and reliability for various critical applications that support our modern economy and society. While parallel and distributed computing played a vital role in modern science, engineering, biology, medicine, pharmacy, astronomy, geology, and archaeology, its application has also been extended to business, finance, economics, management, government, and defense, covering all aspects of our modern society and life. Furthermore, parallel and distributed computing has emerged in recent advances of many hotspot research directions including artificial intelligence, machine learning, Internet of Things, bioinformatics, digital medicine, cybersecurity, and social computing, resulting in numerous ground-breading discoveries that are changing our society and life.