Available Resources In Grid Research Articles

A Price-Based Grid Resources Pricing Approach for NonStorable Real Assets

A grid-computing paradigm delivers the processing power of massively parallel computation to all subscribed users. Current trends, research, and developments in grid computing show that the available grid resources exist as non-storable compute commodities and are distributed geographically – the grid problem. To solve the grid problem, several initiatives have developed frameworks for grid economy and have proposed several algorithms towards an optimized resources scheduling in a grid environment. However, since the grid resources availability depends on the time of usage and are transient, such generic approaches lack the ability to capture the realistic valuation of the resources and fail to guarantee the certainty in their availability measured as Quality of Service (QoS). Uncertainties in grid resource availability do not guarantee a user expected QoS without over committing (e.g., storing the non-storable resources) resources to the users. To guarantee QoS (satisfy a users’ computing needs), we propose a price-based and quality-aware model that captures the realistic value of the grid compute commodities. We use the financial option theory from a real option perspective to value grid resources by treating them as real assets. We discuss a set of results on pricing grid compute cycles. Our results are based on the compute cycle usage obtained from the WestGrid node at the University of Manitoba. We extend and generalize our study to any grid in general but with specific reference to the WestGrid. Keywords: Financial Options, Price Stochasticity, Compute Cycles, Real Options, Quality of Service. Allenotor, D. & Oyemade, D. A. (2022): A Price-Based Grid Resources Pricing Approach for Non-Storable Real AssetsJournal of Advances in Mathematical & Computational Science. Vol. 10, No. 2. Pp 1-18 DOI: dx.doi.org/10.22624/AIMS/MATHS/V10N2P1. Available online at www.isteams.net/mathematics-computationaljournal.

Exploiting resource profiling mechanism for large-scale scientific computing on grids

Large-scale scientific applications from various scientific domains (e.g., astronomy, physics, pharmaceuticals, chemistry, etc.) usually require substantial amounts of computing resources and storage space. International Grid computing resources can be a viable choice for supporting these challenging applications so that effectively locating suitable computing resources with minimal allocation overhead can be crucial. However, Grid resource availability is highly unstable and current Grid Information Service (GIS) cannot provide accurate state information. This can make it very difficult for users to schedule the jobs on the Grid system and to map tasks on appropriate available resources. In this paper, we present SCOUT system that can periodically profile Grid computing elements based on available number of CPU cores and average response time, and monitor the performance of each CE in the Virtual Organizations (VO). Micro-benchmark experimental results demonstrate that leveraging profiled data by SCOUT can improve the success rate of task executions and reduce the average response time.

Design of a Fault Tolerant Load Balancing Scheduler for Computational

Grid is a collection of heterogeneous resources from a decentralized management. These resources are from large number of organizations and each resource with a local administration and scheduling policy are connected by wide area network. Due to these factors, strong variations prevail in the availability of grid resources and lead to increase in the probability of failures than in traditional parallel and distributed systems. An efficient fault tolerant scheduling approach is needed to schedule the jobs to the appropriate resources which can handle the failure nature of grid resources. Load balancing is required to improve the utilization of the resources and throughput. In this paper, a new scheduling approach is proposed that schedules the grid tasks by considering the failure rate of the resources in order to handle task failures. The proposed Fault tolerant Load Balancing (FTLB) algorithm increases the execution success rate and improves makespan. This algorithm also considers load of each resource while scheduling the jobs to increase resource utilization and throughput of the system. By considering user demand such as deadline for job completion, it improves user satisfaction. The analysis of the experimental results proves that the proposed FTLB algorithm has improved makespan, hit rate and average resource utilization than the existing algorithms.

Cost-based Job grouping and Scheduling Algorithm for Grid computing Environments

The integration of remote and diverse resources and the increasing computational needs of Grand challenges problems combined with faster growth of the internet and communication technologies leads to the development of global computational grids. Grid computing is a prevailing technology, which unites underutilized resources in order to support sharing of resources and services distributed across numerous administrative region. An efficient and effective scheduling system is essentially required in order to achieve the promising capacity of grids. The main goal of scheduling is to maximize the resource utilization and minimize processing time and cost of the jobs. In this research, the objective is to prioritize the jobs based on execution cost and then allocate over the resources with minimum cost by merging it with conventional job grouping strategy to provide solution for better and more efficient job scheduling which is beneficial to both user and resource broker. The proposed scheduling approach in grid computing employs a dynamic cost-based job scheduling algorithm for making efficient mapping of job to available resources in grid. It also improves communication to computation ratio (CCR) and utilization of available resources by grouping the user jobs before resource allocation. General Terms Algorithm, GridSim.

Resource Grouping in Grid Environment Towards the Availability and Reliability of Computing Service

Intermittent availability of grid resources results in delays or failures of application execution. Hence, the reliability of application execution declines. In order to achieve a reliable application execution, a consistent availability of computing service is required. This paper proposes a grid computing system that achieves a consistent availability in order to achieve a reliable application execution. In the proposed system, steady state availability is achieved for longer durations. Therefore, the system may be deployed to execute longduration applications. The availability and reliability of the proposed computing service is analyzed using a Markov model. Simulation results prove that the proposed system achieves better reliability and availability compared to the existing replicated application execution system proposed in grid environment.

An Adaptive Scheduler Framework for Complex Workflow Jobs on Grid Systems

Grid Computing provides sharing of geographically distributed resources among large scale complex applications. Due to dynamic nature of resources in grid, there is a need of highly efficient job scheduling and resource management policies in grid. A novel Grid Resource Scheduler (GRS) is proposed to effectively utilize the available resources in Grid. Proposed GRS contributes, an optimal job scheduling algorithm on Job Rank-Backfilling policy and a resource matching algorithm based on ranking of resources with best fit allocation model. Performance of GRS is measured by considering a web based BLAST algorithm – a bioinformatics application. GRS aims in reducing; Makespan of the job workflow, execution time of varied size jobs, response time of the submitted jobs and overhead of using the system. It also considers improving the utilization factor and throughput of the available heterogeneous resources in grid. The experimental results prove that proposed grid scheduler framework performs better when evaluated against widely used First Come First Serve (FCFS), Shortest Job First (SJF) and Minimum Time to Due Date (MTTD) scheduling strategies.

A dynamic replica management strategy in data grid

Data Grid provides scalable infrastructure for storage resource and data files management, which supports several large scale applications. Due to limitation of available resources in grid, efficient use of the grid resources becomes an important challenge. Replication is a technique used in data grid to improve fault tolerance and to reduce the bandwidth consumption. This paper proposes a Dynamic Hierarchical Replication (DHR) algorithm that places replicas in appropriate sites i.e. best site that has the highest number of access for that particular replica. It also minimizes access latency by selecting the best replica when various sites hold replicas. The proposed replica selection strategy selects the best replica location for the users' running jobs by considering the replica requests that waiting in the storage and data transfer time. The simulated results with OptorSim, i.e. European Data Grid simulator show that DHR strategy gives better performance compared to the other algorithms and prevents unnecessary creation of replica which leads to efficient storage usage.

A fuzzy Grid-QoS framework for obtaining higher grid resources availability

A computational grid ensures the on-demand delivery of computing resources, in a security-aware, shared, scalable, and standards-based computing environment. A major concern is how to evolve a general and an encompassing framework that guarantees users' satisfaction measured as Quality of Services (QoS). To obtain a higher QoS, effective QoS perceived by subscribers (users) must conform to specified QoS agreements in the Service Level Agreements (SLAs) document--a legal contract between the Grid Services Provider (GSP) and users. Sometimes the effective user QoS does not conform to the specifications in the SLA because of the vagueness in linguistic definitions in the SLA. Existing approaches overcommitted resources to meet QoS. In this paper, we propose a fuzzy logic framework for calibrating a grid resources user-QoS that addresses the vagueness in linguistic definitions of the SLA document without overcommitting grid resources.

Multidimensional QOS Oriented Task Scheduling in Grid Environments

High availability of resources is the key requirement in grids where computational and network resources are highly dynamic and unpredictable. QoS (Quality of Service) based resource management and scheduling is an important issue in such dynamic and heterogeneous grid environments. The computational resources in the grid vary from dedicated clusters to non-dedicated desktops which are connected over communication networks of diverse bandwidths. The task-resource mapping in such a non-deterministic computing environment leads to concerns over QoS constraints imposed by application tasks to identify suitable resources. In this paper, we consider bandwidth requirement as a QoS of a network and availability requirement as a QoS of a compute resource. An Availability aware QoS oriented Algorithm (AQuA) for task scheduling in grids has been proposed. The algorithm is evaluated and compared with QGMM (QoS Guided Min-Min) algorithm in the simulated grid environment. The experimental analysis and results show that the AQuA algorithm is able to utilize the highly available resources in grid and therefore increases the reliability to successfully execute applications without adversely affecting the makespan.

ACQUIRING KNOWLEDGE IN A GRID SYSTEM VIA A RESOURCE DISCOVERY SCHEME

This paper studies a resource discovery scheme for a Grid system comprised of virtual organizations (VOs). The proposed scheme suggests that routers and resources comprise VOs within a Grid system, where a router controls locally a number of resources. The router also maintains the connections of each VO with other VOs in the system. Each VO acquires only partial/limited knowledge of the availability of resources controlled by other VOs in the system. When a request for a specific resource is created in a VO of the system, the goal is to discover an appropriate resource for its satisfaction, and subsequently to effectively direct this request. The proposed resource discovery scheme takes into consideration the fact that VOs only acquire a limited knowledge of the system and manages to extend this knowledge without affecting negatively the satisfaction of requests. During the satisfaction of requests, the proposed resource discovery scheme supports and enhances the VOs of the system in order to gain better knowledge of the Grid resource availability. As the creation and satisfaction of requests progress, the VOs manage to acquire an adequate knowledge of the Grid resources, enhancing the overall system's well-function. Finally, this paper presents the performance evaluation of the resource discovery scheme by providing a number of simulation tests in Grid systems of different sizes.

Grid Resource Availability Prediction-Based Scheduling and Task Replication

The frequent and volatile unavailability of volunteer-based Grid computing resources challenges Grid schedulers to make effective job placements. The manner in which host resources become unavailable will have different effects on different jobs, depending on their runtime and their ability to be checkpointed or replicated. A multi-state availability model can help improve scheduling performance by capturing the various ways a resource may be available or unavailable to the Grid. This paper uses a multi-state model and analyzes a machine availability trace in terms of that model. Several prediction techniques then forecast resource transitions into the model’s states. We analyze the accuracy of our predictors, which outperform existing approaches. We also propose and study several classes of schedulers that utilize the predictions, and a method for combining scheduling factors. We characterize the inherent tradeoff between job makespan and the number of evictions due to failure, and demonstrate how our schedulers can navigate this tradeoff under various scenarios. Lastly, we propose job replication techniques, which our schedulers utilize to replicate those jobs that are most likely to fail. Our replication strategies outperform others, as measured by improved makespan and fewer redundant operations. In particular, we define a new metric for replication efficiency, and demonstrate that our multi-state availability predictor can provide information that allows our schedulers to be more efficient than others that blindly replicate all jobs or some static percentage of jobs.

Converged Optical Network Infrastructures in Support of Future Internet and Grid Services Using IaaS to Reduce GHG Emissions

With the rapid and growing volume of green house gas (GHG) we may soon cross a tipping point where there may be dramatic climatic catastrophes such that governments will be forced to order the shutdown of coal powered electrical production or mandate carbon neutrality across all sectors of society. On the other hand, in the event of such a development, the future Internet and Grid infrastructure may become absolutely essential for communications and a replacement for travel and for the delivery of critical services such as health, education, research, etc. Information and Communication Technologies (ICT) produce 2%-3% of the world's GHG emissions through the consumption of electricity largely produced by coal plants. This rate of GHG is expected to double in the next few years and is clearly unsustainable. Therefore it is critical that any future Internet and Grid infrastructure be designed not only to survive, but also be sustained, through an age where no additional GHG emissions will be allowed. Converged optical networks, Grid and cloud services hosted at zero carbon renewable energy sites using Infrastructure as a Service (IaaS) where each network and computer element is represented to a user as a configurable virtual service will allow for the deployment of what are often referred to as ldquofollow the sun or follow the windrdquo optical network and Grid architectures where the network and Grid topology and Grid resources availability and location are constantly changing depending on local availability states of the wind or sun.

The GrADS Project: Software Support for High-Level Grid Application Development

Advances in networking technologies will soon make it possible to use the global information infrastructure in a qualitatively different way—as a computational as well as an information resource. As described in the recent book The Grid: Blueprint for a New Computing Infrastructure, this Grid will connect the nation’s computers, databases, instruments, and people in a seamless web of computing and distributed intelligence, which can be used in an on-demand fashion as a problem-solving resource in many fields of human endeavor—and, in particular, science and engineering. The availability of grid resources will give rise to dramatically new classes of applications, in which computing resources are no longer localized but, rather, distributed, heterogeneous, and dynamic; computation is increasingly sophisticated and multidisciplinary; and computation is integrated into our daily lives and, hence, subject to stricter time constraints than at present. The impact of these new applications will be pervasive, ranging from new systems for scientific inquiry, through computing support for crisis management, to the use of ambient computing to enhance personal mobile computing environments. To realize this vision, significant scientific and technical obstacles must be overcome. Principal among these is usability. The goal of the Grid Application Development Software (GrADS) project is to simplify distributed heterogeneous computing in the same way that the World Wide Web simplified information sharing over the Internet. To that end, the project is exploring the scientific and technical problems that must be solved to make it easier for ordinary scientific users to develop, execute, and tune applications on the Grid. In this paper, the authors describe the vision and strategies underlying the GrADS project, including the base software architecture for grid execution and performance monitoring, strategies and tools for construction of applications from libraries of grid-aware components, and development of innovative new science and engineering applications that can exploit these new technologies to run effectively in grid environments.