An Efficient Scheduling Method for Grid Systems Based on a Hierarchical Stochastic Petri Net

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. In this paper, 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; Make span of the job workflow and execution time of varied size jobs. The experimental results prove that proposed job scheduling and resource matching algorithms performs better when evaluated against widely used FCFS job scheduling and biggest free resource matching strategies.

The accelerated development in peer‐to‐peer and Grid computing has positioned them as promising next‐generation computing platforms. They enable the creation of virtual enterprises for sharing resources distributed across the world. However, resource management, application development and usage models in these environments is a complex undertaking. This is due to the geographic distribution of resources that are owned by different organizations or peers. The resource owners of each of these resources have different usage or access policies and cost models, and varying loads and availability. In order to address complex resource management issues, we have proposed a computational economy framework for resource allocation and for regulating supply and demand in Grid computing environments. This framework provides mechanisms for optimizing resource provider and consumer objective functions through trading and brokering services. In a real world market, there exist various economic models for setting the price of services based on supply‐and‐demand and their value to the user. They include commodity market, posted price, tender and auction models. In this paper, we discuss the use of these models for interaction between Grid components to decide resource service value, and the necessary infrastructure to realize each model. In addition to usual services offered by Grid computing systems, we need an infrastructure to support interaction protocols, allocation mechanisms, currency, secure banking and enforcement services. We briefly discuss existing technologies that provide some of these services and show their usage in developing the Nimrod‐G grid resource broker. Furthermore, we demonstrate the effectiveness of some of the economic models in resource trading and scheduling using the Nimrod/G resource broker, with deadline and cost constrained scheduling for two different optimization strategies, on the World‐Wide Grid testbed that has resources distributed across five continents. Copyright © 2002 John Wiley & Sons, Ltd.

The aim of this thesis is to design and implement a new Grid Resource Management methodology, where non-massive resources owners would be able to share their resources and integrate human collaboration across multiple domains regardless of network technology, operative platform or administrative domain.<br/>This thesis proposes a distributed and heuristic policy-based resource management architecture for large-scale Grids. The resource management architecture proposed herein is composed of four main building blocs: services management, resource discovery and monitoring, resource scheduling and jobs allocation and activation. The Grid Services Management (GSM) and Jobs Allocation and Activation (JAA) are supported by means of a Policy-based Grid Resource Management Architecture (PbGRMA). This architecture is able to identify service needs arising from diverse sources during the deployment and management of Grid Services, such as requirements demanded by customers, applications and network conditions. Afterwards, the PbGRMA merges these requirements into deployment policies for the corresponding Grid Services. The Grid Resource Discovery and Monitoring (GRDM) is supported by the introduction of the SNMP-based Balanced Load Monitoring Agents for Resource Scheduling (SBLOMARS), in which network and computational resources are monitored by distributed agents. This allows for a flexible, heterogeneous and scalable monitoring system. The Grid Resource Scheduling (GRS) is based on the Balanced Load Multi-Constrained Resource Scheduler (BLOMERS). This heuristic scheduler represents an alternate way of solving the inherent NP-hard problem for resource scheduling in large-scale distributed networks by means of the implementation of a Genetic Algorithm.<br/>Finally, based on the outcome of both the GRDM and GRS, the PbGRMA allocates the corresponding Grid Services by means of its interfaces with Globus ToolKit Middleware and Unix-based CLI commands along of any large-scale Grid Infrastructure. <br/>The synergy obtained by these components allows Grid administrators to exploit the available resources with predetermined levels of Quality of Service (QoS), reducing computational costs and makespan in resource scheduling while ensuring that the resource load is balanced throughout the Grid. The makespan of a schedule is the time required for all jobs to be processed when no one job could be interrupted during its execution and each node can perform at most one operation at any time.<br/>This new approach has been successfully tested in a real large-scale scenario such as Grid5000. The results presented along this Thesis show that our general solution is a reliable, flexible and scalable architecture to deploy and manage Grid Services in large-scale Grid Infrastructures. Moreover, the substitution of the heuristic algorithm approach used into the Grid Resource Scheduling (GRS) phase by other non-heuristics selection algorithms could make our solution useful in smaller Grid Infrastructures.

Grid resource management and scheduling are confronted with a great challenge. This paper presented a dynamic resource price-adjusting (RPA) strategy in computational Grid. Three new concepts and two new evaluation standards are defined. On the basis of these theories, a Grid resource scheduling algorithm based on marker-driven using dynamic RPA strategy is proposed. With the help of GridSim toolkit, the simulation of resource scheduling algorithm proposed in the paper is realized, and the new algorithm is compared with GridSim DBC (deadline and budget constrained) scheduling algorithms. The experimental results show that the new algorithm improves the performance of Grid in aspect of increasing the task accomplishment ratio and solving the problem of load imbalance.

Performance evaluation of grid resource management and scheduling system is of key importance to the success of the grid. In this paper, we introduce a generalized stochastic Petri net (GSPN) model for such purpose. The model is simple with clarity and can be applied to other research issues on grid to provide them with performance evaluation capabilities.

A distributed multiple dimensional QoS constrained resource scheduling optimization policy in computational grid

Selecting appropriate resources for running a job efficiently is one of the common objectives in a computational grid. Resource scheduling should consider the specific characteristics of the application, and decide the metrics to be used accordingly. This paper presents a distributed resource scheduling framework mainly consisting of a job scheduler and a local scheduler. In order to meet the requirements of different applications, we adopt HGSA, a Heuristic-based Greedy Scheduling Algorithm, to schedule jobs in the grid, where the heuristic knowledge is the metric weights of the computing resources and the metric workload impact factors. The metric weight is used to control the effect of the metric on the application. For different applications, only metric weights and the metric workload impact factors need to be changed, while the scheduling algorithm remains the same. Experimental results are presented to demonstrate the adaptability of the HGSA.

In order to resolve resource management and scheduling problem in Manufacturing Grid (MG) – an application of Grid technology, we develop a resource management and scheduling system with the interaction of Manufacturing Grid Information Service (MGIS) and the Manufacturing Grid Resource Scheduler (MGRS). The former, MGIS, provides fundamental mechanisms for remote resource encapsulating, registration, and monitoring, and the latter, MGRS, performs scheduling roles as Global Process Planning (GPP) analyzing, resource discovery, resource selection, and resource mapping.

Many applications involve the concepts of scheduling, such as communications, packet routing, production planning [Zhai et al., 2006], classroom arrangement [Mathaisel & Comm, 1991], aircrew scheduling [Chang, 2002], nurse scheduling [Ohki et al., 2006], food industrial [Simeonov & Simeonovova, 2002], control system [Fleming & Fonseca, 1993], resource-constrained scheduling problem [Chen, 2007] and grid computing. There are many different types of scheduling problems such as real-time, job-shop, permutation flow-shop, project scheduling and other scheduling problems have been studied intensively. However, in this work, the studied grid task scheduling problem is much more complex than above stated classic task scheduling problems. Restated, a grid application is regarded as a task scheduling problem involving tasks with inter-communication and distributed homogeneous or heterogeneous resources, and can be represented by a task interaction graph (TIG). Grid is a service for sharing computing power and data storage capacity over the Internet. The grid systems outperform simple communication between computers and aims ultimately to turn the global network of computers into one vast computational resource. Grid computing can be adopted in many applications, such as high-performance applications, large-output applications, data-intensive applications and community-centric applications. These applications major concern to efficiently schedule tasks over the available multi-processor environment provided by the grid. A grid is a collaborative environment in which one or more tasks can be submitted without knowing where the resources are or even who owns the resources [Foster et al., 2001]. The efficiency and effectiveness of grid resource management greatly depend on the scheduling algorithm [Lee et al., 2007]. Generally, in the grid environment, these resources are different over time, and such changes will affect the performance of the tasks running on the grid. In grid computing, tasks are assigned among grid system [Salman, 2002]. The purpose of task scheduling in grid is to find optimal task-processor assignment and hence minimize application completion time (total cost). Most scheduling problems in these applications are categorized into the class of NP-complete problems. This implies that it would take amount of computation time to obtain an optimal solution, especially for a large-scale scheduling problem. A variety of approaches have been applied to solve scheduling problems, such as

Improving the grid resource matching performance under large-scale users and computing resources environment is an important and challenging problem. Many existing grid resource matching and scheduling algorithms need search and compare every grid computing resource node without considering characteristics of grid resource and users' behavior. At the same time, with service oriented grid development, grid system should provide personalized service in scheduling process. Therefore, this paper proposes a novel grid resource matching algorithm based improved collaborative filtering algorithm that can give user recommendation resource set by computing the best approximate resource based on user's behavior.

The Grid Computing has emerged as a thorny platform to tackle numerous large-scale issues, particularly in science and engineering domains. One of the primary issues related to the economical and effective utilization of heterogeneous resources in a Grid scheduling. It is mainly due to the dynamic nature of grid. Grid scheduling could be subtle higher cognitive process that operates at totally different levels of grids. Grid Schedulers is employed to map user’s job to resources in keeping with their necessities. There are handful programming mechanism for grid environment the realistically wear down this dynamic nature in literature. In this study, Sensible Centrality Scheduling is used to deal with the programming computationally intensive Horde of Jobs (HOJ) applications. Their common and first aim is that they create planning choices while not totally correct performance prediction information. Another purpose to notice is that this Sensible algorithm adopts redite (needless replication) jobs. Our analysis study employs variety of experiments with numerous simulation settings. The results show the efficiency and aggressiveness of our algorithms in comparison to existing ways and we proved that is sensible centrality algorithm is the best algorithm.

In the wake of developments in computer and internet, power Internet of things technology is becoming more and more mature. As an extension of cloud computing, fog computing provides computing, network, storage and network edge communication for power grid Internet of things. Due to the limitation of its own storage and computing power and the increasing business demand, it is urgent to improve the efficient and feasible resource scheduling scheme. This paper takes the fog resource scheduling in power grid as starting point, proposes a fog resource scheduling algorithm based on time series optimization for the Internet of things in the power grid, and evaluates the proposed algorithm. The results show that the algorithm has strong application value for electricity the development of Internet of things provides the possibility.

This paper proposed an Aspect-Oriented Open Grid Services Architecture (AO-OGSA) to tackle the multi-binding of Open Grid Services Architecture (OGSA). Through forming the grid public demand into aspect module,AO-OGSA realized the separation between crosscutting concerns and core concerns in the grid application software. Therefore,it reduced the coupling between modules. Finally,taking the service packaging and resource scheduling in the optical grid as an example,the simulation modules based on OGSA and AO-OGSA were respectively constructed,and the simulation results were contrastively analyzed according to software system performance.

The article considers the resource allocation and scheduling problem in a grid computing environment. The article proposes system optimisation scheduling (SOS) that provides a potential solution of joint optimisation of objectives for both the resource and application layer, which combine both application-oriented and resource-oriented scheduling benefits. Grid systems will strive to find an optimal relation between user satisfaction and resource utilisation. Utility functions are used to express grid user's Quality of Service requirement, resource provider's benefit function and system's objectives. In order to verify the efficiency of the proposed scheduling algorithm, we compare the performance of application optimisation scheduling, resource optimisation scheduling, SOS with a traditional Round-Robin algorithm. The simulations study the effect of the request rate and task-to-resource ratio on the different scheduling algorithm.

As Grid technologies evolve quickly on Internet, research related to resource scheduling faces new opportunities and challenges. These new technologies, ideas and approaches provide a new environment for researching and developing the economy-based resource scheduling system. Aiming at the hierarchical Grid model, following utility optimization thinking, while an universal flexible utility function is provided for addressing the Grid users’ QoS requirements of deadline and budget, this paper proposes a new Grid resource scheduling algorithm, which can not only increase the utilization of resources and system throughput, but also realize cost-time optimization between resource providers and users within Grid systems. On the basis of having described the different functional parts of the proposed algorithm, two kinds of simulative experiments about completion time and cost of tasks are conducted. Finally, the experiment results show that the proposed algorithm is effective.