An efficient scheduling method for grid systems based on a hierarchical stochastic petri net

This paper addresses the problem of resource scheduling in a grid computing environment. One of the main goals of grid computing is to share system resources among geographically dispersed users, and schedule resource requests in an efficient manner. Grid computing resources are distributed, heterogeneous, dynamic, and autonomous, which makes resource scheduling a complex problem. This paper proposes a new approach to resource scheduling in grid computing environments, the hierarchical stochastic Petri net (HSPN). The HSPN optimizes grid resource sharing, by categorizing resource requests in three layers, where each layer has special functions for receiving subtasks from, and delivering data to, the layer above or below. We compare the HSPN performance with the Min-min and Max-min resource scheduling algorithms. Our results show that the HSPN performs better than Max-min, but slightly underperforms Min-min

A fast, effective local search for scheduling independent jobs in heterogeneous computing environments

The efficient scheduling of independent computational jobs in a heterogeneous computing (HC) environment is an important problem in domains such as grid computing. Finding optimal schedules for such an environment is (in general) an NP-hard problem, and so heuristic approaches must be used. Work with other NP-hard problems has shown that solutions found by heuristic algorithms can often be improved by applying local search procedures to the solution found. This paper describes a simple but effective local search procedure for scheduling independent jobs in HC environments which, when combined with fast construction heuristics, can find shorter schedules on benchmark problems than other solution techniques found in the literature, and in significantly less time

Resource Schedulingin Grid Computing: A Survey

Grid computing is a computing framework to meet growing demands for running heterogeneous grid enables applications. A grid system is composed of computers which are separately located and connected with each other through a network. Grids are systems that involve resource sharing and problem solving in heterogeneous dynamic grid environments. Here we present five different approaches/algorithms for resource allocation/ Scheduling in grid computing environment

Resource Management in Grid Computing: A Review

A Network Computing System is a virtual computer formed by a networked set of heterogeneous machines that agree to share their local resources with each other. A grid is a very large scale network computing system that scales to internet size environments with machines distributed across multiple organizationsand administrative domains. The resource management system is the central component of grid computing system. Resources in the grid are distributed, heterogeneous, autonomous and unpredictable. A resource management system matches requests to resources, schedules the matched resources, and executes the requests using scheduled resources. Scheduling in the grid environment depends upon the characteristics of the tasks, machines and network connectivity. The paper provides a brief overview of resource management in grid computing considering important factors such as types of resource management in grid computing, resource management models and comparison of various scheduling algorithm in resource management in grid computing

DBRS: Directed Acyclic Graph based Reliable Scheduling Approach in Large Scale Computing

In large scale environments, scheduling presents a significant challenge because it is an NP-hard problem. There are basically two types of task in execution- dependent task and independent task. The execution of dependent task must follow a strict order because output of one activity is typically the input of another. In this paper, a reliable fault tolerant approach is proposed for scheduling of dependent task in large scale computing environments. The workflow of dependent task is represented with the help of a DAG (directed acyclic graph). The proposed methodology is evaluated over various parameters by applying it in a large scale computing environment- ‘grid computing’. Grid computing is a high performance computing for solving complex, large and data intensive problems in various fields. The result analysis shows that the proposed DAG based reliable scheduling (DBRS) approach increases the performance of system by decreasing the makespan, number of failures and increasing performance improvement ratio (PIR)

Capacity planning and stochastic scheduling in grid computing environments

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