Poster

Optimizing the scheduling of a set of applications on a Grid Computing

June 2015

Conference: Symposium on Optimization and Information Systems
At: Oran, Algeria

Authors:

Setaouti Samir

University Mustapha Stambouli of Mascara

ResearchGate has not been able to resolve any citations for this publication.

ResearchGate has not been able to resolve any references for this publication.

Article

Grid Computing Job Scheduling Using Biogeography based Optimization with Elitism

May 2019 · Journal of the Korean Operations Research and Management Science Society

Sung-Soo Kim

Conference Paper

Utility Based Multiple QoS Guaranteed Resource Scheduling Optimization in Grid Computing

March 2007

In this paper, we consider multiple QoS based grid resource scheduling. It is heterogeneity and dynamics of the grid that make QoS problems in grid environment challenging. Computational grid's resource management must deal with various demands from users. Each of grid task agent's diverse requirements is modeled as a quality of service (QoS) dimension, associated with each QoS dimension is a ... [Show full abstract] utility function that defines the benefit that is perceived by a user with respect to QoS choices in that dimension. The objective of multiple QoS based grid resource scheduling is to maximize the global utility of the scheduling system. This paper proposes an iterative scheduling algorithm that is used to perform optimal multiple QoS based resource scheduling. The experiments show that optimal multiple QoS based resource scheduling involves less overhead and leads to more efficient resource allocation than no optimal resource allocation

Article

Optimizing grid scheduling with particle swarm optimization

January 2013 · Life Science Journal

Grid computing is a computing framework to run different grid enabled applications. This paper proposes a neural network to capture user requirements automatically and to use it for resource selection. It also introduces a Particle Swarm Optimization (PSO) based approach to schedule computational grid jobs. Representations of position and particle velocity in conventional PSO are extended to real ... [Show full abstract] vectors. The proposed approach plans to dynamically generate an optimal schedule to finish jobs within a specific minimum time duration. It also uses resources efficiently.

Article

Utility Optimal Scheduling in Processing Networks

October 2010 · Performance Evaluation

We consider the problem of utility optimal scheduling in general \emph{processing networks} with random arrivals and network conditions. These are generalizations of traditional data networks where commodities in one or more queues can be combined to produce new commodities that are delivered to other parts of the network. This can be used to model problems such as in-network data fusion, stream ... [Show full abstract] processing, and grid computing. Scheduling actions are complicated by the \emph{underflow problem} that arises when some queues with required components go empty. In this paper, we develop the Perturbed Max-Weight algorithm (PMW) to achieve optimal utility. The idea of PMW is to perturb the weights used by the usual Max-Weight algorithm to ``push'' queue levels towards non-zero values (avoiding underflows). We show that when the perturbations are carefully chosen, PMW is able to achieve a utility that is within O(1/V) of the optimal value for any

V\geq1

, while ensuring an average network backlog of O(V).

Article

Evolutionary Grid Scheduling Algorithm with Predictive Resource Optimization

July 2010

Optimal assigning jobs to resources is an important problem in grid computing. Now grid scheduling policies are mostly traditional heuristic algorithms for scheduling n independent tasks on m processors in early finishing time. However grids have developed to wide area, heterogeneous and non autonomous environments, business objective also became crucial for the success of the scheduling. ... [Show full abstract] Therefore base on a grid scheduling model with business parameters, we designed an evolutionary scheduling algorithm. The algorithm capability and performance were demonstrated by simulations. Furthermore a predictive resource mechanism was brought out to improve scheduling efficiency. At last we presented implementation scenario of our algorithm with predictive resource optimization in wide area open grid.

Optimizing the scheduling of a set of applications on a Grid Computing

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