A Multiagent-Based Approach to the Grid-Scheduling Problem

Lecture Notes in Computer Science, 2004

This paper presents an agent-based resource scheduling algorithm for Grid computing systems. With this scheduling algorithm, the computational resources of the Grid can be dynamically scheduled according to the real-time working load on each node. Thus a Grid system can hold an excellent load balance state. Furthermore, the application of this algorithm is introduced into the practical protein molecules docking applications, which run at the DDG, a Grid computing system for drug discovery and design. Solid experimental results show the load balance and robustness of the proposed algorithm.

Computational Science, Engineering & Technology Series

HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

2020

Load-Balancing is an important problem in distributed heterogeneous systems. In this paper, an Agent-based load-balancing model is developed for implementation in a grid environment. Load balancing is realized via migration of worker agents from overloaded resources to underloaded ones. The proposed model purposes to take benefit of the multi-agent system characteristics to create an autonomous system. The Agent-based load balancing model is implemented using JADE (Java Agent Development Framework) and Alea 2 as a grid simulator. The use of MAS is discussed, concerning the solutions adopted for gathering information policy, location policy, selection policy, worker agents migration, and load balancing.

International Journal of Computer Applications and Technology, 2012

Grid computing is considered to be wide area distributed computing which provides sharing, selection and aggregation of distributed resources. Agent paradigm has been widely used in large number of research area and now a days it is widely used in grid computing. In this paper, we proposed agent based strategy that uses knowledge base reasoning framework for task scheduling in grid which minimizes makespan.

2013

The purpose of grid computing is to produce a virtual supercomputer by using free resources available through widespread networks such as the Internet. This resource distribution, changes in resource availability, and an unreliable communication infrastructure pose a major challenge for efficient resource allocation. Because of the geographical spread of resources and their distributed management, grid scheduling is considered to be a NP-complete problem. It has been shown that evolutionary algorithms offer good performance for grid scheduling. This article uses a new evaluation (distributed) algorithm inspired by the effect of leaders in social groups, the group leaders' optimization algorithm (GLOA), to solve the problem of scheduling independent tasks in a grid computing system. Simulation results comparing GLOA with several other evaluation algorithms show that GLOA produces shorter makespans.

Grid computing is a group of clusters connected over high-speed networks that involves coordinating and sharing computational power, data storage and network resources operating across dynamic and geographically dispersed locations. Resource management and job scheduling are critical tasks in grid computing. Resource selection becomes challenging due to heterogeneity and dynamic availability of resources. Job scheduling is a NP-complete problem and different heuristics may be used to reach an optimal or near optimal solution. This paper proposes a model for resource and job scheduling in dynamic grid environment. The main focus is to maximize the resource utilization and minimize processing time of jobs. Grid resource selection strategy is based on Max Heap Tree (MHT) that best suits for large scale application and root node of MHT is selected for job submission. Job grouping concept is used to maximize resource utilization for scheduling of jobs in grid computing. Proposed resource selection model and job grouping concept are used to enhance scalability, robustness, efficiency and load balancing ability of the grid.

Grid Computing is a collection of computing and storage resources that are collected from multiple administrative domains. Grid resources can be applied to reach a common goal. Since computational grids enable the sharing and aggregation of a wide variety of geographically distributed computational resources, an effective task scheduling is vital for managing the tasks. Efficient scheduling algorithms are the need of the hour to achieve efficient utilization of the unused CPU cycles distributed geographically in various locations. The existing job scheduling algorithms in grid computing are mainly concentrated on the system's performance rather than the user satisfaction. This research work presents a new algorithm that mainly focuses on better meeting the deadlines of the statically available jobs as expected by the users. This algorithm also concentrates on the better utilization of the available heterogeneous resources.

IJCA Proceedings on …, 2012

Grid computing solves larger scale applications by coordinating and sharing computational power, data storage and network resources across dynamic and geographically dispersed organizations by providing high performance computing platform with the goal of providing users with access to the resources they need, even when they need. Grids provide remote access to IT assets, and aggregate processing power. The goal of scheduling is to achieve highest possible system throughput and to match the application need with the available computing resources. Scheduling onto the Grid is NP complete, so there is no best scheduling algorithm for all grid computing systems. The basic grid model generally composed of a number of hosts, each composed of several computational resources, which may be homogeneous or heterogeneous. In this article we have discussed all possible job scheduling algorithm in brief and lastly provided comparison for all.

2012

Grid, an infrastructure for resource sharing, currently has shown its importance in many scientific applications requiring tremendously high computational power. Grid computing enables sharing, selection and aggregation of resources for solving complex and large-scale scientific problems. Grids computing, whose resources are distributed, heterogeneous and dynamic in nature, introduces a number of fascinating issues in resource management. Grid scheduling is the key issue in grid environment in which its system must meet the functional requirements of heterogeneous domains, which are sometimes conflicting in nature also, like user, application, and network. Moreover, the system must satisfy non-functional requirements like reliability, efficiency, performance, effective resource utilization, and scalability. Thus, overall aim of this research is to introduce new grid scheduling algorithms for resource allocation as well as for job scheduling for enabling a highly efficient and effective utilization of the resources in executing various applications. The four prime aspects of this work are: firstly, a model of the grid scheduling problem for dynamic grid computing environment; secondly, development of a new web based simulator (SyedWSim), enabling the grid users to conduct a statistical analysis of grid workload traces and provides a realistic basis for experimentation in resource allocation and job scheduling algorithms on a grid; thirdly, proposal of a new grid resource allocation method of optimal computational cost using synthetic and real workload traces with respect to other allocation methods; and finally, proposal of some new job scheduling algorithms of optimal performance considering parameters like waiting time, turnaround time, response time, bounded slowdown, completion time and stretch time. The issue is not only to develop new algorithms, but also to evaluate them on an experimental computational grid, using synthetic and real workload traces, along with the other existing job scheduling algorithms. Experimental evaluation confirmed that the proposed grid scheduling algorithms possess a high degree of optimality in performance, efficiency and scalability.

Proc. 6th Int'l Conf. …, 2005

The main objective of the Intelligent GRID Scheduling System (ISS) project is to provide a middleware infrastructure allowing optimal positioning and scheduling of real life applications in a computational GRID. According to data collected on the machines in the GRID, on the behaviour of the applications, and on the performance requirements demanded by the user, the best-suited computational resource is detected and allocated to execute the application. The monitoring information collected during execution is put into a database and reused for the next resource allocation decision. In addition to providing scheduling information, the collected data allows to detect overloaded resources and to pin-point inefficient applications that could be further optimised.