Abstract
This study aims to increase the productivity of grid systems by an improved scheduling method. A brief overview and analysis of the main scheduling methods in grid systems are presented. A method for increasing efficiency by optimizing the task graph structure considering the grid system node structure is proposed. Task granularity (the ratio between the amount of computation and transferred data) is considered to increase the efficiency of planning. An analysis of the impact on task scheduling efficiency in a grid system is presented. A correspondence of the task graph structure considering the node structure (in which the task is immersed) to the effectiveness of scheduling in a grid system is shown. A modified method for scheduling tasks while considering their granularity is proposed. The relevant algorithm for task scheduling in a grid system is developed. Simulation of the proposed algorithm using the modeling system GridSim is conducted. A comparative analysis between the modified algorithm and the algorithm of the hierarchical scheduler Maui is shown. The general advantages and disadvantages of the proposed algorithm are discussed.
Highlights
Planning and resource allocation in grid systems are crucial tasks due to the heterogeneous structure, large dimensionality and different types of problems encountered [1]
J=1,2,...Ni) represents the grid system node processors, and a plurality of ribs Li={lk,j | k,j=1,2,...Ni) of the graph indicates the relationships among the processors
Three main types of scheduling methods are used in grid systems: centralized, decentralized and hierarchical [2]
Summary
Planning and resource allocation in grid systems are crucial tasks due to the heterogeneous structure, large dimensionality and different types of problems encountered [1]. A grid system typically consists of K computed nodes {ri. Each node ri includes a plurality of Pi={pj |. J=1,2,...Ni) processors, the relations between which is given by the loaded Hi=(Bi,Li) graph. J=1,2,...Ni) represents the grid system node processors, and a plurality of ribs Li={lk,j | k,j=1,2,...Ni) of the graph indicates the relationships among the processors. Each vertex bj Bi has a weight vj equal to the performance of the corresponding. CPU pj Pi. The performance of the complete grid system of the i-th node is equal ∑
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