Practical Experiences with Task Scheduling Strategies for Image Processing Application on Heterogeneous Distributed Computing System

Malaysian Journal of Computer Science

Masahiko Hatanaka¹  Kalim Qureshi¹ 

关键词: Distributed image/raytracing computing;    Task partitioning and scheduling;    Load balancing;    Heterogeneous distributed computing;    Performance evaluation;   

DOI  :  

学科分类：社会科学、人文和艺术（综合）

来源: University of Malaya * Faculty of Computer Science and Information Technology

【 摘 要 】

Heterogeneous Distributed Computing (HDC) system consists of Workstations (WSs) and Personal Computers (PCs). In HDC system, each WS/PC may have different processor and performance. In order to take advantage of this diversity of processing power of a system, an effective task partitioning, scheduling, and load balancing are needed to get the optimum performance.This paper examines the effectiveness of task partitioning and scheduling strategies for image (raytracing) processing application on HDC system. The static and dynamic/Run-time Task Scheduling (RTS) strategies are shown inadequate for balancing the load of HDC system. Two adaptive tasks scheduling strategies are proposed for HDC image computing system. The adaptive strategies are: i) Master Initiated Sub-task size (MIS) strategy (based on centralized resources management approach), and ii) Worker Initiated Sub-task size (WIS) strategy (based on semi-decentralized resources management approach). Performances of all investigated strategies are evaluated on manager/master and workers model of HDC system.

【 授权许可】

Unknown