Local grid scheduling techniques using performance prediction

Abstract

The use of computational grids to provide an integrated computer platform, composed of differentiated and distributed systems, presents fundamental resource and workload management questions. Key services such as resource discovery, monitoring and scheduling are inherently more complicated in a grid environment where the resource pool is large, dynamic and architecturally diverse. The authors approach the problem of grid workload management through the development of a multi-tiered scheduling architecture (TITAN) that employs a performance prediction system (PACE) and task distribution brokers to meet user-defined deadlines and improve resource usage efficiency. Attention is focused on the lowest tier which is responsible for local scheduling. By coupling application performance data with scheduling heuristics, the architecture is able to balance the processes of minimising run-to-completion time and processor idle time, whilst adhering to service deadlines on a per-task basis.