DIANA Scheduling Hierarchies for Optimizing Bulk Job Scheduling

TL;DR

This paper introduces DIANA, a peer-to-peer scheduling model designed to improve job re-organization and load balancing in large-scale Grid systems, addressing limitations of existing meta-schedulers.

Contribution

It proposes a novel peer-to-peer scheduling approach with the DIANA algorithm for dynamic, self-organizing resource management in Grid infrastructures.

Findings

DIANA improves load distribution in Grid systems.

The model supports dynamic re-organization of scheduled jobs.

Case studies validate the effectiveness of the approach.

Abstract

The use of meta-schedulers for resource management in large-scale distributed systems often leads to a hierarchy of schedulers. In this paper, we discuss why existing meta-scheduling hierarchies are sometimes not sufficient for Grid systems due to their inability to re-organise jobs already scheduled locally. Such a job re-organisation is required to adapt to evolving loads which are common in heavily used Grid infrastructures. We propose a peer-to-peer scheduling model and evaluate it using case studies and mathematical modelling. We detail the DIANA (Data Intensive and Network Aware) scheduling algorithm and its queue management system for coping with the load distribution and for supporting bulk job scheduling. We demonstrate that such a system is beneficial for dynamic, distributed and self-organizing resource management and can assist in optimizing load or job distribution in complex Grid infrastructures.