Libra: a computational economy-based job scheduling system for clusters

  • Authors:

  • Jahanzeb Sherwani;Nosheen Ali;Nausheen Lotia;Zahra Hayat;Rajkumar Buyya

  • Affiliations:

  • Department of Computer Science and Engineering, Lahore University of Management Sciences (LUMS), Lahore, Pakistan;Department of Computer Science and Engineering, Lahore University of Management Sciences (LUMS), Lahore, Pakistan;Department of Computer Science and Engineering, Lahore University of Management Sciences (LUMS), Lahore, Pakistan;Department of Computer Science and Engineering, Lahore University of Management Sciences (LUMS), Lahore, Pakistan;Grid Computing and Distributed Systems Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Australia

  • Venue:
  • Software—Practice & Experience
  • Year:
  • 2004

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Abstract

Clusters of computers have emerged as mainstream parallel and distributed platforms for high-performance, high-throughput and high-availability computing. To enable effective resource management on clusters, numerous cluster management systems and schedulers have been designed. However, their focus has essentially been on maximizing CPU performance, but not on improving the value of utility delivered to the user and quality of services. This paper presents a new computational economy driven scheduling system called Libra, which has been designed to support allocation of resources based on the users' quality of service requirements. It is intended to work as an add-on to the existing queuing and resource management system. The first version has been implemented as a plugin scheduler to the Portable Batch System. The scheduler offers market-based economy driven service for managing batch jobs on clusters by scheduling CPU time according to user-perceived value (utility), determined by their budget and deadline rather than system performance considerations. The Libra scheduler has been simulated using the GridSim toolkit to carry out a detailed performance analysis. Results show that the deadline and budget based proportional resource allocation strategy improves the utility of the system and user satisfaction as compared with system-centric scheduling strategies.