1st International ICST Conference on Performance Evaluation Methodologies and Tools

Research Article

A two-class parallel queue with pure space sharing among rigid jobs and general service times

  • @INPROCEEDINGS{10.1145/1190095.1190097,
        author={Dimitrios  Filippopoulos and Helen  Karatza},
        title={A two-class parallel queue with pure space sharing among rigid jobs and general service times},
        proceedings={1st International ICST Conference on Performance Evaluation Methodologies and Tools},
        publisher={ACM},
        proceedings_a={VALUETOOLS},
        year={2012},
        month={4},
        keywords={Parallel System Supplementary Variable Stability Partial Derivative Discriminant Real Roots Monotonicity},
        doi={10.1145/1190095.1190097}
    }
    
  • Dimitrios Filippopoulos
    Helen Karatza
    Year: 2012
    A two-class parallel queue with pure space sharing among rigid jobs and general service times
    VALUETOOLS
    ACM
    DOI: 10.1145/1190095.1190097
Dimitrios Filippopoulos1,*, Helen Karatza1,*
  • 1: Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, 541 24, Greece.
*Contact email: fildim@csd.auth.gr, karatza@csd.auth.gr

Abstract

We analyze a parallel system model with one queue, two servers and rigid jobs. Each job arriving to the queue may require either one or two servers. Jobs that require one server (type-1 jobs) have exponentially distributed service requirements while jobs requiring two servers (type-2 jobs) have general service time distribution. The FCFS scheduling discipline is assumed and all jobs are served according to pure space sharing. By applying the supplementary variable method we derive closed-form expressions, exact as well as approximate, for the steady-state mean queue length and utilization of the servers. In addition, we investigate the utilization as well as the throughput of the system as the load reaches its maximal value inside the stability region. Numerical results are presented that give insight into the impact of each parameter on system performance.