10th EAI International Conference on Performance Evaluation Methodologies and Tools

Research Article

Optimal population mix in pool depletion systems with two-class workload

  • @INPROCEEDINGS{10.4108/eai.25-10-2016.2266566,
        author={Davide Cerotti and Marco Gribaudo and Riccardo Pinciroli and Giuseppe Serazzi},
        title={Optimal population mix in pool depletion systems with two-class workload},
        proceedings={10th EAI International Conference on Performance Evaluation Methodologies and Tools},
        publisher={ACM},
        proceedings_a={VALUETOOLS},
        year={2017},
        month={5},
        keywords={stochastic models pool depletion system performance evaluation optimization multiclass models},
        doi={10.4108/eai.25-10-2016.2266566}
    }
    
  • Davide Cerotti
    Marco Gribaudo
    Riccardo Pinciroli
    Giuseppe Serazzi
    Year: 2017
    Optimal population mix in pool depletion systems with two-class workload
    VALUETOOLS
    ACM
    DOI: 10.4108/eai.25-10-2016.2266566
Davide Cerotti1, Marco Gribaudo1,*, Riccardo Pinciroli1, Giuseppe Serazzi1
  • 1: Politecnico di Milano
*Contact email: marco.gribaudo@polimi.it

Abstract

The evolutions of digital technologies and software applications have introduced a new computational paradigm that involves the concurrent processing of jobs taken from a large pool in systems with limited capacity. The definition of admission control policies that choose which jobs to process is crucial to improve the overall performance especially in systems with multiclass workload. In a previous work we show that in such systems, hereinafter called pool depletion systems, few parameters have a non-trivial impact on the processing time of the whole pool. Other performance indices, such as the energy consumption, are also deeply affected. In the present work, we further investigate such phenomenon by applying results from queueing theory, absorption time analysis and by performing discrete event simulations. We propose different techniques in order to identify the optimal or near-optimal setting. We analyze their complexity and provide guidelines to choose which of them adopt according to the application scenario characteristics.