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10th EAI International Conference on Performance Evaluation Methodologies and Tools

Research Article

Fair workload distribution for multi-server systems with pulling strategies

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.4108/eai.25-10-2016.2267058,
        author={Sabina Rossi and Andrea Marin},
        title={Fair workload distribution for multi-server systems with pulling strategies},
        proceedings={10th EAI International Conference on Performance Evaluation Methodologies and Tools},
        publisher={ACM},
        proceedings_a={VALUETOOLS},
        year={2017},
        month={5},
        keywords={markov models fork-join queueing systems load balancing rate adaptation},
        doi={10.4108/eai.25-10-2016.2267058}
    }
    
  • Sabina Rossi
    Andrea Marin
    Year: 2017
    Fair workload distribution for multi-server systems with pulling strategies
    VALUETOOLS
    ACM
    DOI: 10.4108/eai.25-10-2016.2267058
Sabina Rossi1, Andrea Marin1,*
  • 1: University Ca' Foscari Venice
*Contact email: marin@dais.unive.it

Abstract

We consider systems with a single queue and multiple parallel servers. Each server fetches a job from the queue immediately after completing its current work. We propose a pulling strategy that aims at achieving a fair distribution of the number of processed jobs among the servers. We show that if the service times are exponentially distributed then our strategy ensures that in the long run the expected difference among the processed jobs at each server is finite while maintaining a reasonable throughput. We give the analytical expressions for the stationary distribution and the relevant stationary performance indices like the throughput and the system’s balance. Interestingly, the proposed strategy can be used to control the join-queue length in fork-join queues and the analytical model gives the closed form expression of the performance indices in saturation.

Keywords
markov models fork-join queueing systems load balancing rate adaptation
Published
2017-05-03
Publisher
ACM
http://dx.doi.org/10.4108/eai.25-10-2016.2267058
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