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Synchronous resource allocation: modeling, capacity, and optimization

Abstract

We explore settings where it is necessary (due to physical or operational constraints) or desirable (due to synergies or ease of implementation) to assign resources to tasks in a synchronous manner. We model the system as a queueing network with flexible servers and introduce the notion of a configuration to address the synchronous assignment of servers. This allows for a unified approach to determine the effects of resource synchronization, covering a wide range of problems in the literature. The maximal capacity of the system is given by the solution of a linear programming problem that also provides the optimal fractions of time the servers should spend in different configurations. This is used as a basis for constructing policies that have capacity arbitrarily close to the maximal capacity. We contrast synchronous server assignment with an asynchronous approach (focusing on independently scheduling individual servers rather than configurations) and show that synchronous server assignment is attractive with respect to applicability (it can capture constraints on server assignment and synergies among servers), implementation (it may have significantly fewer combinations of server allocations), and capacity (when both are applicable, asynchronous and synchronous server assignment will yield the same maximal capacity). Finally, we illustrate our modeling framework using several examples.

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Acknowledgements

We would like to thank the anonymous referees for their useful comments. The research of the first two authors has been supported by NSF under grant CMMI-1536990. The third author has been supported by NSERC under the Discovery Grant program.

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Correspondence to Douglas G. Down.

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Andradóttir, S., Ayhan, H. & Down, D.G. Synchronous resource allocation: modeling, capacity, and optimization. OR Spectrum (2022). https://doi.org/10.1007/s00291-022-00684-x

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Keywords

  • Queueing
  • Server synchronization
  • Flexible servers
  • Server collaboration