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A stochastic game framework for analyzing computational investment strategies in distributed computing

Abstract : We study a stochastic game framework with dynamic set of players, for modeling and analyzing their computational investment strategies in distributed computing. Players obtain a certain reward for solving the problem or for providing their computational resources, while incur a certain cost based on the invested time and computational power. We first study a scenario where the reward is offered for solving the problem, such as in blockchain mining. We show that, in Markov perfect equilibrium, players with cost parameters exceeding a certain threshold, do not invest; while those with cost parameters less than this threshold, invest maximal power. Here, players need not know the system state. We then consider a scenario where the reward is offered for contributing to the computational power of a common central entity, such as in volunteer computing. Here, in Markov perfect equilibrium, only players with cost parameters in a relatively low range in a given state, invest. For the case where players are homogeneous, they invest proportionally to the `reward to cost' ratio. For both the scenarios, we study the effects of players' arrival and departure rates on their utilities using simulations and provide additional insights.
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https://hal.inria.fr/hal-01870871
Contributor : Swapnil Dhamal <>
Submitted on : Monday, December 9, 2019 - 10:37:38 PM
Last modification on : Monday, August 24, 2020 - 4:16:12 PM
Long-term archiving on: : Tuesday, March 10, 2020 - 9:04:39 PM

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  • HAL Id : hal-01870871, version 5
  • ARXIV : 1809.03143

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Swapnil Dhamal, Walid Ben-Ameur, Tijani Chahed, Eitan Altman, Albert Sunny, et al.. A stochastic game framework for analyzing computational investment strategies in distributed computing. 2019. ⟨hal-01870871v5⟩

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