Absolutely expedient algorithms for learning Nash equilibria

Phansalkar, V.V.; Sastry, P. S.; Thathachar, M. A. L.

doi:10.1007/bf02830892

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…Moreover, the term (R ( ) − R ( −1) ) used in this model makes this algorithm highly robust in tracking a non-stationary job request arrival process. The PDFs are continuously updated by the cloudlets based on their private information and rewards received at every time-slot to learn the pure-strategy NE of the non-cooperative load balancing game and the space complexity of this algorithm is O(( × ) 2 × ), where is the length of memory required for storing the discrete version of ( ) [36].…”

Section: A Distributed Reinforcement Learning Algorithmmentioning

confidence: 99%

A Decentralized Economic Load-Balancing Framework among Federated Cloudlets

Mondal¹,

Das²

2021

Preprint

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Edge computing servers like cloudlets from different service providers compensate scarce computational and storage resources of mobile devices, are distributed across access networks. However, the dynamically varying computational requirements of associated mobile devices make cloudlets either overloaded or under-loaded. Hence, load balancing among neighboring cloudlets appears to be an essential research problem. Especially, the load balancing problem among federated cloudlets from the same as well as different service providers for low-latency applications needs significant attention. Thus, in this paper, we propose a decentralized load balancing framework among federated cloudlets for low-latency applications that focuses on latency bound rather than latency minimization. In this framework, we employ dynamic processor slicing for handling heterogeneous classes of job requests. We propose a continuous-action reinforcement learning automata-based algorithm that enables cloudlets to independently compute the load balancing strategies in a completely distributed network setting without any exhaustive control message exchange. To capture the economic interaction among federated cloudlets, we model this load balancing problem as an economic and non-cooperative game and by scaffolding the properties of the game formulation, we achieve faster convergence of the reinforcement learning automata. Furthermore, through extensive simulations, we study the impacts of exploration and exploitation on learning accuracy.

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Section: A Distributed Reinforcement Learning Algorithmmentioning

confidence: 99%

A Decentralized Economic Load-Balancing Framework among Federated Cloudlets

Mondal¹,

Das²

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…) used in this model makes this algorithm highly robust in tracking a non-stationary job request arrival process. The PDFs are continuously updated by the cloudlets based on their private information and rewards received at every time-slot to learn the pure-strategy NE of the non-cooperative load balancing game [40]. Theorem 6.1.…”

mentioning

confidence: 99%

Centralized and Decentralized Non-Cooperative Load-Balancing Games among Federated Cloudlets

Mondal,

Das,

Wong

2020

Preprint

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Edge computing servers like cloudlets from different service providers that compensate scarce computational, memory, and energy resources of mobile devices, are distributed across access networks. However, depending on the mobility pattern and dynamically varying computational requirements of associated mobile devices, cloudlets at different parts of the network become either overloaded or under-loaded. Hence, load balancing among neighboring cloudlets appears to be an essential research problem. Nonetheless, the existing load balancing frameworks are unsuitable for low-latency applications. Thus, in this paper, we propose an economic and non-cooperative load balancing game for low-latency applications among neighboring cloudlets, from same as well as different service providers. Firstly, we propose a centralized incentive mechanism to compute the unique Nash equilibrium load balancing strategies of the cloudlets under the supervision of a neutral mediator. With this mechanism, we ensure that the truthful revelation of private information to the mediator is a weakly-dominant strategy for both the under-loaded and overloaded cloudlets. Secondly, we propose a continuousaction reinforcement learning automata-based algorithm, which allows each cloudlet to independently compute the Nash equilibrium in a completely distributed network setting. We critically study the convergence properties of the designed learning algorithm, scaffolding our understanding of the underlying load balancing game for faster convergence. Furthermore, through extensive simulations, we study the impacts of exploration and exploitation on learning accuracy. This is the first study to show the effectiveness of reinforcement learning algorithms for load balancing games among neighboring cloudlets.

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Learning Through Reinforcement and Replicator Dynamics

Börgers

Sarin

1997

Journal of Economic Theory

559

414

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This paper considers a version of Bush and Mosteller's ([5], [6]) stochastic learning theory in the context of games. We compare this model of learning to a model of biological evolution. The purpose is to investigate analogies between learning and evolution. We¯nd that in the continuous time limit the biological model coincides with the deterministic, continuous time replicator process. We give conditions under which the same is true for the learning model. For the case that these conditions do not hold, we show that the replicator process continues to play an important role in characterising the continuous time limit of the learning model, but that a di®erent e®ect (\Probability Matching") enters as well.

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Absolutely expedient algorithms for learning Nash equilibria

Cited by 6 publications

References 9 publications

A Decentralized Economic Load-Balancing Framework among Federated Cloudlets

A Decentralized Economic Load-Balancing Framework among Federated Cloudlets

Centralized and Decentralized Non-Cooperative Load-Balancing Games among Federated Cloudlets

Learning Through Reinforcement and Replicator Dynamics

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