An LSTM Enabled Dynamic Stackelberg Game Theoretic Method for Resource Allocation in the Cloud

2019 ◽  
Author(s):  
Yongxin Liu ◽  
Laurent L. Njilla ◽  
Jian Wang ◽  
Houbing Song
Keyword(s):  
Resource Allocation ◽  
Stackelberg Game ◽  
Theoretic Method ◽  
Game Theoretic

A game-theoretic method of fair resource allocation for cloud computing services

2009 ◽  
Vol 54 (2) ◽  
pp. 252-269 ◽  
Author(s):  
Guiyi Wei ◽  
Athanasios V. Vasilakos ◽  
Yao Zheng ◽  
Naixue Xiong
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Theoretic Method ◽  
Computing Services ◽  
Cloud Computing Services ◽  
Game Theoretic

scholarly journals Game Theoretic Analysis of Security and Sustainability

2017 ◽  
Author(s):  
Bo An
Keyword(s):  
Game Theory ◽  
Resource Allocation ◽  
Cyber Security ◽  
Stackelberg Game ◽  
Decision Aids ◽  
Critical Issues ◽  
Game Theoretic ◽  
Novel Algorithms ◽  
Security Game ◽  
Dynamic Payoffs

Computational game theory has become a powerful tool to address critical issues in security and sustainability. Casting the security resource allocation problem as a Stackelberg game, novel algorithms have been developed to provide randomized security resource allocations. These algorithms have led to deployed security-game based decision aids for many real-world security domains including infrastructure security and wildlife protection. We contribute to this community by addressing several major research challenges in complex security resource allocation, including dynamic payoffs, uncertainty, protection externality, games on networks, and strategic secrecy. We also analyze optimal security resource allocation in many potential application domains including cyber security. Furthermore, we apply game theory to reasoning optimal policy in deciding taxi pricing scheme and EV charging placement and pricing.

A Game-Theoretic Formulation of Multi-Agent Resource Allocation

2005 ◽  
Author(s):  
Jonathan Bredin ◽  
Rajiv T. Maheswaran ◽  
Cagri Imer ◽  
Tamer Basar ◽  
David Kotz ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Multi Agent ◽  
Game Theoretic

Game-Based Resource Allocation Mechanism in B5G HetNets with Incomplete Information

2020 ◽  
Vol 10 (5) ◽  
pp. 1557
Author(s):  
Weijia Feng ◽  
Xiaohui Li
Keyword(s):  
Resource Allocation ◽  
Incomplete Information ◽  
Stackelberg Game ◽  
Superior Performance ◽  
Channel Gain ◽  
Mobile Users ◽  
Pricing Strategies ◽  
Game Equilibrium ◽  
Allocation Process ◽  
Realistic Situation

Ultra-dense and highly heterogeneous network (HetNet) deployments make the allocation of limited wireless resources among ubiquitous Internet of Things (IoT) devices an unprecedented challenge in 5G and beyond (B5G) networks. The interactions among mobile users and HetNets remain to be analyzed, where mobile users choose optimal networks to access and the HetNets adopt proper methods for allocating their own network resource. Existing works always need complete information among mobile users and HetNets. However, it is not practical in a realistic situation where important individual information is protected and will not be public to others. This paper proposes a distributed pricing and resource allocation scheme based on a Stackelberg game with incomplete information. The proposed model proves to be more practical by solving the problem that important information of either mobile users or HetNets is difficult to acquire during the resource allocation process. Considering the unknowability of channel gain information, the follower game among users is modeled as an incomplete information game, and channel gain is regarded as the type of each player. Given the pricing strategies of networks, users will adjust their bandwidth requesting strategies to maximize their expected utility. While based on the sub-equilibrium obtained in the follower game, networks will correspondingly update their pricing strategies to be optimal. The existence and uniqueness of Bayesian Nash equilibrium is proved. A probabilistic prediction method realizes the feasibility of the incomplete information game, and a reverse deduction method is utilized to obtain the game equilibrium. Simulation results show the superior performance of the proposed method.

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