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.

scholarly journals Performance Comparison of Practical Resource Allocation Schemes for Device-to-Device Communications

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Gábor Fodor
Keyword(s):  
Resource Allocation ◽  
Power Control ◽  
Large Scale ◽  
Low Complexity ◽  
Base Station ◽  
Performance Comparison ◽  
Superior Performance ◽  
Channel Gain ◽  
D2d Communications ◽  
Device To Device

Device-to-device (D2D) communications in cellular spectrum have the potential of increasing the spectral and energy efficiency by taking advantage of the proximity and reuse gains. Although several resource allocation (RA) and power control (PC) schemes have been proposed in the literature, a comparison of the performance of such algorithms as a function of the available channel state information has not been reported. In this paper, we examine which large scale channel gain knowledge is needed by practically viable RA and PC schemes for network assisted D2D communications. To this end, we propose a novel near-optimal and low-complexity RA scheme that can be advantageously used in tandem with the optimal binary power control scheme and compare its performance with three heuristics-based RA schemes that are combined either with the well-known 3GPP Long-Term Evolution open-loop path loss compensating PC or with an iterative utility optimal PC scheme. When channel gain knowledge about the useful as well as interfering (cross) channels is available at the cellular base station, the near-optimal RA scheme, termed Matching, combined with the binary PC scheme is superior. Ultimately, we find that the proposed low-complexity RA + PC tandem that uses some cross-channel gain knowledge provides superior performance.

Distance Aware VM allocation process to minimize energy consumption in cloud computing

2019 ◽  
Vol 12 ◽  
Author(s):  
Gurpreet Singh ◽  
Manish Mahajan ◽  
Rajni Mohana
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Physical Machine ◽  
Allocation Process

BACKGROUND: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. AIM AND OBJECTIVE: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. EXECUTION MODEL: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.

scholarly journals Pricing Policies in a Retailer Stackelberg O2O Green Supply Chain

2020 ◽  
Vol 12 (8) ◽  
pp. 3236
Author(s):  
Gan Wan ◽  
Gang Kou ◽  
Tie Li ◽  
Feng Xiao ◽  
Yang Chen
Keyword(s):  
Supply Chain ◽  
Stackelberg Game ◽  
Good Combination ◽  
Green Supply Chain ◽  
Game Model ◽  
Mathematical Function ◽  
Pricing Strategies ◽  
Direct Channel ◽  
Retail Channel ◽  
Offline Channels

Due to the popularization of the concept of “new retailing”, we study a new commercial model named O2O (online-to-offline), which is a good combination model of a direct channel and a traditional retail channel. We analyze an O2O supply chain in which manufacturers are responsible for making green products and selling them through both online and offline channels. The retailer is responsible for all online and offline channels’ orders, and the manufacturer gives the retailer a fixed fee. We construct a mathematical function model and analyze the greenness and pricing strategies of centralized and decentralized settings through the retailer Stackelberg game model. Due to the effects of the double marginalization of supply chain members, we adopt a simple contract to coordinate the green supply chain. The paper’s contributions are that we obtain pricing and greening strategies by taking the cooperation of offline channels and online channels into consideration under the O2O green supply chain environment.

scholarly journals Dynamic SPICE-model of resource allocation in marketing networks

2020 ◽  
Vol 13 ◽  
pp. 8-23
Author(s):  
Movlatkhan T. Agieva ◽  
◽  
Olga I. Gorbaneva ◽  
Keyword(s):  
Resource Allocation ◽  
Stackelberg Game ◽  
Target Audience ◽  
Opinion Leaders ◽  
Stackelberg Equilibrium ◽  
Common Interest ◽  
Theoretic Model ◽  
Goods And Services ◽  
Spice Model ◽  
Private Interests

We consider a dynamic Stackelberg game theoretic model of the coordination of social and private interests (SPICE-model) of resource allocation in marketing networks. The dynamics of controlled system describes an interaction of the members of a target audience (basic agents) that leads to a change of their opinions (cost of buying the goods and services of firms competing on a market). An interaction of the firms (influence agents) is formalized as their differential game in strategic form. The payoff functional of each firm includes two terms: the summary opinion of the basic agents with consideration of their marketing costs (a common interest of all firms), and the income from investments in a private activity. The latter income is described by a linear function. The firms exert their influence not to all basic agents but only to the members of strong subgroups of the influence digraph (opinion leaders). The opinion leaders determine the stable final opinions of all members of the target audience. A coordinating principal determines the firms' marketing budgets and maximizes the summary opinion of the basic agents with consideration of the allocated resources. The Nash equilibrium in the game of influence agents and the Stackelberg equilibrium in a general hierarchical game of the principal with them are found. It is proved that the value of opinion of a basic agent is the same for all influence agents and the principal. It is also proved that the influence agents assign less resources for the marketing efforts than the principal would like.

scholarly journals Financing and pricing strategies of construction supply chain under capital constraint

2021 ◽  
Vol 336 ◽  
pp. 09004
Author(s):  
Yuxin Wen ◽  
Linyi Wu ◽  
Fengmin Yao
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Stackelberg Game ◽  
Distribution Model ◽  
Pricing Strategies ◽  
External Financing ◽  
Pricing Decision ◽  
Decentralized Decision Making ◽  
Internal Financing ◽  
Decision Making Model

Affected by factors such as cost, the financial constraints faced by the supply chain are becoming more and more severe. This paper constructs a financing and pricing decision-making model for the construction supply chain under capital constraints, and uses Stackelberg game theory to analyze and obtain the best financing and pricing strategy for the construction supply chain under the internal and external financing modes. The study found that when centralized decision-making is adopted, there is a profit distribution model that makes the profits obtained by construction developers and contractors greater than the profits obtained in decentralized decision-making; the internal financing model of the construction supply chain is better than external financing, and can enable the construction supply chain get higher profits.

scholarly journals Recommendation with Diversity: Mass Diffusion Model Based on Trust Network and Object Reputation

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yun Bai ◽  
Wandong Cai
Keyword(s):  
Resource Allocation ◽  
Diffusion Model ◽  
Recommendation System ◽  
Small Degree ◽  
Mass Diffusion ◽  
Recommendation Algorithm ◽  
Trust Relationship ◽  
Positive Effects ◽  
Allocation Process ◽  
Resource Allocation Process

The traditional mass diffusion recommendation algorithm only relies on the user’s object collection relationship, resulting in poor recommendation performance for users with small purchases (i.e., small-degree user), and it is difficult to balance the accuracy and diversity of the recommendation system. This paper introduces the trust relationship into the resource allocation process of the traditional mass diffusion algorithm and proposes the Dual Wing Mass Diffusion model (DWMD), which constructs a dual wing graph based on trust relationships and object collection relationships. Implicit trust is mined according to the network structure of the trust relationship and integrated into the resource allocation process, and then merging the positive effects of object reputation on a recommendation through tunable scaling parameters. The user controls the tunable scaling parameter to achieve the best recommendation performance. The experimental results show that the DWMD method significantly improves diversity and novelty while ensuring high accuracy and effectively improves the accuracy and diversity balance. The improved recommendation performance for small-degree users proves that the trust relationship can effectively alleviate the generalized cold start problem of the recommendation algorithm for users who collect a small number of objects.

The Resource Allocation Process

2018 ◽  
pp. 79-93
Author(s):  
Richard Busulwa ◽  
Matthew Tice ◽  
Bruce Gurd
Keyword(s):  
Resource Allocation ◽  
Allocation Process ◽  
Resource Allocation Process
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