Concept Of Nash Bargaining Solution Research Articles

Achievable rate optimization for space–time block code-aided cooperative NOMA with energy harvesting

Cooperative non-orthogonal multiple access (C-NOMA) is one of the promising techniques that support the massive connectivity of users and improve the fair allocation of network resources. However, the performance of C-NOMA networks is restricted due to the complexity introduced by multiple user-assisted relays coupled with their energy consumption bottleneck. In this paper, we develop a low-complexity network framework with the main goal of achieving a fair and optimal achievable rate. For the network model, we adopt a Space–time block code transmission scheme to increase the spatial diversity of the network with a minimum number of relays. Further to this, we consider a radio frequency-based energy harvesting technique known as simultaneous wireless information and power transfer in order to compensate for energy expended by the relays over the cooperating transmission links. Based on the proposed network model, we solve the achievable rate maximization problem from the perspective of joint optimal power allocation factor and power splitting ratio. Due to the non-convex nature of the problem and to also ensure fair resource allocation, we propose a low-complexity solution based on the concept of Nash bargaining solution. Further to this, we evaluate the achievable rate and energy efficiency of the system. Simulation results show that our proposed solution outperforms the baseline approaches.

DEA-based Nash bargaining approach to merger target selection

Mergers and Acquisitions (M&As) are important business strategies in any industry, as they allow the parties involved to achieve, for example, higher market share, profits and influence in one or more industries. In any M&A activity, the key challenge for an acquirer company is to select the target company that can most improve its performance through synergy. The goal of this research is thus to help acquirer companies model and optimally solve their merger target selection problems (MTSPs) in both horizontal integration and vertical integration settings. We apply both a data envelopment analysis (DEA) based performance evaluation framework and the Nash bargaining solution concept to mathematically model an acquirer company's MTSPs under the two types of integration settings. To the best of our knowledge, the proposed new models are the first DEA-based Nash bargaining models in the literature to help acquirer companies obtain their most desired target companies. Finally, this research provides numerical examples, including real-life examples, to illustrate various aspects and implementation details of the two types of models.

A Game-based Thermal-Aware Resource Allocation Strategy for Data Centers

Data centers (DC) host a large number of servers, computing devices and computing infrastructure, which incur significant electricity / energy. This also results in huge amount of heat produced, which if not addressed can lead to overheating of computing devices in the DC. In addition, temperature mismanagement can lead to thermal imbalance within the DC environment, which may result in the creation of hotspots. The energy consumed during the life of a hotspot is greater than the energy saved during computation. Hence, the thermal imbalance impacts on the efficiency of the cooling mechanism installed inside the DC, which can result in high energy consumption. One popular strategy to minimize energy consumption is to optimize resource allocation within the DC. However, existing scheduling strategies do not consider the ambient effect of the surrounding nodes at the time of job allocation. Moreover, thermal-aware resource scheduling as an optimization problem is a topic that is relatively understudied in the literature. Therefore, in this research, we propose a novel Game-based Thermal-Aware Resource Allocation (GTARA) strategy to reduce the thermal imbalances within the DC. Specifically, we use cooperative game theory with a Nash-bargaining solution concept to model the resource allocation as an optimization problem, where the user jobs are assigned to the computing nodes based on their thermal profiles and their potential effect on the surrounding nodes. This allows us to improve the thermal balance and avoid the hotspots. We then demonstrate the effectiveness of GTARA, TACS, TASA, and FCFS, in terms of minimizing thermal imbalance and the hotspots.

A Game-Theoretic Approach for Electric Power Distribution during Power Shortage: A Case Study in Pakistan

Power supply is the cornerstone for the sustainable socio-economic development of any country. In a developing country like Pakistan, shortage of power supply is the main obstacle to its economic growth, making it a disputed and contested resource among different administrative units/provinces and socio-economic sectors. A key challenge is allocating the limited available power among provinces with conflicting and competing needs amid the supply-demand gap. In this research, the allocation of energy during a shortage is considered as a game-theoretic bankruptcy problem. Five bankruptcy rules namely the Proportional Rule, Constraint Equal Award Rule, Constraint Equal Loss Rule, Talmud Rule and Piniles Rule are used for power allocation among the provinces of Pakistan. Each province is characterized by its power demand. A new framework is also proposed for power allocation, which synthesizes the Nash bargaining solution concept with bankruptcy theory to resolve power-related disputes among the four provinces within Pakistan. Additionally, a new method is introduced in this study to compare and contrast the different allocation rules. The results suggest that the basic power demands of the provinces can be satisfied by the proposed disagreement points among the provinces, and the bargaining weights can highlight the role of different levels of power claims, lengths of transmission lines, and variations in population among provinces. The findings also suggest that, due to the lowest dispersion, the proportionate rule is the most suitable method for power allocation among the provinces. The paper combines relevant bankruptcy rules with Nash bargaining theory to propose an algorithm for addressing power sector supply-demand mismatches in Pakistan.

An Agent-Based Hierarchical Bargaining Framework for Power Management of Multiple Cooperative Microgrids

In this paper, we propose an agent-based hierarchical power management model in a power distribution system composed of several microgrids (MGs). At the lower level of the model, multiple MGs bargain with each other to cooperatively obtain a fair, and Pareto-optimal solution to their power management problem, employing the concept of Nash bargaining solution and using a distributed optimization framework. At the highest level of the model, a distribution system power supplier, e.g., a utility company, interacts with both the cluster of the MGs and the wholesale market. The goal of the utility company is to facilitate power exchange between the regional distribution network consisting of multiple MGs and the wholesale market to achieve its own private goals. The power exchange is controlled through dynamic energy pricing at the distribution level, at the day-ahead and real-time stages. To implement energy pricing at the utility company level, an iterative machine learning mechanism is employed, where the utility company develops a price-sensitivity model of the aggregate response of the MGs to the retail price signal through a learning process. This learned model is then used to perform optimal energy pricing. To verify its applicability, the proposed decision model is tested on a system with multiple MGs, with each MG having different load/generation data.

A Market-Based Resilient Power Management Technique for Distribution Systems with Multiple Microgrids Using a Multi-Agent System Approach

In this paper, we present a market-based resilient power management procedure for electrical distribution systems consisting of multiple cooperative MiroGrids (MGs). Distributed optimization is used to find the optimal resource allocation for the multiple MG system, while maintaining the local and global constraints, including keeping the voltage levels of the micro-sources within bounds. The proposed method is based on probabilistic reasoning in order to consider the uncertainty of the decision model in preparation for expected extreme events and in case of unit failure, to improve the resiliency of the system. Basically, the power management problem formulation is a multiobjective optimization problem, which is solved using the concept of Nash Bargaining Solution (NBS). The simulation results show that the proposed method is able to improve the resiliency of the system and prepare it for extreme events and unit failure, by increasing power reserve and modifying the operating point of the system to maintain voltage and power constraints across the MGs.

A Game Theoretic Framework for Congestion Control in Named Data Networking

As an essential building block of the Named Data Networking (NDN) architecture, congestion mechanism has been the focus of much attention. The unique features of NDN architecture bring forward distinct requirements for congestion control from its Internet Protocol (IP) counterpart. In this paper, we present a game theoretic framework for flow rate control in NDN based on the concept of Nash bargaining solution from cooperative game theory, and propose a distributed, flow-aware and hop-by-hop congestion control mechanism on a solid analytical basis. In addition, we developed a possible implementation in ndnSIM and performed various experiments with different settings to study the behavior of our scheme. We have seen that the proposed congestion control mechanism performs as designed, and significantly enhances the network performance. DOI: http://dx.doi.org/10.5755/j01.itc.46.4.15680

Preventing competition using side payments: when non-neutrality creates barriers to entry

Network neutrality is often advocated by content providers, stressing that side payments to Internet Service Providers would hinder innovation. However, we also observe some content providers actually paying those fees. This paper intends to explain such behaviors through economic modeling, illustrating how side payments can be a way for an incumbent content provider to prevent new competitors from entering the market. We investigate the conditions under which the incumbent can benefit from such a barrier-to-entry, and the consequences of that strategic behavior on the other actors: content providers, users, and the Internet Service Provider. We also describe how the Nash bargaining solution concept can be used to determine the side payment.

Water Allocation in Transboundary River Basins under Water Scarcity: a Cooperative Bargaining Approach

Transboundary river basins are one of the main sources of fresh water which are facing water scarcity. When transboundary water is contested not only the allocation outcomes matter but also the allocation process should possess a certain desirable properties such as flexibility and sustainability. Therefore designing a mechanism that possesses these desirable characteristics and allocates the contested water resource is important as well. This article proposed a water allocation framework by combining the bankruptcy theory with asymmetric Nash bargaining solution concept for solving the water sharing problem in transboundary river basins under scarcity. Furthermore, the allocation framework was applied to the Nile river basin and to a hypothetical water scarce transboundary river basin. The results obtained were then compared with the allocation outcomes from classical bankruptcy allocation rules. The results showed that the proposed method can provide insights which could be useful for obtaining water allocation outcomes which are easier to implement and enforce under water scarce conditions.

Cooperative and non-cooperative Nash solution for linear supply function equilibrium game

In this paper, the competition among suppliers in an oligopolistic market is studied using supply function equilibrium model. Since the decision of each supplier affects other suppliers’ profit, we have a game problem rather than optimization. In the proposed supply function equilibrium game, both of the cooperative and non-cooperative behaviors of the players are studied. In non-cooperative case, the Nash equilibrium point of the game is obtained for n-player in constant and price sensitive demand cases. In cooperative game, the concept of Nash bargaining solution is utilized to study the collusive behavior of the suppliers. The convexity and closeness of the players’ payoff set in supply function equilibrium model is proved as the necessary and sufficient condition for existence and uniqueness of the solution and then, the mathematical formulation of Nash bargaining solution is derived. In addition, the cooperative and non-cooperative behavior of players is compared over a wide range of parameters in a case study.

Cooperative content distribution and traffic engineering in an ISP network

Traditionally, Internet Service Providers (ISPs) make profit by providing Internet connectivity, while content providers (CPs) play the more lucrative role of delivering content to users. As network connectivity is increasingly a commodity, ISPs have a strong incentive to offer content to their subscribers by deploying their own content distribution infrastructure. Providing content services in an ISP network presents new opportunities for coordination between traffic engineering (to select efficient routes for the traffic) and server selection (to match servers with subscribers). In this work, we develop a mathematical framework that considers three models with an increasing amount of cooperation between the ISP and the CP. We show that separating server selection and traffic engineering leads to sub-optimal equilibria, even when the CP is given accurate and timely information about the ISP's network in a partial cooperation. More surprisingly, extra visibility may result in a less efficient outcome and such performance degradation can be unbounded. Leveraging ideas from cooperative game theory, we propose an architecture based on the concept of Nash bargaining solution. Simulations on realistic backbone topologies are performed to quantify the performance differences among the three models. Our results apply both when a network provider attempts to provide content, and when separate ISP and CP entities wish to cooperate. This study is a step toward a systematic understanding of the interactions between those who provide and operate networks and those who generate and distribute content.

A Cooperative Game Theoretical Technique for Joint Optimization of Energy Consumption and Response Time in Computational Grids

With the explosive growth in computers and the growing scarcity in electric supply, reduction of energy consumption in large-scale computing systems has become a research issue of paramount importance. In this paper, we study the problem of allocation of tasks onto a computational grid, with the aim to simultaneously minimize the energy consumption and the makespan subject to the constraints of deadlines and tasks' architectural requirements. We propose a solution from cooperative game theory based on the concept of Nash bargaining solution. In this cooperative game, machines collectively arrive at a decision that describes the task allocation that is collectively best for the system, ensuring that the allocations are both energy and makespan optimized. Through rigorous mathematical proofs we show that the proposed cooperative game in mere O(n mlog(m)) time (where n is the number of tasks and m is the number of machines in the system) produces a Nash bargaining solution that guarantees Pareto-optimally. The simulation results show that the proposed technique achieves superior performance compared to the greedy and linear relaxation (LR) heuristics, and with competitive performance relative to the optimal solution implemented in LINDO for small-scale problems.

Energy Efficient Resource Allocation in Distributed Computing Systems

The problem of mapping tasks onto a computational grid with the aim to minimize the power consumption and the makespan subject to the constraints of deadlines and architectural requirements is considered in this paper. To solve this problem, we propose a solution from cooperative game theory based on the concept of Nash Bargaining Solution. The proposed game theoretical technique is compared against several traditional techniques. The experimental results show that when the deadline constraints are tight, the proposed technique achieves superior performance and reports competitive performance relative to the optimal solution. measures. Power management can be achieved by two methods. The Dynamic Power Management (DPM) (27) approach brings a processor into a power-down mode, where only certain parts of the computer system (e.g., clock generation and time circuits) are kept running, while the processor is in an idle state. The Dynamic Voltage Scaling (DVS) (34) approach exploits the convex relation between the CPU supply voltage and power consumption. The rationale behind DVS technique is to stretch out task execution time through CPU frequency and voltage reduction. Power-aware resource allocation using DVS can be classified as static and dynamic techniques. Static techniques are applied at design time by allocating and scheduling resources using off-line approaches, while dynamic techniques control the runtime behavior of the systems to reduce power consumption. The traditional resource allocation and scheduling theory deals with fixed CPU speed, and hence cannot be directly applied to this situation. In this paper, we study the problem of power-aware task allocation (PATA) for assigning a set of tasks onto a computational grid each equipped with DVS feature. The PATA problem is formulated as multi- constrained multi-objective extension of the Generalized Assignment Problem (GAP). PATA is then solved using a novel solution from cooperative game theory based on the celebrated Nash Bargaining Solution (NBS) (22); we shall acronym this solution concept as NBS-PATA. The rest of the paper is organized as follows: A brief discussion of related work is presented in Section II. The PATA problem formulation and background information are discussed in Section III. In Section IV, we model a cooperative game played among the machines for task allocation with the objective to minimize power consumption and makespan, simultaneously. Experimental results and concluding remarks are provided in Sections V and VI, respectively.

Channel‐relay price pair: towards arbitrating incentives in wireless ad hoc networks

AbstractCooperation in wireless ad hoc networks has twofold implications. First, each wireless node does not excessively and greedily inject traffic to the shared wireless channel. Second, intermediate nodes voluntarily relay traffic for upstream nodes towards the destination at the cost of its own private resource. Such an assumption supports almost all existing research when it comes to protocol design in ad hoc networks. We believe that without appropriate incentive mechanisms, the nodes are inherently selfish (unwilling to contribute its private resource to relay traffic) and greedy (unfairly sharing the wireless channel). In this paper, we present a price pair mechanism to arbitrate resource allocation and to provide incentives simultaneously such that cooperation is promoted and the desired global optimal network operating point is reached by convergence with a fully decentralized self‐optimizing algorithm. Such desired network‐wide global optimum is characterized with the concept of Nash bargaining solution (NBS), which not only provides the Pareto optimal point for the network, but is also consistent with the fairness axioms of game theory. We simulate the price pair mechanism and report encouraging results to support and validate our theoretical claims. Copyright © 2006 John Wiley & Sons, Ltd.

Tax structure and the choice of compensation system

We evaluate firm/union preferences for pure wage and profit sharing compensation under the generalized Nash bargaining solution concept. Emphasis is given to the effects of taxes on income as well as profit and payroll taxes. Taxes are shown to play an important role and their assessment is extended to embrace the effects of payroll tax ceilings. Finally, we consider whether differential labour/capital taxes and subsidies might influence the parties' choice of compensation system.