Bandwidth allocation and admission control scheme based on Nash bargaining solution for WiMAX systems

An integrated bandwidth allocation and admission control framework for the support of heterogeneous real-time traffic in class-based IP networks

Heterogeneous networks (HetNets) composed of macrocells and small cells are expected to improve the transmission performance of users significantly. Designing efficient resource allocation schemes, specifically, bandwidth allocation and power allocation schemes for HetNets is of particular importance for it may affect user quality of service (QoS) and network performance severely. In this paper, we consider the resource allocation problem of a HetNet composed of a macrocell and a number of femtocells and propose a resource characteristic and user QoS oriented bandwidth and power allocation algorithm for the femto base stations (FBSs) which share spectrum with the macro base station. By taking into account the service requirements of the femto user equipments (FUEs) and the bandwidth resource characteristics of the network, various bandwidth resource allocation schemes are proposed. Particularly, in the case of slightly insufficient bandwidth resource, we propose a bankruptcy game based bandwidth resource allocation algorithm for the FBSs, and solve the bandwidth allocation problem by means of Shapley value method. To stress the tradeoff between data rate and power consumption, we examine the energy efficiency of the FBSs and formulate the power allocation problem as a multi-objective optimization problem with the objectives of maximizing the energy efficiency of all the FBSs. The optimal power allocation strategy can be obtained by solving the optimization problem via ideal point method and generic algorithm. Simulation results demonstrate the efficiency of the proposed algorithm.

Resource allocation and admission control algorithm based on non-cooperation game in wireless mesh networks

In this paper, the problem sharing resource among selfish nodes and cooperative partner selection are considered in wireless networks. Each wireless node can act as not only a source, but also a potential relay in the system model. The cooperative partners are willing to jointly adjust their power levels and channel bandwidth for cooperative relaying so that an extra rate increase can be achieved. In order to bargain joint bandwidth and power allocation (JBPA) between cooperative partners, a two-user Nash bargaining solution (NBS) is proposed. Then, based on Hungarian method, multiple-user bargaining algorithm and multiple-user grouping algorithm are developed to solve partner selection in large network. By using the proposed multi-user algorithm, the optimal coalitions are formed and the rate increase of overall network is also maximized. Simulation results indicate that the total rate increment based on grouping algorithm is fully close to the optimum, and the resource allocation fairness is dependent on how much rate increase its partner can make to. All in all, based on JBPA, each user negotiates with its partner and a fair NBS is achieved. Then the optimal coalition pairs based on the Hungarian method can be determined with a limited centralized control (such as base station) for the whole network efficiency. Index Terms—Relay selection, bandwidth allocation, power control, nash bargaining solution

A unified time-division multiple access (TDMA)based centralized wireless access scheme is proposed for performing the statistical multiplexing of bursty data sources in a wireless packet data network. This scheme combines dynamic bandwidth allocation with admission control and packet conditioning (at the mobile stations) to provide fair bandwidth distribution among bursty data flows with different profile rates (or subscription levels) in an error-prone environment. The dynamic bandwidth allocation policy is credit-based and both the burst-level and the packet-level bandwidth allocations are considered. The performance of the scheme is evaluated using computer simulations for different total subscription levels, for different compositions of flows with different profile rates, and for different channel quality with different channel-error correlation patterns. The simulation results show that the throughput variability among flows with the same level of subscription is considerably small except for long range dependent flows with very high traffic burstiness. The relative throughput fairness among flows with different profile rates can also be achieved. The post facto loss and delay values (i.e., observed average packet delay and average packet loss values) for the flows depend on the corresponding delay tolerance limits of the data bursts, TDMA frame-length, and the wireless link utilization level. The energy efficiency of the wireless access scheme is evaluated in terms of the average transmitter usage time and the average receiver usage time in the mobile stations for both the burst-level and the packet-level bandwidth allocation. The proposed scheme can be used in an adaptive quality-of-service (QoS) framework for dynamically adjusting the QoS for flows in order to accommodate wireless channel errors and user mobility.

The emerging IEEE 802.11e standard for wireless local area networks (WLANs) has been proposed to support quality of service (QoS) by assigning different channel access parameters (CAPs) to different access categories (ACs). As an important part of QoS, an admission control scheme is required to maximally utilize the wireless medium resources and to efficiently admit the upcoming real time traffic while not compromising the QoS of existing traffic. In this paper, we propose a novel admission and congestion control scheme which obtains the admission control parameters through existing analytical model and traffic QoS requirements. It then dynamically updates the CAPs based on periodical monitoring of current channel conditions. Through numerical analysis and extensive simulation, results show that such a scheme could provide the guaranteed QoS for admitted real-time traffic in terms of guaranteed throughput achievement, bounded maximum delay and bounded maximum dropping rate while maintaining good channel utilization

Next generation Passive Optical Network (PON)technology has been evolving to consolidate the metro andaccess networks in order to offer enhanced capacity, highsplit ratio and reduced deployment cost per subscriber.However, transmission of the signals to long distances up to100km leads to increased propagation delay whereas highsplit ratio may lead to long cycle times resulting in largequeue occupancies and long packet delays. In this article, wepresent a delay-constrained admission control mechanismand adapt this scheme to our previously proposed bandwidthallocation technique, namely Periodic GATE Optimization(PGO). We call this new scheme Delay-Constrained PeriodicGATE Optimization (DC-PGO). DC-PGO is designed to runfor multiple service classes as it inherits the advantages ofPGO by periodically building and solving an ILP formulationat the OLT in order to obtain the appropriate creditvalues for the overloaded ONUs. At the ONU side, DCPGOruns an admission control scheme before pushing thearriving packets in the sub-queues. The admission controlscheme uses statistical information consisting of the localdata at the ONU and the previously received GATE messagesfrom the OLT. Through simulations, we show that DC-PGOenhances the performance of multi-threaded polling in longreachEthernet PON when packets of differentiated serviceclasses arrive with pre-specified delay requirements.

In current IaaS datacenters, tenants are suffering unfairness since the network bandwidth is shared in a besteffort manner. To achieve predictable network performance for rented virtual machines (VMs), cloud providers should guarantee minimum bandwidth for VMs or allocate the network bandwidth in a fairness fashion at VM-level. At the same time, the network should be efficiently utilized in order to maximize cloud providers' revenue. In this paper, we model the bandwidth sharing problem as a Nash bargaining game, and propose the allocation principles by defining a tunable base bandwidth for each VM. Specifically, we guarantee bandwidth for those VMs with lower network rates than their base bandwidth, while maintaining fairness among other VMs with higher network rates than their base bandwidth. Based on rigorous cooperative game-theoretic approaches, we design a distributed algorithm to achieve efficient and fair bandwidth allocation corresponding to the Nash bargaining solution (NBS). With simulations under typical scenarios, we show that our strategy can meet the two desirable requirements towards predictable performance for tenants as well as high utilization for providers. And by tuning the base bandwidth, our solution can enable cloud providers to flexibly balance the tradeoff between minimum guarantees and fair sharing of datacenter networks.

In a recent article, McClennen (Synthese 187:65–93, 2012) defends an alternative bargaining theory in response to his criticisms of the standard Nash bargaining solution as a principle of distributive justice in the context of the social contract. McClennen rejects the orthodox concept of expected individual utility maximizing behavior that underlies the Nash bargaining model in favor of what he calls full rationality, and McClennen’s full cooperation bargaining theory demands that agents select the most egalitarian strictly Pareto-optimal distributional outcome that is strictly Pareto-superior to the state of nature. I argue that McClennen’s full cooperators are best described as reasonable agents whose rationality is constrained by moral considerations and that McClennen’s bargaining theory is moralized in this regard. If, by contrast, the orthodox concept of rationality is assumed and plausible assumptions are made about human nature and social cooperation, then a modified version of the standard Nash bargaining solution, which I call the stabilized Nash bargaining solution (Moehler in Utilitas 22:447–473, 2010), is justified. From the perspective of rational agents, the stabilized Nash bargaining solution can accommodate McClennen’s criticisms of the standard Nash bargaining solution in the context of the social contract and, for such agents, it can serve as a principle of distributive justice in deeply morally pluralistic societies.

Network function virtualization represents a revolutionary approach to network service deployment. This software-oriented approach for virtual network functions (VNFs) deployment enables more flexible and dynamic network services to meet diversified demands. To minimize the execution time of all VNFs in service function chains, VNF scheduling must be addressed. In this paper, we improve upon the flexible job-shop model by introducing the process of bandwidth allocation. First, we propose a multilayer encoding genetic algorithm to solve the VNF scheduling model. In addition, we design a distributed method for bandwidth allocation based on the Nash bargaining solution. Finally, by combining the genetic algorithm with distributed bandwidth allocation, we present a heuristic algorithm that solves the VNF scheduling problem in one stage. Using a multilayer encoding genetic algorithm, we simplify the constraints of the VNF scheduling problem and reduce its time complexity. At the same time, our Nash game solution refines the granularity of bandwidth allocation to further reduce the transmission delay between VNFs. The effectiveness of our proposed heuristic algorithm is verified through numerical evaluation. Compared with existing approaches, our method exhibits shorter scheduling time and reduces CPU time by 45% in simulated scenarios.

Light-trail is a new technique, which is generalized from the concept of light path. It possesses two major advantages; one is bandwidth sharing and the other one is efficient bandwidth utilization. While a lot of literature investigate the design of routing algorithms, however, the performance of light-trail networks relies on the efficiency of dynamic bandwidth allocation schemes. In this paper, we proposed an efficient dynamic bandwidth allocation scheme named Demand and Delay latency-aware with Two-round Evaluation (DDTE). With a double-check mechanism, DDTE can allocate bandwidth more accurately and efficiently in light-trail networks. In addition to the bandwidth allocation scheme, we proposed a setup/release scheme as well. To demonstrate the superiority of DDTE, we developed a simulation suite composed of four topologies with four scenarios and gathered comparisons between DDTE and the previously proposed Dual Auction algorithm. From simulation results we can observe the significant improvement achieved by DDTE and the setup/release mechanism in both of blocking and delay performance.

In this paper, we investigate the bandwidth and power allocation problem in remote radio head cluster (RRHC)-based millimeter wave (mm-wave) massive MIMO heterogeneous cloud radio access networks with limited fronthaul capacity. The coordinated multipoint transmission is applied in each RRHC for cancelling the intra-cluster interference. To avoid the inter-tier interference, distinct bandwidths are allocated to macro base station and RRHs. Following this, we formulate a bandwidth and power allocation optimization problem to maximize the downlink weighted sum rate of the system subject to per-RRHC power and fronthaul capacity constraints, which is a non-convex optimization problem and is difficult to directly solve. Next, we fix the bandwidth allocation and the original problem can be divided into two independent optimization problems, i.e., the weighted sum rate maximization problems of MUs and RRH users, respectively. For the former, the convex optimization technique can be used to solve it. As for the latter, a two-loop iterative algorithm is proposed to deal with it. Specifically, we propose the price-based outer iteration to control the fronthaul capacity and the weighted minimum mean square error-based inner iteration to obtain the power allocation. To this end, a 1-D search method is adopted to find the optimal bandwidth allocation. Finally, numerical results are conducted to verify the effectiveness of the proposed algorithms under different parameters.

Wireless sensor networks are an integral part of Industrial Revolution 4.0. The traffic efficiency of any network depends on link failures and available bandwidth. This paper discusses route discovery in case of a link failure and a control system approach for efficient bandwidth allocation. Control system approach has been attempted to address the twin issues of link failure and bandwidth allocation. The main function of Wireless networks is to gather information about various types of parameters, notably process variables in industries, air quality variables in ambient air monitoring, weather forecasting, cyclonic alertness, congestion and other parameters in traffic routing. The outputs from WSN are feed into Plant systems either to display or control the variables. Naturally for efficient, reliable display and control of parameters continuous supply of information from WSN to Plant systems is important. Hence improving Quality of Service Parameters like signal to noise ratio, fault tolerance, Band width allocation, and stability and load balancing is vital while designing a WSN. We discuss about two QOS parameters in WSN namely Fault Tolerance in case of Link failures and Efficient Bandwidth Allocation. Links here are wireless in nature.

In this paper, the optimization of network performance to support the deployment of federated learning (FL) is investigated. In particular, in the considered model, each user owns a machine learning (ML) model by training through its own dataset, and then transmits its ML parameters to a base station (BS) which aggregates the ML parameters to obtain a global ML model and transmits it to each user. Due to limited radio frequency (RF) resources, the number of users that participate in FL is restricted. Meanwhile, each user uploading and downloading the FL parameters may increase communication costs thus reducing the number of participating users. To this end, we propose to introduce visible light communication (VLC) as a supplement to RF and use compression methods to reduce the resources needed to transmit FL parameters over wireless links so as to further improve the communication efficiency and simultaneously optimize wireless network through user selection and resource allocation. This user selection and bandwidth allocation problem is formulated as an optimization problem whose goal is to minimize the training loss of FL. We first use a model compression method to reduce the size of FL model parameters that are transmitted over wireless links. Then, the optimization problem is separated into two subproblems. The first subproblem is a user selection problem with a given bandwidth allocation, which is solved by a traversal algorithm. The second subproblem is a bandwidth allocation problem with a given user selection, which is solved by a numerical method. The ultimate user selection and bandwidth allocation are obtained by iteratively compressing the model and solving these two subproblems. Simulation results show that the proposed FL algorithm can improve the accuracy of object recognition by up to 16.7% and improve the number of selected users by up to 68.7%, compared to a conventional FL algorithm using only RF.

In this paper, a priority-based bandwidth allocation in the context of heterogeneous wireless network is studied. By utilizing the mobile terminals with multi-homing capability, which simultaneously accesses bandwidth from different wireless network, a novel utility-based bandwidth allocation approach is introduced. An essential factor is the priority mechanism considering the services priority classification, which means the higher-priority service request can be guaranteed on bandwidth allocation prior to the lower-priority service request. The utility function is used to distinguish both the service priority and the network subscribers compared to other users. The bandwidth allocation problem is formulated as an optimization problem and solved by Lagrangian duality. Numerical results reveal that the optimal bandwidth allocation algorithm can differentiates the priority-based service and utilize all available resources within the network capacity.