Resource Allocation Problem In Networks Research Articles

Dynamic femtocell resource allocation for managing inter‐tier interference in downlink of heterogeneous networks

This study investigates the downlink resource allocation problem in orthogonal frequency division multiple access heterogeneous networks consisting of macrocells and femtocells sharing the same frequency band. The focus is to devise optimised policies for femtocells' access to the shared spectrum, in terms of femtocell transmissions, in order to maximise femto-users (FUEs) sum data rate while ensuring that certain level of quality of service (QoS) for the macro-cell users in the vicinity of femtocells is provided. The optimal solution to this problem is obtained by employing the well-known dual Lagrangian method and the optimal femtocell transmit power and resource allocation solution is derived in detail. However, the optimal solution introduces high computational complexity. To this end, a heuristic solution to the problem is proposed. The algorithms to implement both optimal and efficient suboptimal schemes in a practical system are also given in detail while their complexity is compared. Simulation results show that proposed dynamic resource allocation scheme (a) ensures the macro-users QoS requirements compared with the Reuse-1 scheme, where femtocells are allowed to transmit at full power and bandwidth; (b) can maintain FUE data rates at high levels; (c) provides performance close to the optimal solution, while introducing much lower complexity.

Fair Scheduling Policies Exploiting Multiuser Diversity in Cellular Systems With Device-to-Device Communications

We consider the resource allocation problem in cellular networks which support device-to-device communications (D2D). For systems that enable D2D via only orthogonal resource sharing, we propose and analyze two resource allocation policies that guarantee access fairness among all users, while taking advantage of multi-user diversity and local D2D communications, to provide marked improvements over existing cellular-only policies. The first policy, the Cellular Fairness Scheduling (CFS) Policy, provides the simplest D2D extension to existing cellular systems, while the second policy, the D2D Fairness Scheduling (DFS) Policy, harnesses maximal performance from D2D-enabled systems under the orthogonal sharing setting. For even higher spectral efficiency, cellular systems with D2D can schedule the same frequency resource for more than one D2D pairs. Under this non-orthogonal sharing environment, we propose a novel group scheduling policy, the Group Fairness Scheduling (GFS) Policy, that exploits both spatial frequency reuse and multiuser diversity to deliver dramatic improvements to system performance with perfect fairness among the users, regardless of whether they are cellular or D2D users.

Resource allocation in multi-class dynamic PERT networks with finite capacity

In this paper, the resource allocation problem in multi-class dynamic PERT networks with finite capacity of concurrent projects (COnstant Number of Projects In Process (CONPIP)) is studied. The dynamic PERT network is modeled as a queuing network, where new projects from different classes (types) are generated according to independent Poisson processes with different rates over the time horizon. Each activity of a project is performed at a devoted service station with one server located in a node of the network, whereas activity durations for different classes in each service station are independent and exponentially distributed random variables with different service rates. Indeed, the projects from different classes may be different in their precedence networks and also the durations of the activities. For modeling the multi-class dynamic PERT networks with CONPIP, we first consider every class separately and convert the queueing network of every class into a proper stochastic network. Then, by constructing a proper finite-state continuous-time Markov model, a system of differential equations is created to compute the project completion time distribution for any particular project. The problem is formulated as a multi-objective model with three objectives to optimally control the resources allocated to the service stations. Finally, we develop a simulated annealing (SA) algorithm to solve this multi-objective problem, using the goal attainment formulation. We also compare the SA results against the results of a discrete-time approximation of the original optimal control problem, to show the effectiveness of the proposed solution technique.

Resource Allocation in Multiuser Multi-Carrier Cognitive Radio Network via Game and Supermarket Game Theory: Survey, Tutorial, and Open Research Directions

In this tutorial, we integrate the concept of cognitive radio technology into game theory and supermarket game theory to address the problem of resource allocation in multiuser multicarrier cognitive radio networks. In addition, multiuser multicarrier transmission technique is chosen as a candidate to study the resource allocation problem via game and supermarket game theory. This tutorial also includes various definitions, scenarios and examples related to (i) game theory (including both non-cooperative and cooperative games), (ii) supermarket game theory (including pricing, auction theory and oligopoly markets), and (iii) resource allocation in multicarrier techniques. Thus, interested readers can better understand the main tools that allow them to model the resource allocation problem in multicarrier networks via game and supermarket game theory. In this tutorial article, we first review the most fundamental concepts and architectures of CRNs and subsequently introduce the concepts of game theory, supermarket game theory and common solution to game models such as the Nash equilibrium and the Nash bargaining solution. Finally, a list of related studies is highlighted and compared in this tutorial.

Discrete polynary coding immune clonal selection‐based joint subcarrier and power allocation in uplink cognitive OFDM network

SummaryCognitive radio has been considered to be one of the main technologies to solve the problem of low spectrum utilization, while the adaptive allocation of network resource is one of the key technologies. A discrete polynary coding immune clonal selection (DPICS)‐based joint subcarrier and power allocation algorithm is proposed to solve the resource allocation problem in uplink cognitive OFDM networks. The novelties of DPICS include the following: A unique coding method is adopted to deal with multi‐value discrete variables. Compared with the traditional methods, the proposed method can acquire the shortest code. Meanwhile, the constraints of the subcarrier allocation are avoided. A heuristic mutation scheme is used to direct the mutation. Subcarriers are reallocated randomly to the secondary users with larger homotactic noise, which has a large probability to produce the optimal solution and improves the searching process. Subcarriers and power are allocated simultaneously, which is different with the traditional biphasic resource allocation algorithms. The biphasic resource allocation algorithms cannot acquire the subcarrier allocation result and power allocation result simultaneously, which makes the final result imprecise. The proposed algorithm avoids this situation and improves the accuracy of the final result. Compared with state‐of‐the‐art algorithms, the proposed algorithm is shown as effective by simulation results. Copyright © 2014 John Wiley & Sons, Ltd.

Cross-layer resource allocation in wireless multi-hop networks with outdated channel state information

The cross-layer resource allocation problem in wireless multi-hop networks (WMHNs) has been extensively studied in the past few years. Most of these studies assume that every node has the perfect channel state information (CSI) of other nodes. In practical settings, however, the networks are generally dynamic and CSI usually becomes outdated when it is used, due to the time-variant channel and feedback delay. To deal with this issue, we study the cross-layer resource allocation problem in dynamic WMHNs with outdated CSI under channel conditions where there is correlation between the outdated CSI and current CSI. Two major contributions are made in this work: (1) a closed-form expression of conditional average capacity is derived under the signal-to-interference-plus-noise ratio (SINR) model; (2) a joint optimization problem of congestion control, power control, and channel allocation in the context of outdated CSI is formulated and solved in both centralized and distributed manners. Simulation results show that the network utility can be improved significantly using our proposed algorithm.

Radio Resource Allocation for Collaborative OFDMA Relay Networks with Imperfect Channel State Information

This paper addresses the resource allocation problem in collaborative relay-assisted OFDMA networks. Recent works on the subject usually ignored either the selection of relays, asymmetry of the source-to-relay and relay-to-destination links or the imperfections of channel state information. In this article we take into account all these together and our focus is two-fold. Firstly, we consider the problem of asymmetric radio resource allocation, where the objective is to maximize the system throughput of the source-to-destination link under various constraints. In particular, we consider optimization of the set of collaborative relays and link asymmetries together with subcarrier and power allocation. Using a dual approach, we solve each sub-problem in an asymptotically optimal and alternating manner. Secondly, we pay attention to the effects of imperfections in the channel-state information needed in resource allocation decisions. We derive theoretical expressions for the solutions and illustrate them through simulations. The results validate clearly the additional performance gains through an asymmetric cooperative scheme compared to the other recently proposed resource allocation schemes.

Stable families of coalitions for network resource allocation problems

A very common question appearing in resource management is: what is the optimal way of behaviour of the agents and distribution of limited resources. Is any form of cooperation more preferable strategy than pure competition? How cooperation can be treated in the game theoretic framework: just as one of a set of Pareto optimal solutions or cooperative game theory is a more promising approach? This research is based on results proving the existence of a non-empty K -core, that is, the set of allocations acceptable for the family K of all feasible coalitions, for the case when this family is a set of subtrees of a tree. A wide range of real situations in resource management, which include optimal water, gas and electricity allocation problems, can be modeled using this class of games. Thus, the present research is pursuing two goals: 1. optimality and 2. stability.Firstly, we suggest to players to unify their resources and then we optimize the total payoff using some standard LP technique. The same unification and optimization can be done for any coalition of players, not only for the total one. However, players may object unification of resources. It may happen when a feasible coalition can guarantee a better result for every coalitionist. Here we obtain some stability conditions which ensure that this cannot happen for some family K. Such families were characterized by Boros, Gurvich and Vasin [4] as Berge’s normal hypergraphs. Thus, we obtain a solution which is optimal and stable. From practical point of view, we suggest a distribution of profit that would cause no conflict between players.

Stable families of coalitions for network resource allocation problems

A very common question appearing in resource management is: what is the optimal way of behaviour of the agents and distribution of limited resources. Is any form of cooperation more preferable strategy than pure competition? How cooperation can be treated in the game theoretic framework: just as one of a set of Pareto optimal solutions or cooperative game theory is a more promising approach? This research is based on results proving the existence of a non-empty K -core, that is, the set of allocations acceptable for the family K of all feasible coalitions, for the case when this family is a set of subtrees of a tree. A wide range of real situations in resource management, which include optimal water, gas and electricity allocation problems, can be modeled using this class of games. Thus, the present research is pursuing two goals: 1. optimality and 2. stability.Firstly, we suggest to players to unify their resources and then we optimize the total payoff using some standard LP technique. The same unification and optimization can be done for any coalition of players, not only for the total one. However, players may object unification of resources. It may happen when a feasible coalition can guarantee a better result for every coalitionist. Here we obtain some stability conditions which ensure that this cannot happen for some family K. Such families were characterized by Boros, Gurvich and Vasin [4] as Berge’s normal hypergraphs. Thus, we obtain a solution which is optimal and stable. From practical point of view, we suggest a distribution of profit that would cause no conflict between players.

A Fairness Relation Based on the Asymmetric Choquet Integral and Its Application in Network Resource Allocation Problems

The recent problem of network resource allocation is studied where pairs of users could be in a favourable situation, given that the allocation scheme is refined by some add-on technology. The general question here is whether the additional effort can be effective with regard to the user’s experience of fairness. The computational approach proposed in this paper to handle this question is based on the framework of relational optimization. For representing different weightings for different pairs of users, the use of a fuzzy measure appears to be reasonable. The generalized Choquet integrals are discussed from the viewpoint of representing fairness and it is concluded that the asymmetric Choquet integral is the most suitable approach. A binary relation using the asymmetric Choquet integral is proposed. In case of a supermodular fuzzy measure, this is a transitive and cycle-free relation. The price of fairness with regard to a wireless channel allocation problem taking channel interference into account is experimentally studied and it can be seen that the asymmetric on relation actually selects allocations that perform on average between maxmin fairness and proportional fairness, and being more close to maxmin fairness as long as channel interference is not high.

Joint Traffic Splitting and Spectrum Resource Allocation in Heterogeneous Cognitive Wireless Networks

This paper investigates the resource allocation problem in heterogeneous cognitive wireless networks (HCWNs), with the objective of improving the end-to-end quality of service (QoS) performance for the communication session which is based on multiple radio access technologies (Multi-RATs). The cognitive wireless networks (CWNs) share spectrum resource with primary users. The users with Multi-RATs applied transmit data through multiple paths by splitting the arrival packets to different access points in HCWNs. A mathematical model for minimizing the end-to-end delay with joint consideration of traffic splitting and spectrum resource allocation is formulated, and an algorithm is proposed by iteratively optimizing the traffic splitting and spectrum resource allocation parameters. During each iteration in the proposed algorithm, a tradeoff between the performance and complexity can be achieved. Simulation results show that the proposed algorithm iteratively decreases the end-to-end delay and outperforms conventional algorithms in QoS performance and spectrum utilization.

Resource allocation with dynamic substrate network in data centre networks

The resource allocation problem in data centre networks refers to a map of the workloads provided by the cloud users/tenants to the Substrate Network (SN) which are provided by the cloud providers. Existing studies consider the dynamic arrival and departure of the workloads, while the dynamics of the substrate are ignored. In this paper, we first propose the resource allocation with the dynamic SN, and denote it as GraphMap-DS. Then, we propose an efficient mapping algorithm for GraphMap-DS. The performance of the proposed algorithm is evaluated by performing simulation experiments. Our results show that the proposed algorithm can effectively solve the GraphMap-DS.

VeSense: High-performance and energy-efficient vehicular sensing platform

Although vehicular sensing where mobile users in vehicles continuously gather, process, and share location-sensitive and context-sensitive sensor data (e.g., street images, road condition, traffic flow) is emerging, little effort has been investigated in a model-based energy-efficient network paradigm of sensor information sharing in vehicular environments. Upon these optimization frameworks, a suite of optimization subproblems: a program partitioning and network resource allocation problem, we propose a distributed vehicular sensing platform, called VeSense where mobile users in vehicles publish/access sensor data via a cloud computing-based distributed P2P overlay network. The key objective is to satisfy the vehicular sensing application’s quality of service requirements by modeling each subsystem: mobile clients, wireless network medium, and distributed cloud services. By simulations based on experimental data, we present the proposed system can achieve up to 37 times more energy-efficient and 73 times faster compared to a standalone mobile application, in various vehicular sensing scenarios applying a realistic mobility model.

Energy‐efficient resource allocation for OFDMA networks with sleep mode

Investigated is the resource allocation problem in OFDMA networks by introducing the sleep mode. In contrast to the existing literature, which mainly focuses on improving network throughput by increasing energy consumption energy efficient resource allocation with sleep mode is considered under which some components of the transmitter can be shut down to save energy. A mix integer programming problem is proposed to minimise energy consumption while satisfying the average data rate requirement. The existence of a unique globally optimal resource allocation solution is demonstrated and an iterative algorithm is developed to obtain it. The simulation result shows that the proposed algorithm can effectively save total energy consumption especially when the network is under low load condition.

Energy-Efficient Resource Allocation for Heterogeneous Cognitive Radio Networks with Femtocells

Both cognitive radio and femtocell have been considered as promising techniques in wireless networks. However, most of previous works are focused on spectrum sharing and interference avoidance, and the energy efficiency aspect is largely ignored. In this paper, we study the energy efficiency aspect of spectrum sharing and power allocation in heterogeneous cognitive radio networks with femtocells. To fully exploit the cognitive capability, we consider a wireless network architecture in which both the macrocell and the femtocell have the cognitive capability. We formulate the energy-efficient resource allocation problem in heterogeneous cognitive radio networks with femtocells as a Stackelberg game. A gradient based iteration algorithm is proposed to obtain the Stackelberg equilibrium solution to the energy-efficient resource allocation problem. Simulation results are presented to demonstrate the Stackelberg equilibrium is obtained by the proposed iteration algorithm and energy efficiency can be improved significantly in the proposed scheme.

Dimensioning and Energy Efficiency of Multi-Rate Metro Rings

Due to the ever-growing network capacity demand, energy-efficient and scalable networking solutions are needed in all networking segments. This paper focuses on the metro segment and compares the power consumption for four different technologies: Ethernet, Reconfigurable Optical Add-Drop Multiplexers (ROADM), Optical Transport Networks (OTN), and an optical packet switching (OPS) solution called Packet Optical Add-Drop Multiplexers (“POADM”), which was recently proposed. A novel resource allocation strategy for POADM-based OPS multi-rate rings is proposed for the first time. The multi-rate POADM ring network resource allocation problem is shown to be NP-hard and solved using both an MILP formulation and a heuristic. The MILP formulation and the heuristic are compared, along with dimensioning results for Ethernet and ROADM/OTN ring networks. Results show that multi-rate POADM rings achieve savings up to 10% with respect to single-rate design and can be enabled by assigning a different rate to each wavelength. Furthermore, POADM rings can be up to 5 times more energy-efficient than Ethernet rings and 30% than ROADM rings, assuming similar network efficiencies.

A Utility-Based Mechanism for Broadcast Recipient Maximization in WiMAX Multilevel Relay Networks

The radio resource allocation problem in wireless networks is a major research issue because the resource is scarce, and it must be shared among multiple network components. In this paper, we consider the resource allocation problem in downlink broadcast services in IEEE 802.16j WiMAX networks, where the objective is to serve as many mobile stations (MSs) as possible with the given resource budget. Solving the problem is not trivial because allocating different amounts of resources to the multihop relay base station (MR-BS) and relay stations (RSs) yields different network topologies and, thus, diverse routing paths from the MR-BS to the MSs. Existing approaches only provide inflexible heuristic resource allocation strategies or solutions based on a limited network scenario. More significantly, they focus on single-level relay networks (i.e., an RS can only relay data for MSs, not for other RSs). However, in practice, RSs can relay traffic from other RSs, and the resource allocation could be more efficient. This motivates us to formalize the problem as a multilevel broadcast receipt maximization (ML-BRM) problem. We show that the problem is NP-complete and propose a multilevel utility-based resource allocation (ML-URA) mechanism to solve the ML-BRM problem. In addition, we present a computation reduction mechanism that reduces the running time of the ML-URA substantially. The performance evaluation via simulations shows that the proposed ML-URA can serve more MSs than existing approaches, and the derived solution is very close to the optimum under different network scenarios and resource budgets. Finally, we show the approximation ratio between the ML-URA and the optimal solution.

Evaluation of OFDMA resource allocation algorithms in broadband wireless access networks

Broadband Wireless Access (BWA) is gaining interest due to the increasing demand of high-quality mobile wireless systems. In this context Orthogonal Frequency Division Multiple Access (OFDMA) is one of the most promising and researched physical transmission technologies. In OFDMA, data have to be mapped into a time-frequency matrix before the actual transmission. Efficiency, power consumption, Quality of Service (QoS) and complexity factors should be taken into account by mapping algorithms. In this paper, we focus on the OFDMA resource allocation problem in BWA networks. Through extensive simulation in different scenarios, performance of several resource allocation algorithms is evaluated.

Rate control-based framework and algorithm for optimal provisioning

Ideally, networks should be designed to accommodate a variety of different traffic types, while at the same time maximizing its efficiency and utility. Network utility maximization (NUM) serves as an effective approach for solving the problem of network resource allocation (NRA) in network analysis and design. In existing literature, the NUM model has been used to achieve optimal network resource allocation such that the network utility is maximized. This is important, since network resources are at premium with the exponential increase in Internet traffic. However, most research work considering network resource allocation does not take into consideration key issues, such as routing and delay. A good routing policy is the key to efficient network utility, and without considering the delay requirements of the different traffic, the network will fail to meet the user's quality of service (QoS) constraints. In this paper, we propose a new NUM framework that achieves improved network utility while taking into routing and delay requirements of the traffic. Then, we propose a decomposition technique-based algorithm, D-NUM, for solving this model efficiently. We compare our approach with existing approaches via simulations and show that our approach performs well.

Resource allocation in dynamic PERT networks with finite capacity

This article models the resource allocation problem in dynamic PERT networks with finite capacity of concurrent projects (COnstant Number of Projects In Process (CONPIP)), where activity durations are independent random variables with exponential distributions, and the new projects are generated according to a Poisson process. The system is represented as a queuing network with finite concurrent projects, where each activity of a project is performed at a devoted service station with one server located in a node of the network. For modeling dynamic PERT networks with CONPIP, we first convert the network of queues into a stochastic network. Then, by constructing a proper finite-state continuous-time Markov model, a system of differential equations is created to solve and find the completion time distribution for any particular project. Finally, we propose a multi-objective model with three conflict objectives to optimally control the resources allocated to the servers, and apply the goal attainment method to solve a discrete-time approximation of the original multi-objective problem.