Network Utility Maximization Research Articles

Dynamic Network Scheduling for Virtual Routers

Network scheduling is important to satisfy the bandwidth requirements of virtual networks that consist of virtual machines in the end-hosts and the virtual routers connecting them. However, existing studies have focused on developing bandwidth allocation techniques for end-host virtual machines, but do not consider the network performance of virtual routers. In this article, we propose a new network scheduling framework for virtual routers-CreditBank. CreditBank dynamically allocates network resources to virtual routers according to bandwidth requirements, and it adapts to changing network environments without adding significant overhead. CreditBank offers three scheduling policies: minimum bandwidth reservation, weight-based proportional sharing, and hybrid scheduling. In addition, CreditBank supports an efficient work-conserving method to maximize network utilization. We implement CreditBank based on the Xen and Kernel-based Virtual Machine (KVM) hypervisors and evaluate its performance. The evaluation results indicate that CreditBank satisfies bandwidth requirements of the virtual routers while utilizing up to 99% of network resources.

Adaptive Flow-Level Resource Allocation for Wireless Mesh Networks

In this paper, we study how to achieve network utility maximization (NUM) in flow-level wireless networks, which can be characterized by the random arrivals and departures of flow-level traffic in such networks. Existing work in this area has mainly concentrated on finding the stability region in such networks while achieving the NUM objective. In this paper, we propose a fully distributed adaptive CSMA based flow-level cross-layer resource allocation algorithm (FLRA), which achieves the NUM goal while satisfying desired delay requirement via optimized link scheduling at the MAC layer and optimal flow-level admission control at the transport layer. Detailed algorithm design is presented. We analyze the convergence property of FLRA and its optimality in terms of network utility while meeting desired delay requirement. We further reveal the relationship among maximum network utility, desired delay, and number of active flows when a network reaches stability. We conduct extensive simulations and the results validate the effectiveness of our analytical results.

Maximizing Quality Data-collection in Mobile-sink Based Energy-harvesting Sensor Networks

In energy-harvesting wireless sensor network (EHWSNs), sensor nodes (SNs) are rechargeable. But, the harvesting techniques used for recharging SNs’ are dynamic and may not provide evenly harvested energy to all sensors. Thus, maximizing quality data-collection (MQDC) is an interesting issue under energy harvesting constraints. In this article, we focus on this issue to solve. We consider a mobile-sink (MS) that moves on a constrained-path for collecting SNs’ data periodically. The SNs are distributed nearby the constrained-path. This scenario may exist in various real-world applications, such as traffic monitoring, environment monitoring and health monitoring of large buildings or bridges, etc. We transform the MQDC problem into a network-utility maximization problem. We then prove that the converted problem is an NP-hard. Thereafter, we develop a heuristic algorithm, referred to as Maximizing Quality Datacollection using Constrained-path Mobile-Sink (MQDCPMS), to solve it. We address the effect of change in the speed of MS on the quality data-collection. Finally, through extensive simulations, we find that the MQDCPMS algorithm maximizes the quality data-collection comparatively better than other baseline algorithms.

On the Optimal Two-Antenna Static Beamforming With Per-Antenna Power Constraints

This letter provides a theoretical analysis for the static beamforming problem when the base station is equipped with two antennas. It is assumed that the base station cannot change the beamforming vector dynamically due to hardware restrictions. The objective is to find the static beamforming vector that maximizes network utility when long-term information about the user location is available. Non-linear optimization techniques are used to find the solution of the problem for two different network utility functions: average SNR and average rate. Numerical results are provided to support the analysis.

Downlink Interference Management in Cell-Free VLC Network

Visible light communication (VLC) network is a candidate complement to its radio frequency counterpart with the benefits of wide free spectrum and low energy consumption. Due to the dense distribution of access points (APs) and users in VLC network, interference management is crucial to ensure the quality of service. Meanwhile, VLC link relates not only to the transmission power and the orientations of APs, but also to the orientations of users. Hence, the coverage of APs can be irregular and aligned with the motion of users. Cell-free network model, which is composed of irregular cells, is suitable for the VLC network. Therefore, in consideration of the directionality of VLC links, we investigate the joint optimization of AP grouping, AP-user association and AP power allocation for interference management (IM) in the cell-free VLC network, which is formulated as a network utility maximization (NUM) problem. The NUM problem is a mixed non-convex and combinatorial problem, and a graph-based IM algorithm is proposed to solve it. Specifically, a heuristic AP grouping algorithm is proposed based on an interference graph, which is defined according to the locations of APs and users as well as their orientations, to preselect several candidate AP groupings from the huge number of feasible AP groupings. Given each select AP grouping, the AP-user association and AP power allocation is optimized via the successive convex approximation method. Then, the proposed IM algorithm will choose the AP grouping from those candidates, as well as the corresponding AP-user association and AP power allocation, that attain the highest network utility. Simulations demonstrate that the proposed IM algorithm could achieve a network utility close to the maximum one obtained by searching through the entire solution space, with comparatively low complexity, and outperforms the conventional IM schemes.

Randomized Two-Timescale Hybrid Precoding for Downlink Multicell Massive MIMO Systems

Although massive multiple-input multiple-output (MIMO) promises high spectral efficiency, there are several issues that significantly limit the potential gain of massive MIMO, such as severe inter-cell interference, huge channel state information (CSI) overhead/delay, high cost and power consumption of RF chains, and user fairness. Several precoding schemes have been proposed for massive MIMO but there still lacks a systematic solution to address all of the aforementioned issues especially for the multicell case. In this paper, we propose a randomized two-timescale hybrid precoding (RTHP) scheme for the downlink transmission of multicell massive MIMO systems. RTHP allows time sharing among multiple sets of analog precoders to achieve better tradeoff between sum throughput and fairness. Moreover, the time-sharing factors and analog precoders are adapted to the long-term channel statistics to mitigate inter-cell interference with reduced CSI overhead and RF chains. The digital precoder is used to support multiuser MIMO at each base station based on local low-dimensional effective CSI only. As such, RTHP resolves all of the aforementioned issues. We formulate the optimization of RTHP as a general network utility maximization problem (GNUMP), which is a two-timescale stochastic non-convex problem, and consequently challenging to solve. By exploiting some approximation techniques, we convert the problem into a more tractable form and subsequently develop an online algorithm, which is shown to converge to stationary solutions of the approximate GNUMP. Finally, simulation results verify the advantages of the proposed scheme over baseline schemes.

Efficient Network Sharing With Asymmetric Constraint Information

Network sharing has become a key feature of various enablers of the next generation network, such as network function virtualization and fog computing architectures. Network utility maximization (NUM) is a general framework for achieving fair, efficient, and cost-effective sharing of constrained network resources. When agents have asymmetric and private information, however, a fundamental economic challenge is how to solve the NUM Problem considering the self-interests of strategic agents. Many previous related works have proposed economic mechanisms that can cope with agents' private utilities. However, the network sharing paradigm introduces the issue of information asymmetries regarding constraints. The related literature largely neglected such an issue; limited closely related studies provided solutions only applicable to specific application scenarios. To tackle these issues, we propose the Decomposable NUM (DeNUM) Mechanism and the Dynamic DeNUM (DyDeNUM) Mechanism, the first mechanisms in the literature for solving NUM Problems considering private utility and constraint information. The key idea of both mechanisms is to decentralize the decision process to agents, who will make resource allocation decisions without the need of revealing private information to others. Under a monitorable influence assumption, the DeNUM Mechanism yields the network-utility maximizing solution at an equilibrium, and achieves other desirable economic properties (such as individual rationality and budget balance). We further establish the connection between the equilibrium structure and the primal-dual solution to a related optimization problem, based on which we prove the convergence of the DeNUM Algorithm to an equilibrium. When the agents' influences are not monitorable, we propose the DyDeNUM Mechanism that yields the network-utility maximizing solution at the cost of the balanced budget.

A fully distributed traffic allocation algorithm for nonconcave utility maximization in connectionless communication networks

As IP video services have emerged to be the predominant Internet application, how to optimize the Internet resource allocation, while satisfying the quality of experience (QoE) for users of video services and other Internet applications becomes a challenge. This is because the QoE perceived by a user of video services can be characterized by a staircase function of the data rate, which is nonconcave and hence it is “hard” to find the optimal operating point. The work in this paper aims at tackling this challenge. It considers the packet routing problem among multiple end points in packet switching networks based on a connectionless, hop-by-hop forwarding paradigm. We model this traffic allocation problem using a fluid flow model and let the link bandwidth be the only resource to be shared. To maximize the utilization of resources and avoid congestion, we formulate the problem as a network utility maximization problem. More precisely, the objective of this paper is to design a Fully Distributed Traffic Allocation Algorithm (FDTAA) that is applicable to a large class of nonconcave utility functions. Moreover, FDTAA runs in a fully distributed way: it enables each router to independently address and route each data unit using immediate local information in parallel, without referring to any global information of the communication network. FDTAA requires minimum computation workload, since the routing decision made at each router is solely based on the destination information carried in each unit. In addition, the network utility values corresponding to the FDTAA iterate sequence converge to the optimal network utility value at the rate of O(1∕K), where K is the iteration counter. These theoretical results are exemplified by the simulation performed on an example communication network.

Spectrum Management for Multi-Access Edge Computing in Autonomous Vehicular Networks

In this paper, a dynamic spectrum management framework is proposed to improve spectrum resource utilization in a multi-access edge computing (MEC) in autonomous vehicular network (AVNET). To support the increasing communication data traffic and guarantee quality-of-service (QoS), spectrum slicing, spectrum allocating, and transmit power controlling are jointly considered. Accordingly, three non-convex network utility maximization problems are formulated to slice spectrum among base stations (BSs), allocate spectrum among autonomous vehicles (AVs) associated with a BS, and control transmit powers of BSs, respectively. Through linear programming relaxation and first-order Taylor series approximation, these problems are transformed into tractable forms and then are jointly solved through an alternate concave search (ACS) algorithm. As a result, the optimal spectrum slicing ratios among BSs, optimal BS-vehicle association patterns, optimal fractions of spectrum resources allocated to AVs, and optimal transmit powers of BSs are obtained. Based on our simulation, a high aggregate network utility is achieved by the proposed spectrum management scheme compared with two existing schemes.

A distributed Newton algorithm for network utility maximization in wireless ad hoc networks

SummaryThe existing distributed Newton algorithm for network utility maximization cannot be directly applied into the wireless ad hoc networks, as it does not consider the wireless link capacity variation and transmission power consumption. Regarding these, a new joint network utility maximization problem is formulated to optimize the wireless ad hoc network resource utility. The distributed Newton algorithm is implemented to solve for the dual variables and Newton directions by using the local information at each session source and link, respectively. Furthermore, a new iterative method is proposed to improve the convergence rate of the existing matrix‐splitting method. Simulation results validate the efficiency and efficacy of the proposed distributed Newton algorithm for wireless ad hoc network utility maximization.

Gaussian Belief Propagation for Solving Network Utility Maximization with Delivery Contracts.

Classical network utility maximization (NUM) models fail to capture network dynamics, which are of increasing importance for modeling network behaviors. In this paper, we consider the NUM with delivery contracts, which are constraints to the classical model to describe network dynamics. This paper investigates a method to distributively solve the given problem. We first transform the problem into an equivalent model of linear equations by dual decomposition theory, and then use Gaussian belief propagation algorithm to solve the equivalent issue distributively. The proposed algorithm has faster convergence speed than the existing first-order methods and distributed Newton method. Experimental results have demonstrated the effectiveness of our proposed approach.

Online Resource Inference in Network Utility Maximization Problems

The amount of transmitted data in computer networks is expected to grow considerably in the future, putting more and more pressure on the network infrastructures. In order to guarantee a good service, it then becomes fundamental to use the network resources efficiently. Network Utility Maximization (NUM) provides a framework to optimize the rate allocation when network resources are limited. Unfortunately, in the scenario where the amount of available resources is not known a priori, classical NUM solving methods do not offer a viable solution. To overcome this limitation we design an overlay rate allocation scheme that attempts to infer the actual amount of available network resources while coordinating the users rate allocation. Due to the general and complex model assumed for the congestion measurements, a passive learning of the available resources would not lead to satisfying performance. The coordination scheme must then perform active learning in order to speed up the resources estimation and quickly increase the system performance. By adopting an optimal learning formulation we are able to balance the tradeoff between an accurate estimation, and an effective resources exploitation in order to maximize the long term quality of the service delivered to the users.

End-to-End Distributed Flow Control for Networks with Nonconcave Utilities

This paper proposes near-optimal decentralized allocation for traffic generated by real-time applications in communication networks. The quality of experience perceived by users in practical applications cannot be accurately modeled using concave functions. Therefore, we tackle the problem of optimizing general nonconcave network utilities. The approach for solving the resulting nonconvex network utility maximization problem relies on designing a sequence of convex relaxations whose solutions converge to a point that characterizes an optimal solution of the original problem. Three different algorithms are designed for solving the proposed convex relaxation, and their theoretical convergence guarantees are studied. All proposed algorithms are distributed in nature, where each user independently controls its traffic in a way that drives the overall network traffic allocation to an optimal operating point subject to resource constraints. All computations required by the algorithms are performed independently and locally at each user using local information available to that user. We highlight the tradeoff between the convergence speed and the network overhead required by each algorithm. Furthermore, we demonstrate the robustness and scalability of these algorithms by showing that traffic is automatically rerouted in case of a link failure or having new users joining the network. Numerical results are presented to validate our findings.

Research on cross-layer design and optimization algorithm of network robot 5G multimedia sensor network

Cross-layer optimization based on maximizing the utility of network robot 5G multimedia sensor network is a systematic method for cross-layer design of wireless networks. It abstracts the functional and performance requirements of the layers in the protocol stack into objective functions and constraints in mathematical optimization problems. In this article, the cross-layer optimization problem of wireless Mesh networks using multi-radio interface multi-channel technology is studied. The optimization problem is modelled based on the network utility maximization method, and the corresponding algorithm is proposed. Based on the random network utility maximization method, the cross-layer optimization model of network robot 5G multimedia sensor network is established. Aiming at the time-varying randomness of random data flow and wireless propagation environment in network robot 5G multimedia sensor network, a model of joint congestion control and power control based on chance constrained programming is proposed, and its genetic algorithm is used to verify it. Reforming research will help speed up the practical pace of the field, with certain theoretical forward-looking and practical value.

Axiomatizing Congestion Control

The overwhelmingly large design space of congestion control protocols, along with the increasingly diverse range of application environments, makes evaluating such protocols a daunting task. Simulation and experiments are very helpful in evaluating the performance of designs in specific contexts, but give limited insight into the more general properties of these schemes and provide no information about the inherent limits of congestion control designs (such as, which properties are simultaneously achievable and which are mutually exclusive). In contrast, traditional theoretical approaches are typically focused on the design of protocols that achieve to specific, predetermined objectives (e.g., network utility maximization), or the analysis of specific protocols (e.g., from control-theoretic perspectives), as opposed to the inherent tensions/derivations between desired properties. To complement today's prevalent experimental and theoretical approaches, we put forth a novel principled framework for reasoning about congestion control protocols, which is inspired by the axiomatic approach from social choice theory and game theory. We consider several natural requirements ("axioms'') from congestion control protocols -- e.g., efficient resource-utilization, loss-avoidance, fairness, stability, and TCP-friendliness -- and investigate which combinations of these can be achieved within a single design. Thus, our framework allows us to investigate the fundamental tradeoffs between desiderata, and to identify where existing and new congestion control architectures fit within the space of possible outcomes.

Spatial Deep Learning for Wireless Scheduling

The optimal scheduling of interfering links in a dense wireless network with full frequency reuse is a challenging task. The traditional method involves first estimating all the interfering channel strengths then optimizing the scheduling based on the model. This model-based method is however resource intensive and computationally hard because channel estimation is expensive in dense networks; furthermore, finding even a locally optimal solution of the resulting optimization problem may be computationally complex. This paper shows that by using a deep learning approach, it is possible to bypass the channel estimation and to schedule links efficiently based solely on the geographic locations of the transmitters and the receivers, due to the fact that in many propagation environments, the wireless channel strength is largely a function of the distance dependent path-loss. This is accomplished by unsupervised training over randomly deployed networks, and by using a novel neural network architecture that computes the geographic spatial convolutions of the interfering or interfered neighboring nodes along with subsequent multiple feedback stages to learn the optimum solution. The resulting neural network gives near-optimal performance for sum-rate maximization and is capable of generalizing to larger deployment areas and to deployments of different link densities. Moreover, to provide fairness, this paper proposes a novel scheduling approach that utilizes the sum-rate optimal scheduling algorithm over judiciously chosen subsets of links for maximizing a proportional fairness objective over the network. The proposed approach shows highly competitive and generalizable network utility maximization results.

Simulation-Based Distributed Coordination Maximization Over Networks

In various online/offline multi-agent networked environments, it is very popular that the system can benefit from coordinating actions of two interacting agents at some cost of coordination. In this paper, we first formulate an optimization problem that captures the amount of coordination gain at the cost of node activation over networks. This problem is challenging to solve in a distributed manner, since the target gain is a function of the long-term time portion of the inter-coupled activations of two adjacent nodes, and thus a standard Lagrange duality theory is hard to apply to obtain a distributed decomposition as in the standard Network Utility Maximization. In this paper, we propose three simulation-based distributed algorithms, each having different update rules, all of which require only one-hop message passing and locally-observed information. The key idea for being distributedness is due to a stochastic approximation method that runs a Markov chain simulation incompletely over time, but provably guarantees its convergence to the optimal solution. Next, we provide a game-theoretic framework to interpret our proposed algorithms from a different perspective. We artificially select the payoff function, where the game's Nash equilibrium is asymptotically equal to the socially optimal point, i.e., no Price-of-Anarchy. We show that two stochastically-approximated variants of standard game-learning dynamics overlap with two algorithms developed from the optimization perspective. Finally, we demonstrate our theoretical findings on convergence, optimality, and further features such as a trade-off between efficiency and convergence speed through extensive simulations.

Fairness-Aware Dynamic Rate Control and Flow Scheduling for Network Utility Maximization in Network Service Chain

Network function virtualization (NFV) decouples the traditional network functions from specific or proprietary hardware, such that virtualized network functions (VNFs) can run in software form. By exploring NFV, a consecutive set of VNFs can constitute a service function chain (SFC) to provide the network service. From the perspective of network service providers, how to maximize the network utility is always one of the major concerns. To this end, there are two main issues need to be considered at runtime: 1) how to handle the unpredictable network traffic burst? and 2) how to fairly allocate resources among various flows to satisfy different traffic demands? In this paper, we investigate a fairness-aware flow scheduling problem for network utility maximization, with joint consideration of resource allocation and rate control. Based on a discrete-time queuing model, we propose a low-complexity online-distributed algorithm using the Lyapunov optimization framework, which can achieve arbitrary optimal utility with different fairness levels by tuning the fairness bias parameter. We theoretically analyze the optimality of the algorithm and evaluate its efficiency by both simulation and testbed-based experiments.

Necessary and sufficient condition for non-concave network utility maximisation

ABSTRACTAs the popularity of intellectual-property video services growing, users have raised their expectations on better quality of services. However, the existing traffic engineering solutions are not adequate to provide such desired quality to users. This paper aims to develop the distributed, user-utility-aware and optimisation-based traffic allocation mechanisms for real applications with non-concave utility functions, and then provide a solution to the problem. We formulate the traffic allocation problem as a network utility maximisation problem with link capacity constraints. This optimisation problem is challenging because of the non-concavity of the utility functions and the lack of global information. We overcome the difficulty by designing a class of fully distributed traffic allocation control laws, which requires a minimum communication workload. Moreover, we present a necessary and sufficient condition under which the proposed control laws converge to the globally optimal solution. Finally, we present numerical simulations to illustrate the theoretical results.

Completely Uncoupled Algorithms for Network Utility Maximization

In this paper, we present two completely uncoupled algorithms for utility maximization. In the first part, we present an algorithm that can be applied for general non-concave utilities. We show that this algorithm induces a perturbed (by $\epsilon$) Markov chain, whose stochastically stable states are the set of actions that maximize the sum utility. In the second part, we present an approximate sub-gradient algorithm for concave utilities which is considerably faster and requires lesser memory. We study the performance of the sub-gradient algorithm for decreasing and fixed step sizes. We show that, for decreasing step sizes, the Cesaro averages of the utilities converges to a neighbourhood of the optimal sum utility. For constant step size, we show that the time average utility converges to a neighbourhood of the optimal sum utility. Our main contribution is the expansion of the achievable rate region, which has been not considered in the prior literature on completely uncoupled algorithms for utility maximization. This expansion aids in allocating a fair share of resources to the nodes which is important in applications like channel selection, user association and power control.