Network Utility Maximization Model Research Articles

Maximizing network utility while considering proportional fairness for rail transit systems: Jointly optimizing passenger allocation and vehicle schedules

To deal with the contradiction between oversaturated demand and the restricted transport infrastructure and capacity during peak periods in a rail transit system, several passenger flow control methods that include soft pricing incentive, rigorous physical barrier and so on are widely used in current scenarios. However, the traffic disorderliness at the bottleneck and the control unfairness for the flows on specific stations may occur with those methods in the rail transit corridor. To alleviate those problems, this paper focus on the passenger allocation and matching with the corresponding schedules by considering the fairness of passengers and cost of operation from a macro perspective, where the passenger trip plan can be collected via a reservation platform in advance. A passenger utility maximization model that programs passenger allocation is proposed firstly, and we theoretically prove that there is an optimal traffic flow allocation with maximum utility under the oversaturated flow condition for any network. The model is solved by the Newton iteration algorithm and further proven to effectively make a tradeoff between efficiency and fairness. Furthermore, the transportation network utility maximization model is constructed by jointly optimizing the passenger allocation and vehicle schedules, thereby saving the cost of vehicle operation while guaranteeing the fairness of passenger allocation macroscopically. The model is solved by Multi-tree depth-first search algorithm based on Lagrangian relaxation with a subgradient in an iterative manner. A real-world case from the Batong subway line in Beijing is studied to validate the proposed models and algorithms. The results show that the model can jointly optimize the passenger allocation and vehicle schedules effectively in both an economical and fair manner, and the passengers can pass through the bottleneck in an orderly way.

Delay-Based Network Utility Maximization Modelling for Congestion Control in Named Data Networking

Content replication and name-based routing lead to a natural multi-source and multipath transmission paradigm in NDN. Due to the unique connectionless characteristic of NDN, current end-to-end multipath congestion control schemes (e.g. MPTCP) cannot be used directly on NDN. This paper proposes a Network Utility Maximization (NUM) model to formulate multi-source and multipath transmission in NDN with in-network caches. From this model, a family of receiver-driven transmission solutions can be derived, named as path-specified congestion control. The path-specified congestion control enables content consumers to separate the traffic control on each path, which consequently facilitates fair and efficient bandwidth sharing amongst all consumers. As a specific instance, a Delay-based Path-specified Congestion Control Protocol (DPCCP) is presented, which utilizes queuing delays as signals to measure and control congestion levels of different bottlenecks. In addition, a set of high-performance congestion control laws are designed to accelerate bandwidth and fairness convergence towards the optimum defined by the NUM model. Finally, DPCCP is compared with state-of-the-art solutions. The experimental evaluations show that DPCCP outperforms existing solutions in terms of bandwidth utilization, convergence time and packet loss.

An Upward Max-Min Fairness Multipath Flow Control

Many rate allocation algorithms for multipath flows which satisfy max-min fairness are centralized and not scalable. Upward max-min fairness is a well-known relaxation of max-min fairness and can be achieved by an algorithm extended from water-filling algorithm. In this paper, we propose a price-based multipath congestion control protocol whose equilibrium point satisfies upward max-min fairness. Our protocol is derived from a network utility maximization model for multipath flows.

MFAST: A Multipath Congestion Control Protocol for High Bandwidth-Delay Connection

Today, the smart devices are usually equipped with more than one network interfaces. A multipath congestion control protocol that exploits different paths to transmit data will improve the throughput and high-availability. Many multipath congestion control protocols have been developed in the literature. However, most of them are loss-based algorithms, hence they do not well utilize the bandwidth in high bandwidth-delay product (BDP) connections due to the conservative congestion control. From the single-path Fast TCP, we develop a multipath congestion control protocol, called mFast, for high BDP connections. MFast uses queueing delay to measure the congestion as Fast TCP does. Our framework is based on a network utility maximization model for multipath flows. The features of mFast such as load-balancing, TCP friendliness, and throughput improvement are verified via analysis and extensive simulations.

Fair Bandwidth Allocation Algorithm for PONs Based on Network Utility Maximization

Network utility maximization (NUM) models have been successfully applied to address multiple resource- allocation problems in communication networks. This paper explores, for the first time to our knowledge, their application to modeling the bandwidth-allocation problem in passive optical networks (PONs) and long-reach PONs. Using the NUM model, we propose the FEx-DBA (fair excess-dynamic bandwidth allocation) algorithm, a new DBA scheme to allow a fair and efficient allocation of the upstream channel capacity. The NUM framework provides the mathematical support to formally define the fairness concept in the resource allocation and the guidelines to devise FEx-DBA. A simulation study is conducted, whereby FEx-DBA is compared to a state-of-the-art proposal. We show that FEx-DBA (i) provides bandwidth guarantees to the users according to the service level agreement (SLA) contracted and fairly distributes the excess bandwidths among them; (ii) has a stable response and fast convergence when traffic or SLAs change, avoiding the oscillations appearing in other proposals; (iii) improves average delay and jitter measures; and (iv) only depends on a reduced set of parameters, which can be easily tuned.

Fair Congestion Control in Vehicular Networks With Beaconing Rate Adaptation at Multiple Transmit Powers

Cooperative intervehicular applications rely on the exchange of broadcast single-hop status messages among vehicles, which are called beacons. The aggregated load on the wireless channel due to periodic beacons can prevent the transmission of other types of messages, which is called channel congestion due to beaconing activity. In this paper, we propose to let vehicles transmit with different transmit power levels, each with a particular beaconing rate. The selection of the rate is modeled as a network utility maximization (NUM) problem. Our goal is to maximize the number of beacons delivered at each transmit power according to a well-defined fairness notion while complying with the maximum allowed beaconing load on the channel. The algorithm parameters can be set per vehicle and dynamically changed, which provides enough flexibility to support multiple applications on top of the control scheme, whereas the NUM model provides a rigorous framework to design a broad family of simple and decentralized algorithms, with proved convergence guarantees to a fair allocation solution. Simulation results validate our approach and show that it provides fair rate allocations in realistic multihop and dynamic scenarios with packet losses.

A Family of Stable Multipath Dual Congestion Control Algorithms

We consider the problem of multipath congestion control in the Internet. The aim is to take advantage of multiple paths diversity to achieve efficient bandwidth allocation and improve network efficiency. But there exist some potential difficulties when one directly uses the well-known network utility maximization model to design stable multipath congestion control algorithms for the alternative paths. In this paper, we propose a generalized multipath utility maximization model to consider the problem of joint routing and rate control, which can be reduced to specific models with different parameter settings. And then we develop a family of multipath dual congestion control algorithms which are stable in the absence of delays. We also derive decentralized and scalable sufficient conditions for a particular scheme when propagation delays exist in networks. The simulation results show that the proposed multipath dual congestion control algorithms with appropriate parameter settings can achieve stable resource shares while maintaining fairness among the involved users.

Optimal Congestion Control and Routing for Multipath Networks with Random Losses

In this paper we consider optimal congestion control and routing schemes for multipath networks with non-congestion related packet losses which can be caused by, for example, errors on links on the routes, and develop a relaxed multipath network utility maximization problem. In order to obtain the optimum, we present a primal algorithm which is shown to be globally stable in the absence of round-trip delays. When round-trip delays are considered, decentralized sufficient conditions for local stability of the algorithm are proposed, in both continuous-time and discrete- time forms. Finally, a window-flow control mechanism is presented which can approximate the optimum of the multipath network utility maximization model.

Network utility maximization in two-way flow scenario

A communication network usually has data packets and acknowledge (ACK) packets being transmitted in opposite directions. ACK packet flows may affect the performance of data packet flows, which is unfortunately not considered in the usual network utility maximization (NUM) model. This paper presents a NUM model in networks with two-way flows (NUMtw) by adding a routing matrix to cover ACK packet flows. The source rates are obtained by solving the dual model and the relation to the routing matrix of ACK packet flows is disclosed. Furthermore, the source rates in networks with one-way flows by the usual NUM model are compared to those in networks with two-way flows by the NUMtw model.

Network Utility Maximization with Random Link Failures

Network utility maximization model with random link failures was presented in this paper. We provide a distributed algorithm based on the dual decomposition and investigate the impacts of link failures on the network utility. Numerical results validated the convergence of the proposed algorithm and our conclusions.

MReno: a practical multipath congestion control for communication networks

In current networks, end-user devices are usually equipped with several network interfaces. The design of a multipath protocol that can cooperate with current single-path transport protocols is an interesting research field. Most previous works on multipath network utility maximization (NUM) lead to rate-based control protocols. Moreover, these studies do not model a case in which paths from a source may have different characteristics. Thus, these approaches are difficult to deploy to the Internet. In this paper, we introduce a multipath NUM model for a network with both multipath and single-path users. The proposed algorithm converges to a global solution to the multipath NUM. Based on the mathematical framework, we develop a multipath TCP called mReno. Analysis and simulations indicate that mReno is completely compatible with TCP Reno and achieves load-balance, fairness, and performance improvement targets.

Multi-path utility maximization and multi-path TCP design

The canonical multi-path network utility maximization (NUM) model which is extended directly from the single-path NUM has been studied widely in the literature. Most of the previous approaches do not specify the case of subflows on paths with different characteristics. Moreover, the transport protocol derived from the canonical multi-path NUM exhibits flappiness in the subflows because of the non-strictly convexity of the optimization problem.This paper introduces a modified multi-path NUM model and proposes a novel approach to overcome the mentioned issues. Using Jensen’s inequality, the multi-path NUM is approximated to a strictly convex and separable problem which can be solved efficiently by dual-based decomposition method. The algorithm successively solving a sequence of approximation problems is proven to converge at the global optimum of the original problem. Moreover, considering the separable form of the approximation utility and the dual-based nature of the proposed algorithm, the reverse engineering frameworks of the current TCPs are used to develop a series of multi-path TCPs that are compatible with corresponding regular single-path TCPs.

Utility-based bandwidth adaptation in mission-oriented wireless sensor networks

This article develops a utility-based optimization framework for resource sharing by multiple competing missions in a mission-oriented wireless sensor network (WSN) environment. Prior work on network utility maximization (NUM) based optimization has focused on unicast flows with sender-based utilities in either wireline or wireless networks. In this work, we develop a generalized NUM model to consider three key new features observed in mission-centric WSN environments: i) the definition of the utility of an individual mission (receiver) as a joint function of data from multiple sensor sources; ii) the consumption of each sender's (sensor) data by multiple missions; and iii) the multicast-tree-based dissemination of each sensor's data flow, using link-layer broadcasts to exploit the “wireless broadcast advantage” in data forwarding. We show how a price-based, distributed protocol (WSN-NUM) can ensure optimal and proportionally fair rate allocation across multiple missions, without requiring any coordination among missions or sensors. We also discuss techniques to improve the speed of convergence of the protocol, which is essential in an environment as dynamic as the WSN. Further, we analyze the impact of various network and protocol parameters on the bandwidth utilization of the network, using a discrete-event simulation of a stationary wireless network. Finally, we corroborate our simulation-based performance results of the WSN-NUM protocol with an implementation of an 802.11b network.

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.

Network utility maximization of coded packet network under delivery contracts constraint

The paper studied the problem of network utility maximization of coded packet network under delivery contracts constraint when coding subgraph was given.Based on the network utility maximization model proposed a distributed projection subgradient algorithm for this problem based on dual decomposition theory was developed and the sufficient condition that can make the algorithm converge to the globally optimal solutions was proved.In the end numerical example verifier the correctness of the algorithm.