Network Utility Maximization Problem Research Articles

On incentivizing resource allocation and task offloading for cooperative edge computing

Cooperative edge computing serves as an effective solution to provide reliable and elastic edge computing services through pooling geographically proximate edge resources and efficiently allocating them to users. The incentive mechanism is critical for realizing cooperative edge computing. In this paper, we propose a virtual machine (VM) or container based resource allocation scheme and two market-based mechanisms to stimulate collaboration among edge servers (ESs). Our first mechanism is a novel two-stage double auction based approach where edge resources are allocated to services/VMs at the first stage and then distributed among ESs at the second stage via a two-sided multi-unit auction. To make it more practical, we use markups to set the ask and bid prices for the participants in the market. Moreover, with the concept of virtual sellers and virtual buyers, we cast the multi-unit auction as single-unit one so that the auction becomes tractable. We further propose a price-based decentralized mechanism in case there is no central authority. With an appropriately constructed objective function, we formulate the resource allocation task as a network utility maximization (NUM) problem and leverage the dual-based decomposition theory for a distributed solution. We theoretically prove that both mechanisms guarantee properties such as budget balance and individual rationality for all ESs. Numerical studies with real-world dataset are presented to demonstrate the superior performance of our mechanisms over baselines.

Enabling Distributed and Optimal RDMA Resource Sharing in Large-Scale Data Center Networks: Modeling, Analysis, and Implementation

Remote Direct Memory Access (RDMA) suffers from unfairness issues and performance degradation when multiple applications share RDMA network resources. Hence, an efficient resource scheduling mechanism is urged to optimally allocates RDMA resources among applications. However, traditional Network Utility Maximization (NUM) based solutions are inadequate for RDMA due to three challenges: 1) The standard NUM-oriented algorithm cannot deal with coupling variables introduced by multiple dependent RDMA operations; 2) The stringent constraint of RDMA on-board resources complicates the standard NUM by bringing extra optimization dimensions; 3) Naively applying traditional algorithms for NUM suffers from scalability issues in solving a large-scale RDMA resource scheduling problem. In this paper, we present how to optimally share the RDMA resources in large-scale data center networks with a distributed manner. First, we propose Distributed RDMA NUM (DRUM) to model the RDMA resource scheduling problem as a new variation of the NUM problem. Second, we present distributed algorithms to efficiently solve the large-scale, interdependent RDMA resource sharing problem for different RDMA use cases. Through theoretical analysis, the convergence and parallelism of proposed algorithms are guaranteed. Finally, we implement the algorithms as a kernel-level indirection module in the real-world RDMA environment, so as to provide end-to-end resource sharing and performance guarantee. Through extensive evaluations by large-scale simulations and testbed experiments, we show that our method significantly improves applications’ performance under resource contention, achieving <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.7-3.1\times$</tex-math> </inline-formula> higher throughput, and in a dynamic context, the largest performance improvement reaches <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$98.1\%$</tex-math> </inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$64.1\%$</tex-math> </inline-formula> in terms of latency and throughput, respectively.

Joint Resource Slicing and Vehicle Association for Drone-Assisted Vehicular Networks

The drone-small-cell-assisted air-ground integrated network is a promising architecture for enabling diverse vehicle applications. This paper presents a joint resource slicing and vehicle association framework for drone-assisted vehicular networks, which facilitates spectrum sharing among heterogeneous base stations (BSs) and achieves dynamic resource provisioning in the presence of network load dynamics. We formulate the network utility maximization problem as mixed-integer nonlinear programming, considering traffic statistics, quality-of-service (QoS) constraints, varying vehicle locations, load conditions in each cell, and interdrone interference. The original maximization problem is transformed into a biconcave optimization problem to ensure mathematical tractability. An alternate concave search algorithm is then designed to iteratively solve vehicle association patterns and spectrum partitioning among heterogeneous BSs until convergence. Simulation results show that the proposed scheme achieves a significant performance improvement in throughput and spectrum utilization compared with two other baseline schemes.

Joint user association and resource allocation for cost-efficient NOMA-enabled F-RANs

Integrating Non-Orthogonal Multiple Access (NOMA) into Fog Radio Access Networks (F-RANs) has shown to be effective in boosting the spectral efficiency, energy efficiency, connectivity, and reducing the latency, thus attracting significant research attention. However, the performance improvement of the NOMA-enabled F-RANs is at the cost of computational overheads, which are commonly neglected in their design and deployment. To address this issue, in this paper, we propose a hybrid dynamic downlink framework for NOMA-enabled F-RANs. In this framework, we first develop a novel network utility function, which takes both the network throughput and computational overheads into consideration, thus enabling us to comprehensively evaluate the performance of different access schemes for F-RANs. Based on the developed network utility function, we further formulate a network utility maximization problem, subject to practical constraints on the decoding order, power allocation, and quality-of-service. To solve this NP-hard problem, we decompose it into two subproblems, namely, a user equipment association and subchannel assignment subproblem and a power allocation subproblem. Three-dimensional matching and sequential convex programming-based algorithms are designed to solve these two subproblems, respectively. Through numerical results, we show how our proposed algorithms can achieve a good balance between the network throughput and computational overheads by judiciously adjusting the maximum transmit power of fog access points. We also show that the proposed NOMA-enabled F-RAN framework can increase, by up to 89%, the network utility, compared to OMA-based F-RANs.

Adaptive Random Access and Data Transmission Scheme With Mixed Traffic in NGSO Satellite Networks

In non-geostationary orbit satellite networks, the resource is limited and the traffic under each satellite is in variation due to the motion of satellites and the non-uniform traffic on the ground. Generally, the uplink communication is guaranteed by successful random access (RA) and sufficient data transmission (DT) resources for various traffic types. Different traffic types usually have different requirements for resources, and joint resource management for mixed traffic tries to meet the needs of various traffic types at the same time and may improve uplink resource efficiency. However, existing strategies have not given integrative consideration to jointly allocate resources for RA and DT under dynamic mixed traffic demands, which leads to inefficient network resource utilization. Considering such a problem, we propose an adaptive random access and data transmission scheme for NGSO satellite networks in the mixed traffic scenario, considering two types of users, i.e., real-time users and best-effort users. Specifically, the utility functions of different types of users are introduced, and a utility-based access control and resource allocation algorithm is developed by solving the network utility maximization problem. Our scheme aims to achieve optimal uplink network utilization by adaptively controlling random access and allocating resources according to the dynamic demands of mixed traffic. Simulation results show that the proposed scheme outperforms other methods in terms of network utility, and exhibits near-optimal performance.

Online Network Utility Maximization: Algorithm, Competitive Analysis, and Applications

In this article, we consider an online version of the well-studied network utility maximization problem, where users arrive one by one and a network operator makes irrevocable rate allocation decisions for each user without knowing the details of future arrivals. We propose a threshold-based algorithm and analyze its worst-case performance. We prove that the competitive ratio of the proposed algorithm is logarithmic in the maximum number of links requested by a user. Extensive simulations are conducted to demonstrate the performance advantage of our proposed algorithm in comparison with two state-of-the-art algorithms. In addition, we devise an adaptive implementation of our algorithm with online learning.

System-Level Analysis of Full-Duplex Self-Backhauled Millimeter Wave Networks

Integrated access and backhaul (IAB) facilitates cost-effective deployment of millimeter wave (mmWave) cellular networks through multihop self-backhauling. Full-duplex (FD) technology, particularly for mmWave systems, is a potential means to overcome latency and throughput challenges faced by IAB networks. We derive practical and tractable throughput and latency constraints using queueing theory and formulate a network utility maximization problem to evaluate both full-duplex (FD)-IAB and half-duplex (HD)-IAB networks. We use this to characterize the network-level improvements seen when upgrading from conventional HD IAB nodes to FD ones by deriving closed-form expressions for (i) latency gain of FD-IAB over HD-IAB and (ii) the maximum number of hops that a HD- and FD-IAB network can support while satisfying latency and throughput targets. Extensive simulations illustrate that FD-IAB can facilitate reduced latency, higher throughput, deeper networks, and fairer service. Compared to HD-IAB, FD-IAB can improve throughput by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times $ </tex-math></inline-formula> and reduce latency by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula> for a fourth-hop user. In fact, upgrading IAB nodes with FD capability can allow the network to support latency and throughput targets that its HD counterpart fundamentally cannot meet. The gains are more profound for users further from the donor and can be achieved even when residual self-interference is significantly above the noise floor.

Information Flow Optimization for Estimation in Linear Models Using a Sensor Network

The problem considered is one of maximizing the information flow through a sensor network tasked with estimating, at a fusion center, an underlying parameter in a linear observation model. The sensor nodes take observations, quantize them, and send them to the fusion center through a network of relay nodes. The links in the network are assumed to satisfy certain capacity constraints in terms of the maximum number of bits that can transmitted on the links. Furthermore, the relay nodes are assumed to satisfy flow conservation constraints, i.e., the number of bits flowing into a relay node is equal to the number of bits flowing out of it. It is shown that this flow optimization problem for estimation can be cast as a Network Utility Maximization (NUM) problem by suitably defining the utility functions at the sensors. The inference problem considered is one of parameter estimation with a linear observation model, which is studied in both Bayesian and non-Bayesian settings. Upper bounds on the mean-squared error (MSE) of optimal linear estimators are obtained in both settings, and these bounds are used to construct utility functions for the corresponding NUM problems. It is verified via simulations that the bit assignments at the sensors obtained through the solutions to the NUM problems, in both the Bayesian and non-Bayesian settings, yield considerably better estimation performance than the Max-Flow solution that simply assigns bits to the sensors in such a way as to maximize the total bits transmitted to the fusion center.

Federated Edge Network Utility Maximization for a Multi-Server System: Algorithm and Convergence

We propose a novel Federated Edge Network Utility Maximization (FEdg-NUM) architecture for solving a large-scale distributed network utility maximization (NUM) problem. In FEdg-NUM, clients with private utilities communicate to a peer-to-peer network of edge servers. This represents a departure from the classical distributed NUM master-slave configuration and enables distributed computing harnessing local communications. Compared to a solution using cloud synchronization via Ring AllReduce, we prove that our federated edge computing model has shorter run-time in the presence of network congestion, thanks to its configuration and its ability to make progress in the presence of intermittent links. The paper studies its convergence and run-time performance both analytically and numerically, and illustrates several possible networking applications.

Analysis of Opportunistic Relaying and Load Balancing Gains Through V2V Clustering

Recent advances in digitization, artificial intelligence, and wireless networking are revolutionizing the traditional transportation networks and the automotive industry, leading for instance to the widespread adoption of ride-sharing services and the emergence of self-driving vehicles. As a result, a considerable rise in the volume of infotainment data consumed by the vehicles’ passengers is expected. To support such traffic loads, we explore leveraging vehicles as part of the cellular infrastructure. This paper investigates and quantifies the performance gains that using Vehicle-to-Vehicle (V2V) communication to form relay clusters would enable, including (1) improving opportunistic channel access to the cellular infrastructure by relaying the cluster’s traffic through the vehicle seeing the best channel quality; and (2) balancing traffic loads across cells through cluster multihoming. We introduce a stochastic geometric model allowing us to provide a deeper understanding of the possible gains associated with cluster-based opportunistic relaying and its sensitivity to the system parameters, e.g., base station density, vehicle density on the roads, etc. We develop a natural network utility maximization problem to serve as a baseline to evaluate the performance of a simpler distributed cluster management algorithm which we show to be near-optimal. Overall the results suggest that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10-20\times$</tex-math></inline-formula> gains in the vehicles’ shared rate can be achieved along with significant fairness improvements, while other mobile devices also benefit from the proposed network architecture.

Utility Maximization for Multihop Wireless Networks Employing BATS Codes

BATS (BATched Sparse) codes are a class of efficient random linear network coding variation that has been studied for multihop wireless networks mostly in scenarios of a single communication flow. Towards sophisticated multi-flow network communications, we formulate a network utility maximization (NUM) problem that jointly optimizes the BATS code parameters of all the flows and network scheduling. The NUM problem adopts a batch-wise packet loss model that can be obtained from the network local statistics without any constraints on packet loss patterns. Moreover, the NUM problem allows a different number of recoded packets to be transmitted for different batches in a flow, which is called adaptive recoding. Due to both the probably nonconcave objective and the BATS code-related variables, the algorithms developed for the existing flow optimization problems cannot be applied directly to solve our NUM problem. We introduce a two-step algorithm to solve our NUM problem, where the first step solves the problem with nonadaptive recoding schemes, and the second step optimizes adaptive recoding hop-by-hop from upstream to downstream in each flow. We perform various numerical evaluations and simulations to verify the effectiveness and efficiency of the algorithm.

HOPASS: A two-layer control framework for bandwidth and delay guarantee in datacenters

In data center networks (DCNs), flows with different objectives coexist and compete for limited network resources (such as bandwidth and buffer space). Without harmonious resource planning, chaotic competition among these flows would lead to severe performance degradation. Furthermore, low latency is critical for many emerging applications such as augmented reality(AR), virtual reality(VR) and telepresence, making the network control problem even more challenging. To address the above issues, this paper novelly proposes a receiver-driven two-layer control framework called HOPASS, which incorporates a slow control layer and a fast control layer to strike a balance among multiple network sharing objectives and achieve low latency. The slow control layer ensures bandwidth guarantee in an aggregated flow level by solving a multi-objective network utility maximization (NUM) problem using an online learning approach. Then the results will be dispatched to the data plane by configuring weights in the switches with weighted fair queue functionality. Under the configuration dictated by the slow control layer, the fast control layer leverages the token packets sent by receivers to dynamically probe and reserve network capacity, so that it can proactively prevent network congestion and guarantee low latency data delivery. To evaluate the proposed framework, we have implemented HOPASS in ns-3 and conduct extensive experiments under various network scenarios. The simulation results show that HOPASS achieves near-optimal performance in terms of bandwidth allocation in multi-objective scenarios and also guarantees low end-to-end delay. Moreover, it outperforms DCTCP and NewReno in terms of average bandwidth utilization and global total network utility at the aggregated flow level. Therefore, we conclude that HOPASS provides an effective framework for DCNs when considering both multi-objective optimization and a low latency network.

A fast distributed algorithm for coupled utility maximization problem with application for power control in wireless sensor networks

This paper investigates a method to distributively solve a Network Utility Maximization (NUM) problem with coupled variables and applies it to study power control in wireless sensor networks (WSNs). We present a dual decomposition-based consistency price algorithm to solve the coupled problem. However, the consistency price algorithm suffers from slow convergence. We then propose a two-step method to address the given issue. The first step is to build up a global consensus problem by introducing slack variables to transform the NUM problem with globally coupled variables into a NUM problem with coupled constraints. The second step is to design a distributed algorithm that combines the first-order gradient/subgradient method and a local consensus algorithm to solve the global consensus problem. The proposed algorithm is a primary algorithm which has faster convergence speed than the consistency price algorithm which is a primary-dual algorithm. Experimental results have demonstrated the effectiveness of our proposed approach.

Minimal Delay Violation-Based Cross-Layer Scheduler and Resource Allocation for DSL Networks

The quality of service of many modern communication systems depends on the delay performance of the underlying network. In digital subscriber line (DSL) networks, for example, crosstalk introduces competition for data rate among users, which influences the delay distribution. In such a competitive environment, delay performance is largely determined by the manner in which resources are dynamically allocated to the different users. A common approach to this allocation problem is through a cross-layer scheduler. Such a scheduler violates the OSI model by allowing communication between different layers, in order to steer the physical layer towards operating points that maximize some upper layer performance metric. In this paper, we present a new cross-layer scheduler and resource allocation algorithm in the context of DSL networks, referred to as the minimal delay violation (MDV) scheduler, which aims to minimize the number of delay violations and achieve a high throughput. Rather than solving a linear network utility maximization problem, as most other schedulers from literature, we consider a problem that is reciprocal with the service rate, allowing the scheduler to allocate the data rates at a finer level, while still maintaining good performance. Through simulations, we show that the MDV scheduler performs better than cross-layer scheduling algorithms from literature with respect to packet loss ratio, delay and throughput performance for various scenarios, and often operates closely to an ideal scheduler.

Constrained Utility Maximization in Dual-Functional Radar-Communication Multi-UAV Networks

In this paper, we investigate the network utility maximization problem in a dual-functional radar-communication multi-unmanned aerial vehicle (multi-UAV) network where multiple UAVs serve a group of communication users and cooperatively sense the target simultaneously. To balance the communication and sensing performance, we formulate a joint UAV location, user association, and UAV transmission power control problem to maximize the total network utility under the constraint of localization accuracy. We then propose a computationally practical method to solve this NP-hard problem by decomposing it into three sub-problems, i.e., UAV location optimization, user association and transmission power control. Three mechanisms are then introduced to solve the three sub-problems based on spectral clustering, coalition game, and successive convex approximation, respectively. The spectral clustering result provides an initial solution for user association. Based on the three mechanisms, an overall algorithm is proposed to iteratively solve the whole problem. We demonstrate that the proposed algorithm improves the minimum user data rate significantly, as well as the fairness of the network. Moreover, the proposed algorithm increases the network utility with a lower power consumption and similar localization accuracy, compared to conventional techniques.

Stochastic Transceiver Optimization in Multi-Tags Symbiotic Radio Systems

Symbiotic radio (SR) is emerging as a spectrum-and energy-efficient communication paradigm for future passive Internet of Things (IoT), where some single-antenna backscatter devices, referred to as Tags, are parasitic in an active primary transmission. The primary transceiver is designed to assist both direct-link (DL) and backscatter-link (BL) communication. In multi-Tags SR systems, the transceiver designs become much more complicated due to the presence of DL and inter-Tag interference, which further poses new challenges to the availability and reliability of DL and BL transmission. To overcome these challenges, we formulate the stochastic optimization of transceiver design as the general network utility maximization problem (GUMP). The resultant problem is a stochastic multiple-ratio fractional nonconvex problem, and consequently challenging to solve. By leveraging some fractional programming techniques, we tailor a surrogate function with the specific structure and subsequently develop a batch stochastic parallel decomposition (BSPD) algorithm, which is shown to converge to stationary solutions of the GNUMP. The simulation results verify the effectiveness of the proposed algorithm by numerical examples in terms of the achieved system throughput.

Energy-Aware Joint Clustering and Scheduling for Multicast Beamforming in Cloud-RAN Downlink

In cloud radio access network (Cloud-RAN), dense remote radio heads (RRHs) connect to baseband unit (BBU) pool through optical transport link, causing significant interference and power consumption in multicast downlinks. Coordinated beamforming with RRHs clustering is expected to be a key element towards improving network performance with more consideration of energy efficiency and interference management. This letter proposes an energy-aware joint clustering and scheduling (EA-JCS) scheme for multicast beamforming in Cloud-RAN downlink, which is formulated as a network utility maximization problem with fractional formulation and low-rank constraints. To efficiently solve this problem, we first develop a heuristic algorithm to provide sub-optimal performance by Dinkelbach’s transformation. To handle non-convex formulation, we propose an iterative approximation algorithm with linear approximation and constraints relaxation. The effectiveness of the proposed scheme is confirmed by simulations.

Distributed Mechanism Design for Network Resource Allocation Problems

In the standard mechanism design framework, agents’ messages are gathered at a central point and allocation/tax functions are calculated in a centralized manner, i.e., as functions of all network agents’ messages. This requirement may cause communication and computation overhead and necessitates the design of mechanisms that alleviate this bottleneck. We consider a scenario where message transmission can only be performed locally so that the mechanism allocation/tax functions can be calculated in a decentralized manner. Each agent transmits messages to her local neighborhood, as defined by a given message-exchange network, and her allocation/tax functions are only functions of the available neighborhood messages. This scenario gives rise to a novel research problem that we call “distributed mechanism design.”In this paper, we propose two distributed mechanisms for network utility maximization problems that involve private and public goods with competition and cooperation between agents. As a concrete example, we use the problems of rate allocation in networks with either unicast or multirate multicast transmission protocols. The proposed mechanism for each of the protocols fully implements the optimal allocation in Nash equilibria, and its message space dimensionality scales linearly with respect to the number of agents in the network.

End-to-End TCP Congestion Control for Mobile Applications

This paper addresses the re-engineering of congestion control for TCP applications over networks with coupled wireless links. Using queueing delay as a congestion measure, we show that optimal TCP congestion control can be achieved by developing window-control oriented implicit primal-dual solvers for intended network utility maximization problem. Capitalizing on such an idea, we prove the existence of scalable, easy-to-deploy, yet optimal end-to-end congestion control schemes for networks with wireless links, given that the wireless access point appropriately schedules packet transmissions. A class of so-called QUIC-TCP congestion control algorithms are developed. Relying on a Lyapunov method, we rigorously establish the global convergence/stability of the proposed QUIC-TCP to optimal equilibrium in the network fluid model. Numerical results corroborate the merits of the proposed schemes in IPv6-based Internet environments.

Joint BATS Code and Periodic Scheduling in Multihop Wireless Networks

Batched sparse (BATS) code is a promising technology for reliable data transmission in error-prone environments. A BATS code consists of an outer code and an inner code. It has been shown that a well-designed inner code is critical to the performance of BATS. Though great efforts have been made in the design of inner codes, the impact of scheduling (that determines when inner codes take place at nodes in the path) on inner codes is still unclear. In this paper, we study the joint design of inner code and scheduling in multihop wireless networks. We first introduce a new network utility from which to associate inner codes with scheduling, and formulate the coding constraint and the scheduling constraint using independent sets of a graph. With the utility, the joint design problem is then transformed to a network utility maximization problem under the constraints. We next exploit the property of the proposed optimization problem and reveal the relation between the expected batch transfer matrix rank and maximal independent sets. In the light of their relationship, we propose joint coding and scheduling rules and show that a periodic scheduling can be used to achieve provable performance guarantees. However, under most realistic coding settings, the proposed coding and scheduling problem is NP-hard. In order to meet the practical needs of the implementation, we develop greedy algorithms that attempt to iteratively improve the current best solution. Numerical results show that our algorithms enables us to approach the utilization of multi-hop wireless networks with a relatively low end-to-end delay.