Network Utility Maximization 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.

Ubiquitous learning models for 5G communication network utility maximization through utility-based service function chain deployment

The main problem of deploying service function chains in virtualized 5G networks is dealt with in wireless 5G communications and effective ubiquitous learning models. The aim is to ensure differentiated network performance for a wide range of services while maximizing the collaborative revenue of infrastructure operators and wireless virtual operators. To achieve this, a utility-based service function chain deployment strategy is introduced, tailored to the specific characteristics of Ubiquitous Learning based 5G-RAN-Architecture. Consideration is given to the virtual operator's maximum tolerable end-to-end latency, minimum service rate requirements, and the infrastructure operator's constraints on computing and link resources. It also looks at how different deployment scenarios for service function chains affect network performance and creates a utility model using a business framework. The ultimate objective is to optimize the collective revenue of infrastructure operators and virtual operators. The approach leverages genetic algorithms and Matlab's Linprog function for iterative problem-solving. The graph clearly shows that the SFC deployment algorithm in this study uses less infrastructure resources for Front Haul links. This implies that the algorithm successfully reduces the load on Front Haul lines, which in turn lowers the cost of SFC deployment and makes it easier to deploy more SFCs across the infrastructure. This work contributes to the evolution of 5G wireless communications and its seamless integration with ubiquitous learning models.

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.

Energy Efficient Power Allocation in Massive MIMO Based on Parameterized Deep DQN

Machine learning offers advanced tools for efficient management of radio resources in modern wireless networks. In this study, we leverage a multi-agent deep reinforcement learning (DRL) approach, specifically the Parameterized Deep Q-Network (DQN), to address the challenging problem of power allocation and user association in massive multiple-input multiple-output (M-MIMO) communication networks. Our approach tackles a multi-objective optimization problem aiming to maximize network utility while meeting stringent quality of service requirements in M-MIMO networks. To address the non-convex and nonlinear nature of this problem, we introduce a novel multi-agent DQN framework. This framework defines a large action space, state space, and reward functions, enabling us to learn a near-optimal policy. Simulation results demonstrate the superiority of our Parameterized Deep DQN (PD-DQN) approach when compared to traditional DQN and RL methods. Specifically, we show that our approach outperforms traditional DQN methods in terms of convergence speed and final performance. Additionally, our approach shows 72.2% and 108.5% improvement over DQN methods and the RL method, respectively, in handling large-scale multi-agent problems in M-MIMO networks.

An Enhanced Deep Reinforcement Learning-based Slice Acceptance Control System (EDRL-SACS) for Cloud–Radio Access Network

In 5G networks, Network Slicing (NS) allows service providers (SPs) to create customized standalone networks on shared platforms provided by infrastructure providers (InPs). However, the challenge arises when prioritizing slice requests sent by SPs to InPs due to limited and shared resources. The Cloud Radio Access Network (C-RAN) architecture is a novel approach that supports various services in 5G networks, but efficient resource utilization and increased revenue across the three layers of C-RAN are necessary. To address this issue, we propose an Enhanced Deep Reinforcement Learning-based Slice Acceptance Control System (EDRL-SACS) technique to maximize network utility by accepting suitable requests. EDRL-SACS considers time-varying resource situations, various service requirements, and the frequency of requests from SPs to endorse model-free solutions for Slice Acceptance Control (SAC). By adding effective parameters to the deep reinforcement learning algorithm, the proposed methodology improves its efficiency and performance resulting in better resource utilization, coverage, revenue, and rejection of users’ request terms. The system’s evaluation shows that EDRL-SACS performs well and effectively maximizes the utility of the network by providing slices to SPs in response to their slice requests.

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 Active-Passive Beamforming and User Association in IRS-Assisted mmWave Cellular Networks

Intelligent reflecting surfaces (IRSs) are a promising technology for future-generation wireless networks by extending coverage region to blind spots and increasing mmWave propagation paths in non-line of sight environments. User association (UA) in dense millimeter wave (mmWave) networks is vital to characterizing connections among base stations (BSs) and mobile users for load balancing, interference management, and maximizing network utility. However, it has yet to be examined thoroughly in a multi-IRS-aided network. This paper presents a new UA scheme that takes cell interference into account for a multi-cell mmWave cellular network aided with multiple IRSs. We formulate a network spectral efficiency maximization problem by jointly optimizing active beamforming (AB) at the BSs, passive beamforming (PB) at the IRSs, and user-BS association with consideration of the impact of IRSs. We then propose a computationally efficient iterative algorithm based on alternating optimization (AO) to resolve this intractable mixed-integer non-convex problem. A fractional programming technique is used to optimize active beamforming at the BSs and passive beamforming at the IRSs, and a penalization method combined with successive convex programming is applied for UA optimization, which is shown to reach the optimal solution. Simulation results show significant performance improvements obtained by the proposed algorithm, providing higher spectral efficiency compared to several benchmark algorithms, while having a low computational complexity.

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.

Two-Hop Cooperative Caching and UAVs Deployment Based on Potential Game

This paper explores the joint cache placement and 3D deployment of Unmanned Aerial Vehicle (UAV) groups, utilizing potential game theory and a two-hop UAV cooperative caching mechanism, which could create a tradeoff between latency and coverage. The proposed scheme consists of three parts: first, the initial 2D location of UAV groups is determined through K-means, with the optimal altitude based on the UAV coverage radius. Second, to balance the transmission delay and coverage, the MOS (Mean Opinion Score) and coverage are designed to evaluate the performance of UAV-assisted networks. Then, the potential game is modeled, which transfers the optimization problem into the maximization of the whole network utility. The locally coupling effect resulting from action changes among UAVs is considered in the design of the potential game utility function. Moreover, a log-linear learning scheme is applied to solve the problem. Finally, the simulation results verify the superiority of the proposed scheme in terms of the achievable transmission delay and coverage performance compared with two other tested schemes. The coverage ratio is close to 100% when the UAV number is 25, and the user number is 150; in addition, this game outperforms the benchmarks when it comes to maximizing MOS of users.

Switching in the Rain: Predictive Wireless x-haul Network Reconfiguration

4G, 5G, and smart city networks often rely on microwave and millimeter-wave x-haul links. A major challenge associated with these high frequency links is their susceptibility to weather conditions. In particular, precipitation may cause severe signal attenuation, which significantly degrades the network performance. In this paper, we develop a Predictive Network Reconfiguration (PNR) framework that uses historical data to predict the future condition of each link and then prepares the network ahead of time for imminent disturbances. The PNR framework has two components: (i) an Attenuation Prediction (AP) mechanism; and (ii) a Multi-Step Network Reconfiguration (MSNR) algorithm. The AP mechanism employs an encoder-decoder Long Short-Term Memory (LSTM) model to predict the sequence of future attenuation levels of each link. The MSNR algorithm leverages these predictions to dynamically optimize routing and admission control decisions aiming to maximize network utilization, while preserving max-min fairness among the nodes using the network (e.g., base-stations) and preventing transient congestion that may be caused by switching routes. We train, validate, and evaluate the PNR framework using a dataset containing over 2 million measurements collected from a real-world city-scale backhaul network. The results show that the framework: (i) predicts attenuation with high accuracy, with an RMSE of less than 0.4 dB for a prediction horizon of 50 seconds; and (ii) can improve the instantaneous network utilization by more than 200% when compared to reactive network reconfiguration algorithms that cannot leverage information about future disturbances. The full paper associated with this abstract can be found at https://doi.org/10.1145/3570616.

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.

Leveraging Deep Reinforcement Learning With Attention Mechanism for Virtual Network Function Placement and Routing

The efficacy of Network Function Virtualization (NFV) depends critically on (1) where the virtual network functions (VNFs) are placed and (2) how the traffic is routed. Unfortunately, these aspects are not easily optimized, especially under time-varying network states with different QoS requirements. Given the importance of NFV, many approaches have been proposed to solve the VNF placement and Service Function Chaining (SFC) routing problem. However, those prior approaches mainly assume that the network state is static and known, disregarding dynamic network variations. To bridge that gap, we leverage Markov Decision Process (MDP) to model the dynamic network state transitions. To jointly minimize the delay and cost of NFV providers and maximize the revenue, we first devise a customized Deep Reinforcement Learning (DRL) algorithm for the VNF placement problem. The algorithm uses the attention mechanism to ascertain smooth network behavior within the general framework of network utility maximization (NUM). We then propose attention mechanism-based DRL algorithm for the SFC routing problem, which is to find the path to deliver traffic for the VNFs placed on different nodes. The simulation results show that our proposed algorithms outperform the state-of-the-art algorithms in terms of network utility, delay, cost, and acceptance ratio.

Deep Recurrent Reinforcement Learning-Based Distributed Dynamic Spectrum Access in Multichannel Wireless Networks With Imperfect Feedback

This paper investigates Dynamic Spectrum Access (DSA) paradigm with imperfect feedback for multiuser wireless network. Each user selects an orthogonal channel in particular time slot to transmit packet with a certain transmission probability. In next time slot, the user who has transmitted a packet receives an ACK signal based on local observation. Bearing in mind the dynamic nature of wireless networks, it is appealing to develop a blended strategy to perform effective DSA. This paper aims to design a distributed Deep Reinforcement Learning (DRL) based scheme with an objective of maximizing network utility. In conventional DRL framework, it is assumed that the feedback received (ACK packet) is always correct but in wireless networks, it may be lost or corrupted due to noise. Furthermore, it is challenging to promise that the multiple agents will cooperate to make coherent decisions in order to accomplish the same objective, particularly under imperfect feedback. To tackle these challenges, this work proposes (i) Deep Recurrent Reinforcement Learning network with integrated GRU layer to optimize the network utility function and (ii) a feedback recovery mechanism using complete and incomplete replay buffers. Extensive simulations corroborate the success of proposed scheme in complex multiuser scenario and exhibit robustness against the detrimental effects of the imperfect feedback.

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.

Radio resource management in energy harvesting cooperative cognitive UAV assisted IoT networks: A multi-objective approach

Cooperative communication through energy harvested relays in Cognitive Internet of Things (CIoT) has been envisioned as a promising solution to support massive connectivity of Cognitive Radio (CR) based IoT devices and to achieve maximal energy and spectral efficiency in upcoming wireless systems. In this work, a cooperative CIoT system is contemplated, in which a source acts as a satellite, communicating with multiple CIoT devices over numerous relays. Unmanned Aerial Vehicles (UAVs) are used as relays, which are equipped with onboard Energy Harvesting (EH) facility. We adopted a Power Splitting (PS) method for EH at relays, which are harvested from the Radio frequency (RF) signals. In conjunction with this, the Decode and Forward (DF) relaying strategy is used at UAV relays to transmit the messages from the satellite source to the CIoT devices. We developed a Multi-Objective Optimization (MOO) framework for joint optimization of source power allocation, CIoT device selection, UAV relay assignment, and PS ratio determination. We formulated three objectives: maximizing the sum rate and the number of admitted CIoT in the network and minimizing the carbon dioxide emission. The MOO formulation is a Mixed-Integer Non-Linear Programming (MINLP) problem, which is challenging to solve. To address the joint optimization problem for an epsilon optimal solution, an Outer Approximation Algorithm (OAA) is proposed with reduced complexity. The simulation results show that the proposed OAA is superior in terms of CIoT device selection and network utility maximization when compared to those obtained using the Nonlinear Optimization with Mesh Adaptive Direct-search (NOMAD) algorithm.

Combination of Throughput-optimal Scheduling and Network Utility Maximization in NOMA Systems with Flow-Level Dynamics

In wireless networks, the network queue stability (throughput-optimality) and the network utility maximization are two crucial tasks for network operators. Besides, new techniques of mobile communications, e.g. the non-orthogonal multiple access (NOMA), have emerged endlessly to meet coming challenges. In this paper, we for the first time, investigate a downlink NOMA system with flow-level dynamics, where long-lived flows and short-lived flows exist simultaneously, and formulate an optimization problem of user selection and power allocation. We aim to maximize the network utility at each time slot and simultaneously guarantee the throughput-optimality, under the quality of service (QoS) constraints. Considering the practicability of algorithm, the suboptimal algorithm containing two stages, is proposed. And we analyze the complexity of proposed algorithm. In the performance evaluation, the benchmark algorithm is provided for comparison. And we take the energy efficiency (EE) as an example of the network utility and define the scheduling delay (SD). Simulation results show that the suboptimal algorithm is throughput-optimal, and understandably, its performances could be better than those in the benchmark algorithm in terms of EE and SD, as long as the appropriate parameter is set.

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.

Learning-NUM: Network Utility Maximization With Unknown Utility Functions and Queueing Delay

Network Utility Maximization (NUM) studies the problems of allocating traffic rates to network users in order to maximize the users’ total utility subject to network resource constraints. In this paper, we propose a new NUM framework, Learning-NUM, where the users’ utility functions are unknown apriori and the utility function values of the traffic rates can be observed only after the corresponding traffic is delivered to the destination, which means that the utility feedback experiences queueing delay. The goal is to design a policy that gradually learns the utility functions and makes rate allocation and network scheduling/routing decisions so as to maximize the total utility obtained over a finite time horizon <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> . In addition to unknown utility functions and stochastic constraints, a central challenge of our problem lies in the queueing delay of the observations, which may be unbounded and depends on the decisions of the policy. We first show that the expected total utility obtained by the best dynamic policy is upper bounded by the solution to a static optimization problem. Without the presence of feedback delay, we design an algorithm based on the ideas of gradient estimation and Max-Weight scheduling. To handle the feedback delay, we embed the algorithm in a parallel-instance paradigm to form a policy that achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\tilde {O}(T^{3/4})$ </tex-math></inline-formula> -regret, i.e., the difference between the expected utility obtained by the best dynamic policy and our policy is in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\tilde {O}(T^{3/4})$ </tex-math></inline-formula> . Furthermore, we extend our policy to deal with the case where the utility observations are noisy and show that it achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\tilde {O}(T^{7/8})$ </tex-math></inline-formula> -regret. Finally, to demonstrate the practical applicability of the Learning-NUM framework, we apply it to three application scenarios including database query, job scheduling and video streaming. We further conduct simulations on the job scheduling application to evaluate the empirical performance of our policy.

Approximate Aggregate Utility Maximization Using Greedy Maximal Scheduling

In this paper, we study the problem of aggregate utility maximization in multihop wireless networks. Following standard approaches, we consider the dual of a convex optimization problem that can be decomposed into two sub-problems. One of the sub-problem is a scheduling problem that requires enumeration of all maximal independent sets in the network—a well-known NP-hard problem. To overcome this computational complexity, we resort to polynomial-time sub-optimal Greedy Maximal Scheduling (GMS). The majority of this paper is dedicated towards quantifying the sub-optimality that arises in the aggregate utility maximization problem due to GMS. We also provide some insights into factors affecting aggregate utility maximization by providing bounds on the maximum achievable aggregate utility.