Network utility maximization for delay-sensitive applications in unknown communication settings

In the last decades the Internet traffic has greatly evolved. The advent of new Internet services and applications has, in fact, led to a significant growth of the amount of data transmitted, as well as to a transformation of the data type. As a matter of fact, nowadays, the largest amount of traffic share consists of multimedia data, which do not represent classical Internet data. Due to the increasing amount of traffic, the network resources might be scarce, and in such cases it becomes extremely important to optimize network transmission in order to provide a satisfying service to the users. Although methods for maximizing the network utility in scenarios with limited resources have been studied extensively, the evolution of the Internet services poses continuously new challenges that require novel solution methods to meet the transmission requirements. In this thesis we propose novel solutions methods to network utility maximization problems that arise in the context of nowadays network communications. In particular we analyze problems related to delay-sensitive Internet applications and rate allocation in unknown network settings. In the first problem we study how to effectively allocate the transmission rates in a multiparty videoconference system. The main contribution of this chapter is an approximate fast rate rate allocation method that is able to adapt quickly to changes in the videoconference conditions. This fast adaptation cannot be achieved with classical network utility maximization solving methods, as they are usually based on iterative approaches. In this case we leverage the particular structure of the problem to design a novel distributed solving method which proves to be very effective when compared to baseline solutions. The next problem that we address is the design of a congestion control algorithm for delay-sensitive applications. One of the main problems of existing delay-based congestion control algorithms is that they tend to achieve an extremely low throughput when competing against loss-based algorithms. In order to overcome this difficulty we propose a novel adaptive controller based on a bandit problem approach. The adaptive controller tries to infer how the network responds, in terms of rate-delay pair at equilibrium, when changing the delay sensitivity of an underlying delay-based congestion control. Once the network response is inferred, the controller selects the sensitivity that leads to the best trade-off between the transmitting rate and the experienced delay. In the final problem, we analyze the design of an overlay rate allocation systems to be used when: the amount of available network resources is not known, and the user congestion feedback cannot be used as valid signal to reach the optimal rate allocation. Such a scenario appears when an Internet application wants to maximize a certain utility metric, but, at the same time, it must operate using a specific congestion control algorithm that is completely unaware of the application utility. To solve this problem we design a distributed system that coordinates the users in order to perform active learning on the amount of network resource. Adopting such a method reveals to be the key to an effective maximization of the long term application utility for the entire system

Network Utility Maximization under Maximum Delay Constraints and Throughput Requirements

We consider the problem of maximizing aggregate user utilities over a multi-hop network, subject to link capacity constraints, maximum end-to-end delay constraints, and user throughput requirements. A user's utility is a concave function of the achieved throughput or the experienced maximum delay. The problem is important for supporting real-time multimedia traffic, and is uniquely challenging due to the need of simultaneously considering maximum delay constraints and throughput requirements. We first show that it is NP-complete either (i) to construct a feasible solution strictly meeting all constraints, or (ii) to obtain an optimal solution after we relax maximum delay constraints or throughput requirements up to constant ratios. We then develop a polynomial-time approximation algorithm named PASS. The design of PASS leverages a novel understanding between non-convex maximum-delay-aware problems and their convex average-delay-aware counterparts, which can be of independent interest and suggest a new avenue for solving maximum-delay-aware network optimization problems. Under realistic conditions, PASS achieves constant or problem-dependent approximation ratios, at the cost of violating maximum delay constraints or throughput requirements by up to constant or problem-dependent ratios. PASS is practically useful since the conditions for PASS are satisfied in many popular application scenarios. We empirically evaluate PASS using extensive simulations of supporting video-conferencing traffic across Amazon EC2 datacenters. Compared to existing algorithms and a conceivable baseline, PASS obtains up to $100\%$ improvement of utilities, by meeting the throughput requirements but relaxing the maximum delay constraints that are acceptable for practical video conferencing applications

Joint Scheduling and ARQ for MU-MIMO Downlink in the Presence of Inter-Cell Interference

User scheduling and multiuser multi-antenna (MU-MIMO) transmission are at the core of high rate data-oriented downlink schemes of the next-generation of cellular systems (e.g., LTE-Advanced). Scheduling selects groups of users according to their channels vector directions and SINR levels. However, when scheduling is applied independently in each cell, the inter-cell interference (ICI) power at each user receiver is not known in advance since it changes at each new scheduling slot depending on the scheduling decisions of all interfering base stations. In order to cope with this uncertainty, we consider the joint operation of scheduling, MU-MIMO beamforming and Automatic Repeat reQuest (ARQ). We develop a game-theoretic framework for this problem and build on stochastic optimization techniques in order to find optimal scheduling and ARQ schemes. Particularizing our framework to the case of "outage service rates", we obtain a scheme based on adaptive variable-rate coding at the physical layer, combined with ARQ at the Logical Link Control (ARQ-LLC). Then, we present a novel scheme based on incremental redundancy Hybrid ARQ (HARQ) that is able to achieve a throughput performance arbitrarily close to the "genie-aided service rates", with no need for a genie that provides non-causally the ICI power levels. The novel HARQ scheme is both easier to implement and superior in performance with respect to the conventional combination of adaptive variable-rate coding and ARQ-LLC.Comment: Submitted to IEEE Transactions on Communications, v2: small correction