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

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

Distributed Rate Allocation in Switch-Based Multiparty Videoconferencing System

Multiparty videoconferences, or more generally multiparty video calls, are gaining a lot of popularity as they offer a rich communication experience. These applications have, however, large requirements in terms of both network and computational resources and have to deal with sets of heterogenous clients. The multiparty videoconferencing systems are usually either based on expensive central nodes, called Multipoint Control Units (MCU), with transcoding capabilities, or on a peer-to-peer architecture where users cooperate to distribute more efficiently the different video streams. Whereas the first class of systems requires an expensive central hardware, the second one depends completely on the redistribution capacity of the users, which sometimes might neither provide sufficient bandwidth nor be reliable enough. In this work we propose an alternative solution where we use a central node to distribute the video streams, but at the same time we maintain the hardware complexity and the computational requirements of this node as low as possible, e.g. it has no video decoding capabilities. We formulate the rate allocation problem as an optimization problem that aims at maximizing the Quality of Service (QoS) of the videoconference. We propose two different distributed algorithms for solving the optimization problem: the first algorithm is able to find an approximate solution of the problem in a one-shot execution, whereas the second algorithm, based on Lagrangian relaxation, performs iterative updates of the optimization variables in order to gradually increase the value of the objective function. The two algorithms, though being disjointed, nicely complement each other. If executed in sequence, they allow to achieve both, a quick approximate rate reallocation in case of a sudden change of the system conditions, and a precise refinement of the variables which avoids problems caused by possible faulty approximate solutions. We have further implemented our solution in a network simulator where we show that our rate allocation algorithm is able to properly optimize users' QoS. We also illustrate the benefits of our solution in terms of network usage and overall utility when compared to a baseline heuristic method operating on the same system architecture

Price-based Controller for Quality-Fair HTTP Adaptive Streaming

HTTP adaptive streaming (HAS) has become the universal technology for video streaming over the Internet. Many HAS system designs aim at sharing the network bandwidth in a rate-fair manner. However, rate fairness is in general not equivalent to quality fairness as different video sequences might have different characteristics and resource requirements. In this work, we focus on this limitation and propose a novel controller for HAS clients that is able to reach quality fairness while preserving the main characteristics of HAS systems and with a limited support from the network devices. In particular, we adopt a price-based mechanism in order to build a controller that maximizes the aggregate video quality for a set of HAS clients that share a common bottleneck. When network resources are scarce, the clients with simple video sequences reduce the requested bitrate in favor of users that subscribe to more complex video sequences, leading to a more efficient network usage. The proposed controller has been implemented in a network simulator, and the simulation results demonstrate its ability to share the available bandwidth among the HAS users in a quality-fair manner