Equilibrium bandwidth and buffer allocations for elastic traffics

Consider a set of users sharing a network node under an allocation scheme that provides each user with a fixed minimum and a random extra amount of bandwidth and buffer. Allocations and prices are adjusted to adapt to resource availability and user demands. Equilibrium is achieved when all users optimize their utility and demand equals supply for nonfree resources. We analyze two models of user behavior. We show that at equilibrium expected return on purchasing variable resources can be higher than that on fixed resources. Thus users must balance the marginal increase in utility due to higher return on variable resources and the marginal decrease in utility due to their variability. For the first user model we further show that at equilibrium where such tradeoff is optimized all users hold strictly positive amounts of variable bandwidth and buffer. For the second model we show that if both variable bandwidth and buffer are scarce then at equilibrium every user either holds both variable resources or none

Optimization flow control -- I: Basic algorithm and convergence

We propose an optimization approach to flow control where the objective is to maximize the aggregate source utility over their transmission rates. We view network links and sources as processors of a distributed computation system to solve the dual problem using a gradient projection algorithm. In this system, sources select transmission rates that maximize their own benefits, utility minus bandwidth cost, and network links adjust bandwidth prices to coordinate the sources' decisions. We allow feedback delays to be different, substantial, and time varying, and links and sources to update at different times and with different frequencies. We provide asynchronous distributed algorithms and prove their convergence in a static environment. We present measurements obtained from a preliminary prototype to illustrate the convergence of the algorithm in a slowly time-varying environment. We discuss its fairness property

Joint rate control and scheduling for providing bounded delay with high efficiency in multihop wireless networks

This thesis considers the problem of supporting traffic with elastic bandwidth requirements and hard end-to-end delay constraints in multi-hop wireless networks, with focus on source transmission rates and link data rates as the key resource allocation decisions. Specifically, the research objective is to develop a source rate control and scheduling strategy that guarantees bounded average end-to-end queueing delay and maximises the overall utility of all incoming traffic, using network utility maximisation framework. The network utility maximisation based approaches to support delay-sensitive traffic have been predominantly based on either reducing link utilisation, or approximation of links as M/D/1 queues. Both approaches lead to unpredictable transient behaviour of packet delays, and inefficient link utilisation under optimal resource allocation. On the contrary, in this thesis an approach is proposed where instead of hard delay constraints based on inaccurate M/D/1 delay estimates, traffic end-to-end delay requirements are guaranteed by proper forms of concave and increasing utility functions of their transmission rates. Specifically, an alternative formulation is presented where the delay constraint is omitted and sources’ utility functions are multiplied by a weight factor. The alternative optimisation problem is solved by a distributed scheduling algorithm incorporating a duality-based rate control algorithm at its inner layer, where optimal link prices correlate with their average queueing delays. The proposed approach is then realised by a scheduling algorithm that runs jointly with an integral controller whereby each source regulates the queueing delay on its paths at the desired level, using its utility weight coefficient as the control variable. Since the proposed algorithms are based on solving the alternative concave optimisation problem, they are simple, distributed and lead to maximal link utilisation. Hence, they avoid the limitations of the previous approaches. The proposed algorithms are shown, using both theoretical analysis and simulation, to achieve asymptotic regulation of end-to-end delay given the step size of the proposed integral controller is within a specified range

Learning algorithms for the control of routing in integrated service communication networks

There is a high degree of uncertainty regarding the nature of traffic on future integrated service networks. This uncertainty motivates the use of adaptive resource allocation policies that can take advantage of the statistical fluctuations in the traffic demands. The adaptive control mechanisms must be 'lightweight', in terms of their overheads, and scale to potentially large networks with many traffic flows. Adaptive routing is one form of adaptive resource allocation, and this thesis considers the application of Stochastic Learning Automata (SLA) for distributed, lightweight adaptive routing in future integrated service communication networks. The thesis begins with a broad critical review of the use of Artificial Intelligence (AI) techniques applied to the control of communication networks. Detailed simulation models of integrated service networks are then constructed, and learning automata based routing is compared with traditional techniques on large scale networks. Learning automata are examined for the 'Quality-of-Service' (QoS) routing problem in realistic network topologies, where flows may be routed in the network subject to multiple QoS metrics, such as bandwidth and delay. It is found that learning automata based routing gives considerable blocking probability improvements over shortest path routing, despite only using local connectivity information and a simple probabilistic updating strategy. Furthermore, automata are considered for routing in more complex environments spanning issues such as multi-rate traffic, trunk reservation, routing over multiple domains, routing in high bandwidth-delay product networks and the use of learning automata as a background learning process. Automata are also examined for routing of both 'real-time' and 'non-real-time' traffics in an integrated traffic environment, where the non-real-time traffic has access to the bandwidth 'left over' by the real-time traffic. It is found that adopting learning automata for the routing of the real-time traffic may improve the performance to both real and non-real-time traffics under certain conditions. In addition, it is found that one set of learning automata may route both traffic types satisfactorily. Automata are considered for the routing of multicast connections in receiver-oriented, dynamic environments, where receivers may join and leave the multicast sessions dynamically. Automata are shown to be able to minimise the average delay or the total cost of the resulting trees using the appropriate feedback from the environment. Automata provide a distributed solution to the dynamic multicast problem, requiring purely local connectivity information and a simple updating strategy. Finally, automata are considered for the routing of multicast connections that require QoS guarantees, again in receiver-oriented dynamic environments. It is found that the distributed application of learning automata leads to considerably lower blocking probabilities than a shortest path tree approach, due to a combination of load balancing and minimum cost behaviour