Optimization flow control with Newton-like algorithm

We proposed earlier an optimization approach to reactive flow control where the objective of the control is to maximize the aggregate utility of all sources over their transmission rates. The control mechanism is derived as a gradient projection algorithm to solve the dual problem. In this paper we extend the algorithm to a scaled gradient projection. The diagonal scaling matrix approximates the diagonal terms of the Hessian and can be computed at individual links using the same information required by the unscaled algorithm. We prove the convergence of the scaled algorithm and present simulation results that illustrate its superiority to the unscaled algorithm

Simulation comparison of RED and REM

We propose earlier an optimization based low control for the Internet called Random Exponential Marking (REM). REM consists of a link algorithm, that probabilistically marks packets inside the network, and a source algorithm, that adapts source rate to observed marking. The marking probability is exponential in a link congestion measure, so that the end-to-end marking probability is exponential in a path congestion measure. Because of the finer measure of congestion provided by REM, sources do not constantly probe the network for spare capacity, but settle around a globally optimal equilibrium, thus avoiding the perpetual cycle of sinking into and recovering from congestion. In this paper we compare the performance of REM with Reno over RED through simulation

An enhanced random early marking algorithm for Internet flow control

We propose earlier an optimization based flow control for the Internet called Random Early Marking (REM). In this paper we propose and evaluate an enhancement that attempts to speed up the convergence of REM in the face of large feedback delays. REM can be regarded as an implementation of an optimization algorithm in a distributed network. The basic idea is to treat the optimization algorithm as a discrete time system and apply linear control techniques to stabilize its transient. We show that the modified algorithm is stable globally and converges exponentially locally. This algorithm translates into an enhanced REM scheme and we illustrate the performance improvement through simulation

An empirical validation of a duality model of TCP and queue management algorithms

In this paper we validate through simulations a duality model of TCP and active queue management (AQM) proposed earlier. In this model, TCP and AQM are modeled as carrying out a distributed primal-dual algorithm over the Internet to maximize aggregate source utility. TCP congestion avoidance algorithms, such as Reno and Vegas, iterate on source rates, the primal variable. AQM algorithms, such as RED and REM, iterate on marking probability, the dual variable

TCP Behavior in Quality of Service Networks

Best effort networks fail to deliver the level of service emerging Internet applications demand. As a result many networks are being transformed to Quality of Service (QoS) networks, of which most are Differentiated Services (DiffServ) networks. While the deployment of such networks has been feasible, it is extremely difficult to overhaul the transport layer protocols such as Transmission Control Protocol (TCP) running on hundreds of millions of end nodes around the world. TCP, which has been designed to run on a best effort network, perform poorly in a DiffServ network. It fails to deliver the performance guarantees expected of DiffServ. In this thesis we investigate two aspects of TCP performance in a DiffServ network unaccounted for in previous studies. We develop a deterministic model of TCP that intrinsically captures flow aggregation, a key component of DiffServ. The other important aspect of TCP considered in this thesis is its' transient behavior. Using our deterministic model we derive a classical control system model of TCP applicable in a DiffServ network. Performance issues of TCP can potentially inhibit the adoption of DiffServ. A DiffServ network commonly use token buckets, that are placed at the edge of the network, to mark packets according to their conformance to Service Level Agreements (SLA). We propose two token bucket variants designed to mitigate TCP issues present in a DiffServ network. Our first proposal incorporates a packet queue alongside the token bucket. The other proposal introduces a feedback controller around the token bucket. We validate both analytically and experimentally the performance of the proposed token buckets. By confining our changes to the token bucket we avoid any changes at end-nodes. The proposed token buckets can also be incrementally deployed. Most part of the Internet still remains as a best effort network. However, most nodes run various QoS functions locally. We look at one such important QoS function, i.e. the ability to survive against flows that are non-responsive to congestion, the equivalent of a Denial of Service (DoS) attack. We analyze existing techniques and propose improvements

Faster parameter estimation using risk-sensitive filters

In this paper, we propose a risk-sensitive approach to parameter estimation for hidden Markov models (HMMs). The parameter estimation approach considered exploits estimation of various functions of the state, based on model estimates. We propose certain practical suboptimal risk-sensitive filters to estimate the various functions of the state during transients, rather than optimal risk-neutral filters as in earlier studies. The estimates are asymptotically optimal, if asymptotically risk neutral, and can give significantly improved transient performance, which is a very desirable objective for certain engineering applications. To demonstrate the improvement in estimation simulation studies are presented that compare parameter estimation based on risk-sensitive filters with estimation based on risk-neutral filters