A Sliding Mode-based congestion control for time delayed differentiated-services networks

Abstract

In this paper robust control techniques are used and investigated for congestion control problem of time-delayed scalable differentiated Services (DiffServ) networks. The robustness capabilities of sliding mode variable structure control (SM-VSC) technique are utilized as a design basis for a new congestion control strategy subject to inaccurate/uncertain network model. The fluid flow model (FFM) adopted in this paper is of low order and simpler than a detailed Markovian queuing probabilistic models. The proposed time-delayed dependent congestion control dynamics is studied analytically to guarantee stability of the closed-loop system. To evaluate the capabilities of our proposed robust control strategy, simulation results are provided for different operating conditions and time-delay values.

A robust adaptive congestion control strategy for large scale networks with differentiated services traffic

“…The three edge nodes 1, 2 and 3 can communicate with each other to share the information among the three clusters. This network configuration is quite general and can be found in many applications such as sensor/actuator networks, cooperative team of unmanned vehicles [15], [16], [17], [18], and high speed Ethernet networks. For our simulation studies we implement the network behavior by an event-based simulator tool known as QualNet [25] software environment.…”

“…Consequently, the presence of unknown and time-varying delays and latencies are not considered in the design of the congestion control scheme. The lack of explicit consideration of the delays will yield a critical challenge and even an instability when the approach is applied to a large scale network consisting of many nodes structured in arbitrary configurations containing feedback [15], [16], [17], [18].…”

Robust nonlinear congestion controller for cognitive radio based wireless network

“…The analytical modelling of the CR network is done us ing queuing model or Fluid Flow Model [8], [9]. A set of nonlinear differential equations are presented to describe the time varying behavior of the mean queue lengths at various network queues.…”

“…development of a stable hyper surface that is the geometrical locus consisting of boundaries, and formulation of the control law that is the combination of equivalent and corrective control. Let us define the surface variable as n a(x ) = L CiXi = CIXI + C2X2 = cT X i =1 (9) with C i that are weighting parameters affecting the system trajectory and states [12] and in turn, its performance. Choice of C i has been done by minimizing the quadratic cost function based on optimal integral rule.…”