Figure 1: The integrated framework
Traffic Management and QoS Issues: Broadband
(ATM) Networks
The real-time video traffic is expected to occupy a large bandwidth of
broadband networks. Dynamic bandwidth allocation schemes are necessary in
order to efficiently utilize the network resources (e.g., bandwidth,
buffers) and maximize the number of video sessions that can be supported
with existing resources. Proper online adaptation to the changing
bandwidth requirements of the highly correlated video traffic at regular
intervals is necessary to achieve the desired queue performance. This
issue of reducing the complexity and yet providing better response time
for effectively supporting real-time traffic in high-speed ATM networks,
is one of our key research goals.
The Advance reservations for the resources (bandwidth) has been an active area of research. Most of the current techniques may be categorized as semi-static over a certain period of time until further renegotiation for bandwidth takes place. The dynamic adaptability with the changing bandwidth requirements based on online traffic measurements is open for investigation. Even with the semi-static resource allocations, once the bandwidth is reserved for a session, the utilization of the bandwidth may be of concern if the reserved bandwidth is not fully utilized. This is where we need short-term controlling mechanisms for bandwidth allocation in order to maximize the utilization. The cell-level scheduling facilitates such short-term controlling mechanisms. Frequent renegotiations may not be possible in an ATM network environment where bandwidth requirements for both ongoing sessions and sessions that are in call-admission phase are changing. The online traffic measurement-based adaptive bandwidth allocation schemes are essential for a quick adaptation to the changing traffic rates and minimizing the number of renegotiations for bandwidth, especially in the context of highly correlated VBR video traffic. The linear prediction of video traffic is being investigated using autoregressive (AR) models. It is usually assumed that the nodes (ATM switches) will reserve the bandwidth as requested by predictors. This may not be the situation when we multiplex a number of video sessions sharing the bandwidth (statistical multiplexing gain). The other issue that arises from prediction of video traffic is the effect of error in traffic estimation on the queues. Our video traffic management architecture addresses these issues.
The correlation properties of video traffic make predictor-based bandwidth allocation schemes attractive. A novel predictor-based architecture that addresses the above issues through a dynamic bandwidth allocation at burst level (milliseconds) and a short-term resource management through cell-scheduling (micro secs) for video traffic has been proposed and analyzed. In order to reduce computational requirements, linear predictors (regressive) of VBR video traffic were employed and to minimize the ill-effects of traffic prediction errors on queueing system, a short-term controller was used to provide a quick reactive control (overestimated bandwidth of some of the sessions is shared among the underestimated sessions). It has been shown that dynamic adaptive techniques outperform static allocation schemes in terms of queue lengths and delays. We propose an integrated framework for VBR video traffic management based on traffic prediction that facilitates the online adaptation to the changing traffic rates as shown in Figure 1.
Figure 1: The integrated framework
The correlation properties of the VBR video traffic make traffic prediction possible and based on the predictor estimates online adaptation to traffic rates can be acheived. Based on the traffic estimates for future adaptation intervals, the predictor system dynamically allocates the bandwidth to various ongoing video sessions. The short-term controller (STC) works at cell-level scheduling while the predictor system works at the burst-level (frame or few tens of milliseconds). The purpose of the STC is to reduce the effects of prediction errors on the queues of individual sessions and also at the same time exploit the statistical multiplexing gain across the sessions. Figure 2 depicts proposed Predictor and Short-term controller (Predictor-STC) architecture. The predictor module (PM) estimates the amount of bandwidth of various sessions, and accordingly reserves the bandwidth per session. The STC schedules the cells according to the reserved bandwidth by the PM, but provides a quick reactive control to the errors in bandwidth estimation of the PM, through cell/slot adjustments of various sessions. Thus STC is very useful as there will be a certain amount of error in the estimation of the long term predictor. The STC plays a crucial role in maximizing the statistical multiplexing gain. The traffic of an underestimated session is served with the minimum bandwidth MinBW, in addition to STC adjustments from the overestimates of other session's bandwidth. This can also be helpful in fine tuning the QoS provided. The STC also acts as a traffic regulator conforming to the peak-rates of various sessions. Thus STC provides a reactive control and is indispensable in the predictor-based server architectures. This approach also gives a closer way of looking at the sessions instead of treating them as en-masse sessions of various classes and provides a mechanism of online adaptation to changing rates of VBR video traffic.
Figure 2: The Predictor-STC system
The total capacity (bandwidth) that is available for the predictor system for the allocation among the sessions is obtained from the Call-admitter. During the call-admission phase, the Call-admitter relies on prior statistical characterization of the prospective video sessions that may not be accurate enough leading to an approximate estimation of bandwidth requirement. Thus the QoS Change and Bandwidth Renegotiation (QSCBR) module plays an important role for the necessary renegotiations for the desired bandwidth that may be needed by the sessions after admission. The Predictor-STC system provides a mechanism for online adaptation to traffic rates of individual sessions and at the same time exploits the statistical gain across the sessions thereby decreasing the need for frequent bandwidth renegotiations. The change in end-user based picture quality requirements are considered as QoS changes (equivalently translated to the bandwidth requirements) of the ongoing video sessions that are taken care by the QSCBR module. Work is underway to further study the decrease in bandwidth renegotiations that are possible due to the exploitation of statistical gain across the sessions.
For proliferation of ATM to the desktop, it is critical that legacy LAN internetwork traffic is supported over the public ATM network. Several techniques that provide LAN interconnetivity via ATM include: tunneling, maintaining permanent ATM resources in the ATM network, and acquiring resources on-demand. However, these schemes have their own pros and cons in supporting LAN traffic requiring QoS quarantees.
The primary goal of this research is to develop an integrated approach based on Demand Allocation with Channel Reuse for the transportation of Inter-LAN traffic over ATM.
Congestion Control for ABR Services in ATM
Networks
