Building a Controlled Delay Assured Forwarding Class in DiffServ Networks Parag Kulkarni Nazeeruddin Mohammad Sally McClean Gerard Parr Michaela Black Bryan Scotney School of Computing and Information Engineering Faculty of Engineering University of Ulster Coleraine, Northern Ireland

Overview Quality of Service (QoS) within a typical router in the internet is quantified through several parameters –e.g. Delay, Delay Variation, Packet Loss Effective Queue Management helps to improve QoS Objective of this research –To design a “ProActive Queue Management” strategy that provides quantitative controlled delay guarantees on a per hop basis across an end-to-end (e2e) path by regulating queue size around an operating point q 0 (thereby regulating delay below the control target and minimizing delay variation).

Background Service differentiation essential to support SLAs –DiffServ is the way forward due to its simplicity and scalability IETF has standardised PHBs but has not mandated particular mechanisms to realise them Focus of this work -> AF PHB –AF PHB has outlined 4 classes and 3 drop precedence levels within each class –RFC 2957 has recommended AQM to realise the drop precedence levels (to achieve differentiated dropping) –Transactional data applications (interactive) are an important class of apps requiring low delay –Currently available solutions provide only throughput guarantees –Support available only for qualitative differentiation of delay and no quantitative guarantees are offered

Design Objectives The challenge is to build an AQM scheme that can –Prevent Global Synchronisation –Capture the dynamics of the underlying system accurately in real time thereby bypassing the parameter tuning problem –Avoid the conservative approach of mathematical modelling –Achieve the performance objective (regulate the delay below the control target)

Extensions to the PAQMAN Algorithm Extended algorithm called PAQMAN-DS Supports 3 drop precedences per queue Uses same queue threshold for all 3 precedences Uses different packet drop probability computation function for each precedence Employs coupled queue lengths

PAQMAN-DS Algorithm Input - average queue size observations for all three precedences over the past interval for every PI seconds { –Compute average queue size for each precedence over the past interval –Predict average queue size for each precedence over the next interval –Compute packet drop probability (PDP) for each precedence }

PAQMAN-DS Flowchart

Action on each packet arrival

Salient features of the proposed Approach Bypasses parameter tuning by employing “Data driven Adaptive Learning Approach” Does not need any prior knowledge of the traffic model (no assumptions) Adapts well to changes in underlying traffic –RLS algorithm converges in 2N iterations Tested with different types of traffic Achieves performance objective by regulating the delay below the control target

Performance Evaluation of PAQMAN-DS Two rate three colour marker used at edge Target delay at CR = 50ms (hence, q 0 =37.5 pkts) Tested under two Scenarios involving long lived TCP and ON/OFF TCP for different subscription rates varying from 25% to 125% in steps of 25%

Performance Metrics Average delay S.D of instantaneous queue size Link Utilization Green, Yellow and RED Packet Loss Ratios

Results Performance metrics v/s subscription ratio - long lived traffic Performance metrics v/s subscription ratio – On/Off traffic Target delay = 0.05s

Summary of the Results Irrespective of the traffic load, PAQMAN-DS regulates delay below the control target as opposed to the other two approaches S.D. of PAQMAN-DS queue is low and steady in comparison to that of RIO and ARIO indicating that it is capable of minimizing delay variation It also achieves link utilization very similar to the other two approaches Leniency of RIO and ARIO result in lower Green packets being lost as compared to PAQMAN-DS

PAQMAN-DS Summary Simple Lightweight Does not make assumptions about the traffic model Capable of providing sustained and consistent QoS by regulating the delay below the control target Capable of discriminating in favour of IN contract traffic

Future Work Performance evaluation in the presence of more complex traffic mixes Performance evaluation in the presence of multiple bottleneck links