Huirong Fu and Edward W. Knightly Rice Networks Group Aggregation and Scalable QoS: A Performance Study

Edward W. Knightly Problem: Scalability of Admission Control l Goal: provide predictable and controlled performance to Internet flows l Limitations of current approaches –Intserv requires state communication and storage for each flow  Scalability and deployability limitations –Diffserv is simple and scalable but cannot quantify or control flow service quality (unless over-provisioned)  Weaker service model

Edward W. Knightly Can We Simultaneously Achieve...? l High utilization l Scalability (not micro-managing flows) l Strong service model (e.g., suitable for VOIP) –Internet (YYN) –Phone Network (NNY) –Intserv/ATM (YNY) –Diffserv (YYN)

Edward W. Knightly IntServ with Aggregation l Ingress routers make “bulk” or aggregate core resv. –adjust as necessary l Core routers do not manage state, process signaling messages, and make reservations for every flow

Edward W. Knightly How Effective is Aggregation? … It Depends… l One extreme: traffic demand is relatively constant –Rarely signal core to adjust aggregate reservation –Achieve all three! l Other extreme: demand varies quickly and dramatically (rapid and highly variable flow arrivals and departures) 1.True demand mismatches aggregate reservation  Incorrectly block flows and under utilize network 2.Rapidly adjust aggregate reservation to track demand  Lose signaling gain, default back to unscalable Intserv Important role of timescales and variance of the traffic demand

Edward W. Knightly Outline l Simple traffic and theoretical model to study aggregation l Validation and basic conclusions on timescales and variance l Remove assumptions of the basic model via simulations –Other primary demand functions –Correlation in secondary demand (multi-scale) l Trace driven simulations –Model validation –Insights into more realistic scenarios l Goal:devise framework to understand perf. of aggregation

Edward W. Knightly Basic System Model l Aggregation system: –Ingress admits flow if sufficient bulk reservation  If new flow rate plus current demand < current agg. reservation – Adjust bulk reservation level every  seconds  Assume perfect prediction for next  seconds  Well-defined control time scale l Assume bottleneck link C and N aggr.’s l Intserv admits flow if

Edward W. Knightly Simple Model of Aggregate Demand l Primary demand –Sinusoid with period T, amplitude a, and random phase l Secondary demand –White noise –Uniform distribution U[-b,b] += l Demand time scale T l Demand variance also due to white noise a b T

Edward W. Knightly Control and Demand Time Scales IntServStatic Aggr. ResvDynamic Aggr. Resv Recall: control time scale  ; demand time scale T Intserv (  =0) and static aggregate reservation (  =T) upper and lower bound performance l Note: reserved resource utilization

Edward W. Knightly Example Analytical Result l Overload probability - ratio of overloaded traffic (not admitted) to the total demand l Derived as: where

Edward W. Knightly Overload and Control Time Scale l Performance continuum between Intserv and static reservation l If 0.01T, aggregation is near ideal l Given limit of signaling system, can determine achievable performance l Theoretical model tracks simulation results T

Edward W. Knightly Reserved Resource Utilization l RRU = fraction of reserved capacity utilized l Intserv is 1 l Faster signaling better tracks demand, with 0.01T near perfect T

Edward W. Knightly Variance of the Secondary Demand l Demand variance degrades performance for –Ex. For perf. within 20% of Intserv’s, need var <.05, or secondary demand range <.39 times primary range var=0 var=0.01 var=0.33 +

Edward W. Knightly Outline l Simple traffic and theoretical model to study aggregation l Validation and basic conclusions on timescales and variance l Remove assumptions of the basic model via simulations –Other primary demand functions –Correlation in secondary demand (multi-scale) l Trace driven simulations –Model validation –Insights into more realistic scenarios l Goal:devise framework to understand perf. of aggregation

Edward W. Knightly Alternate Primary Demand Models l Different periodic functions with identical mean, variance, and period have little impact

Edward W. Knightly Alternate Secondary Demand Models l Small impact, especially for smaller T 2, smaller b Uncorrelated Correlated T 2 =T/4 +

Edward W. Knightly Trace-Driven Simulation Sources l Qbone trace (m 56.8 Mb/sec, var 191, T 24 hours, s 5 min) l NLANR trace (m 0.74 Mb/sec, var 0.45, T 24 hours, s 1 sec) l Caveat: all traffic vs. real-time flows

Edward W. Knightly QBone Simulation and Model Predictions System l Variance is moderate b/a=0.42 If =T/72, aggregate resv. achieves utilization of 97% of IntServ’s Model l Theoretical model retains predictive capability l Primary + secondary outperforms primary only

Edward W. Knightly NLANR Simulation and Model Predictions System l High variance in secondary demand hinders performance (b/a=1.9) If =0.01T, agg. achieves utilization of 44.2% of IntServ’s Model l Secondary demand critical for model l Captures basic trend with larger prediction errors

Edward W. Knightly Impact of Number of Aggregate Demands l Each aggregate introduces quantization error Effect is cumulative and most visible for large  l Could reverse trend via inter-aggregate statistical multiplexing or “merging”

Edward W. Knightly Impact of Merging l Merge multiple aggregates into 1 vs. each independent l Significant performance improvements, especially when l Gains from statistical smoothing of multiplexed flow

Edward W. Knightly Impact of Demand Phase l What if all aggregates are synchronized? l Performance degrades aggregation and IntServ l A capacity planning issue

Edward W. Knightly Summary of Factors Affecting Aggregation l Major factors – Demand time scale T, control time scale , variance   –  <.01 T, and moderate variance is ideal –Simple analytical model captures these effects l Minor factors –Correlation structure of primary demand –Existence of correlation (vs. white noise) in secondary demand –Network topology (multiple bottlenecks) l Other Factors –# of aggregates (-), merging (+), phase (- to all)

Edward W. Knightly Conclusions l Proposed a simple model for aggregate traffic l Derived closed-form expressions for the system’s key performance metrics l Provide a methodology to determine the regime under which aggregation is an accurate and high-performance mechanism

Edward W. Knightly Demand Model l Demand and Aggregation Model for Aggregate Demand, Request and Reservation

Edward W. Knightly Demand Time Scale l To achieve performance within 10% IntServ, hours, for minutes

Edward W. Knightly NLANR (5 Minutes Average) Simulation and Model Predictions l Mean same, variance >0.32 l Since b/a decreases 1.9-->0.68, for 0.01T, aggregation performs better

Edward W. Knightly ”Sketch” Derivation of Overload Probability l Consider aggr. resv. requests occur at identical epochs l Decouple the impact of primary and secondary demands –Primary demand: odd symmetric –Secondary demand: ADDITIONAL bandwidth must be reserved since Conditioning on the relative phases of different aggregates

Edward W. Knightly Impact of Network Topology l Little impact –Large T incurs slight deviation according to the number of contention points