Signalling Analysis for Adaptive TCD Routing in ISL Networks *

Transcription

1 COST 272 Packet-Oriented Service delivery via Satellite Signalling Analysis for Adaptive TCD Routing in ISL Networks * Ales Svigelj, Mihael Mohorcic, Gorazd Kandus Jozef Stefan Institute, Ljubljana, Slovenia * This TD is a draft version of a journal paper submission planned for mid March 2004 COST 272 MCM, Toulouse, France 1

2 Traffic load oscillation suppression Traffic load oscillations are degrading routing performance and have a great impact on signalling load NOT TRAFFIC ADAPTIVE TRAFFIC ADAPTIVE NLL NLL t [min] Reduction of traffic load oscillation by: using oscillation suppression link-cost function apple Presented at the MCM in Naples t [min] using advanced forwarding policies for more homogeneous distribution of traffic load between ISLs apple Presented at the joint COST 272 & 280 Workshop in Noordwijk March 2004 COST 272 MCM, Toulouse, France 2

3 Oscillation suppression link-cost function Oscillation suppression link-cost function: takes into account the previous values of expected queuing delay in the link-cost calculation T T q exp qexp ( t ( t i 0 ) ) = = 0 [ T ( t ) T ( t )] k + T ( t ) ; k [ 0,1] exp _ av i qexp i 1 qexp i 1 the influence of the previous values regulated with k [0,1] March 2004 COST 272 MCM, Toulouse, France 3

4 Advanced forwarding policies Packet forwarding only along shortest route (SPR) Traffic load distribution with advanced forwarding policies: static forwarding policies using pre-selected rule: apple alternate link routing with deflection in source node (ALR-S) apple alternate link routing with deflection in all nodes (ALR-A) adaptive forwarding based on local information about the link load apple expected queuing delay T exp for traffic classes A and C apple Number of packets n in the outgoing queues for traffic class B apple number of hops to the destination h apple conditions for alternative path selection ( h h ) ( T ( t) T ( t) > A, C 2 1 exp1 exp2 tr Δ ) ( h h ) 1 2 ( n1( t) n2 ( t) > Δ B tr ) March 2004 COST 272 MCM, Toulouse, France 4

5 Basic assumptions inclined LEO satellite constellation with ISLs 4 permanent ISLs per satellite: 2 intraplane ISLs 2 interplane ISLs per-hop packet routing connectionless operation mode only routing in ISL segment Latitude [deg] Orbit altitude Orbit period Orbit inclination 48 Number of satellites 63 Number of orbits km 114 minutes intraplane ISLs interplane ISLs Longitude [deg] March 2004 COST 272 MCM, Toulouse, France 5

6 Traffic Class Dependent routing TCD routing procedure capable of distinguishing different traffic classes with diverse requirements Representative traffic classes traffic class A - requires delay to be minimized apple distance of ISLs - considered through the propagation delay (T p ) apple average expected queuing delay (T exp_av ) - considered through the average number of packets n in the outgoing queue traffic class B - requires throughput to be maximized apple available bandwidth on the link considered through the link utilisation during the last routing table update interval traffic class C - without any specific requirements apple same link-cost metrics considered as for traffic class A March 2004 COST 272 MCM, Toulouse, France 6

7 Distributing the acquired information Network state information distributed by signalling On-demand signalling (convenient for CO networks) Unsolicited signalling (convenient also for CL networks) apple periodically distributed in equal time intervals (amount of signalling known in advance) apple distribution triggered by changes of the network status exceeding pre-defined threshold values separately for all traffic classes (τ A, τ B, τ C ) March 2004 COST 272 MCM, Toulouse, France 7

8 ISL simulation model INPUT DATA TRAFFIC LOAD MODULE SIMULATION RESULTS Traffic models Simulation parametrs Traffic generator ROUTING MODULE CALCULATION OF ROUTING TABLES Routing table QUEUING MODULE MONITORING OF LINK STATUS AND LINK COST CALCULATION R R Packet termination ROUTING R R packet delay, throughput, hop count,... analysis of routing tables queuing delay, link cost Simulation parameters Simulation duration equal to one orbit period (114 minutes) Dijkstra shortest path algorithm for central calculation of routing tables Periodic updating of routing tables every 30 s Poisson traffic source generators Symmetric bi-directional traffic flow Total Network Traffic 8000 packets/s Mean Packet Length 680 bytes Maximum Packet Length 2000 bytes Link Capacity 4 Mbit/s Propagation delay matrix NETWORK TOPOLOGY MODULE propagation delay propagation delay ISL network propagation delay propagation delay March 2004 COST 272 MCM, Toulouse, France 8

9 Simulation scenarios Traffic flow dynamics models with homogeneous distribution of traffic sources and destinations over landmasses (LM) and: i) uniform (UNI) traffic flow pattern between all satellite pairs LM-UNI ii) hot spot (HS) traffic flow pattern LM-HS Single service routing as a reference TCD routing With periodic (T I =30s) or triggered (τ {(0.5, 0.97, 0.5); (1, 0.95, 1); (5, 0.90, 5)}) signalling With (Δ tr =(0,0,0)) or without (Δ tr =(,, )) forwarding based on local link load information With (k=0.1) or without (k=1) oscillation suppression link-cost function March 2004 COST 272 MCM, Toulouse, France 9

10 Choice of parameter k average normalised data throughput [%] k = 0.2 k = 0.1 k = 0.4k = 0.6 k = 0.8 k = 0.05 k = 0.1 k = 0.05 k = 0.2 k = 0.4 k = 0.6 k = 0.8 k = 1 k = 1 LM-UNI LM-HS k = 0 k = average relative delay deviation [%] March 2004 COST 272 MCM, Toulouse, France 10

11 Performance evaluation The network performance has been evaluated in terms of average packet delay, average data throughput, signalling load and normalised link load. relative delay deviation (RDD) normalised data throughput (NDT) normalised link load (NLL) NDT apple Link heavily loaded if NLL > 0.9 RDD March 2004 COST 272 MCM, Toulouse, France 11 = DT C 100 % ; 100 % where apple Number of heavily loaded links denoted with NoHLL link cost update (LCU) - average share of links requiring link cost update after a routing table update period (30 s); with periodic signalling all link costs are updated LCU = 100 % = DP D D Li i NLL = W C P min P min DT L = T PT T PE

12 Simulation results (I) periodic signalling average normalised data throughput [%] traffic class A traffic class B traffic class C k = 1; Δ = (,, ); LM-UNI k = 0.1; Δ = (,, ); LM-UNI k = 1; Δ = (0, 0, 0); LM-UNI k = 1; Δ = (,, ); LM-HS k = 0.1; Δ = (,, ); LM-HS k = 1; Δ = (0, 0, 0); LM-HS LM-UNI LM-HS Plain TCD routing: - no exp. smoothing link-cost function (k = 1) - no adaptive forwarding Δ = (,, ) Improved TCD routing: - exp. smoothing link-cost function (k = 0.1) - no adaptive forwarding Δ = (,, ) Improved TCD routing: 72 - no exp. smoothing link-cost 70 function (k = 1) average relative delay deviation [%] - adaptive forwarding Δ = (0, 0, 0) March 2004 COST 272 MCM, Toulouse, France 12

13 average normalised data throughput [%] Simulation results (II) triggered signalling with different threshold values τ LM-UNI LM-HS traffic class A traffic class B traffic class C τ = (0, 1, 0), LM-UNI τ = (0.5, 0.97, 0.5), LM-UNI τ = (1, 0.95, 1), LM-UNI τ = (5, 0.90, 5), LM-UNI τ = (0, 1, 0), LM-HS τ = (0.5, 0.97, 0.5), LM-HS τ = (1, 0.95, 1), LM-HS τ = (5, 0.90, 5), LM-HS Improved TCD routing: - exp. smoothing link-cost function (k = 0.1) - no adaptive forwarding Δ = (,, ) average LCU [%] LM-UNI LM-HS τ = (0.5, 0.97, 0.5) A 4 6 B C τ = (1, 0.95, 1) A 1 1 B C τ = (5, 0.90, 5) A 1 1 average relative delay deviation [%] B C March 2004 COST 272 MCM, Toulouse, France 13

14 average normalised data throughput [%] triggered signalling with different threshold values τ Simulation results (III) LM-UNI LM-HS traffic class A 84 traffic class B traffic class C 83 τ = (0, 1, 0), LM-UNI τ = (0.5, 0.97, 0.5), LM-UNI τ = (1, 0.95, 1), LM-UNI 82 τ = (5, 0.90, 5), LM-UNI τ = (0, 1, 0), LM-HS τ 81 = (0.5, 0.97, 0.5), LM-HS τ = (1, 0.95, 1), LM-HS τ = (5, 0.90, 5), LM-HS average relative delay deviation [%] Improved TCD routing: - no exp. smoothing link-cost function (k = 1) - adaptive forwarding Δ = (0, 0, 0) average LCU [%] LM-UNI LM-HS τ = (0.5, 0.97, 0.5) A 3 4 B C τ = (1, 0.95, 1) A 1 1 B C τ = (5, 0.90, 5) A 1 1 B March 2004 COST 272 MCM, Toulouse, France C

15 Simulation results (IV) Plain TCD routing: - no exp. smoothing link-cost function (k = 1) - no adaptive forwarding Δ = (,, ) - τ = (0.5, 0.97, 0.5) Improved TCD routing: - exp. smoothing link-cost function (k = 0.1) - no adaptive forwarding Δ = (,, ) - τ = (0.5, 0.97, 0.5) Improved TCD routing: - no exp. smoothing link-cost function (k = 1) - adaptive forwarding Δ = (0, 0, 0) - τ = (0.5, 0.97, 0.5) Heavily loaded links Parameters Max NLL LM-UNI NoHLL Max NLL LM-HS NoHLL k = 1; Δ tr = (,, ); τ = (0, 1, 0) k = 0.1; Δ tr = (,, ); τ = (0, 1, 0) k = 0.1; Δ tr = (,, ); τ = (0.5, 0.97, 0.5) k = 0.1; Δ tr = (,, ); τ = (1, 0.95, 1) k = 0.1; Δ tr = (,, ); τ = (5, 0.90, 5) k = 1; Δ tr = (0, 0, 0); τ = (0, 1, 0) k = 1; Δ tr = (0, 0, 0); τ = (0.5, 0.97, 0.5) k = 1; Δ tr = (0, 0, 0); τ = (1, 0.95, 1) k = 1; Δ tr = (0, 0, 0); τ = (5, 0.90, 5) March 2004 COST 272 MCM, Toulouse, France 15

16 Conclusions Effect of triggered signalling on the performance of adaptive TCD routing procedure enhanced with: oscillation suppression link-cost function, or adaptive forwarding based on local link load information. Two non-homogeneous traffic load scenarios were used to evaluate the performance of the proposed routing procedure. Triggered signalling enabled significant reduction in the number of required link cost updates with respect to periodic signalling: without notable decrease of routing performance in terms of average relative delay deviation and average normalised data throughput. with only minor increase of average normalised link load and the number of heavily loaded links March 2004 COST 272 MCM, Toulouse, France 16