Design of IP Networks with End-to. to- End Performance Guarantees

Transcription

1 Design of IP Networks with End-to to- End Performance Guarantees Irena Atov and Richard J. Harris* ( Swinburne University of Technoogy & *Massey University)

2 Presentation Outine Introduction Mutiservice IP networks (DiffServ/MPLS) Generic features for IP network design with QoS Propose a new network design methodoogy Mode assumptions (i.e., network mode and cost mode) Formuation of the mathematics programming probem Soution procedure and heuristics Computationa resuts Deveopment of a software-based network panning too Concusions Semester Advanced Teecommunications Side 2

3 Unprecedented Muti-service Capabiity d<<1sec Voice Video Tenet Interactive games d~2sec λ3 d~10sec d>>10sec From ITU-T Rec. G.1010 Error toerant Conversationa voice and video Voice/video messaging Streaming audio and video Fax Error intoerant Command/contro (eg Tenet, interactive games) Transactions (eg E-commerce, WWW browsing, Emai access) Messaging, Downoads (eg FTP, sti image) Background (eg Usenet) Interactive (deay <<1 sec) Responsive (deay ~2 sec) Timey (deay ~10 sec) Non-critica (deay >>10 sec) Semester Advanced Teecommunications Side 3

4 Motivation The paradigm for network design must change to take account of new IP QoS technoogies (DiffServ/MPLS) and must incude mutipe eves of deay-based performance guarantees Traditiona mutiservice modes foow oss-based approach (e.g., ATM network) and design of conventiona IP best-effort networks considers singe deay constraint for a traffic Our work addressed deveopment of a nove design methodoogy for mutiservice IP networks that takes account of: technoogies that can provide QoS (e.g., DiffServ/MPLS) mutipe deay constraints (i.e. deay bound and/or itter) whie maintaining reasonabe vaue of overa utiisation effectivey mode the known burstiness of IP traffic Semester Advanced Teecommunications Side 4

5 Generic Features for IP Network Design with QoS Network Mode Premium Customers Broadband Link WFQ / MPLS Standard Customers GENERIC FEATURES Various casses of service quaity based on deay/itter Differentiated routing based on cass (MPLS) Queueing modes that provide differentiated deays (WFQ) Enhanced traffic characterisation PRIMARY DECISIONS Cass-based bandwidth aocation on inks Tota ink capacities How traffic of each cass is routed through the network Semester Advanced Teecommunications Side 5

6 Q ( d Cost Mode Cost Cost k γ ) 0 0 Genera Probem Formuation d Discrete k θ Deay/itter bound Queueing cost function Capacity cost function {Q(d),Q(v)} E Non-increasing Genera integer type Set of ine types Deay I d / Capacity ν Given: Network topoogy Cost functions of inks/switches Traffic demands for each cass End-to-end QoS constraints for each cass Set of candidate routes Output: For each cass bandwidth aocations on the inks i.e., oca QoS partitions Tota ink capacities For each cass primary routes Obective: Minimise tota queueing and capacity cost Subect to: A OD pair traffic demands and QoS constraints must be satisfied Semester Advanced Teecommunications Side 6

7 Mathematica Formuation Mathematicay, the optimisation probem can be formuated as k finding the x, y, d, ν vaues that satisfy the foowing: c c c Minimise x [ + ] + c ac δ cq ( dc ) (1 δ c ) Q ( ν c ) Subect to x c c Cˆ R ( u, v) Π R R c c d ν R c c c c x c x x =1 c c E D V c c + ( 1 + ( 1 x c ) M Queueing Cost x c ) M k y k I k k k I = bc ˆ ρ θ y + (1 y ) θ c C k = 0,1}, y = {0,1}, dc > dprop, νc U Semester Advanced Teecommunications Side 7 E γ k y k End-to-end deay and itter requirements for Capacity the QoS-Cossensitive traffic casses are satisfied Ony one route is chosen for each OD pair and cass of traffic = 1 Ony one ine type is chosen for each ink { > 0 Feasibiity of fow on each ink Soution space for the decision variabes

8 Design Framework Nove approach based on partitioning of goba constraints into oca constraints Step 1: For each QoS-sensitive cass determine primary routes and ink QoS constraints Optima QoS partition and routing probem Step 2: Determine cass-based bandwidth aocations and tota ink continuous capacities Capacity assignment probem Step 3: Perform another cost optimisation to restrict capacities to discrete set of vaues Overa network cost minimisation probem Semester Advanced Teecommunications Side 8

9 Step 1: Probem OPQR-G OPQR-G determines, for each cass, a primary path between every OD pair, together with a oca partition of the goba QoS constraints WAN Aocate as much deay as possibe on the inks whie satisfying the end-to-end constraints of a traversing routes Traffic Source Traffic Destination Loca QoS Loca QoS Loca QoS Loca QoS End-to-end QoS constraint Semester Advanced Teecommunications Side 9

10 Optima QoS Partitioning and Routing OPQR Minimum cost route and deay partition that satisfies the end-toend deay requirements for a given singe OD pair. Given: Find: Min: S.t.: { } ( u, v), Q ( d ), D E r, { d } r Q( d) r r d D, d > 0 Lorenz & Orda (Infocom 2000) OPQR-G Set of primary routes between mutipe OD pairs and deay partitions on the inks so that the overa cost in the network is minimised, whie a OD pair endto-end deay requirements are satisfied. Our work 2003 (IPCCC 03 & Esevier ourna) Semester Advanced Teecommunications Side 10

11 The OPQR-G Probem Formuation Packet deay d 0 Cost 0 NP compete probem and we resort to heuristics: Greedy OPQR-G LP1 Non-increasing LP2 d = Queueing + transmission deay + propagation deay Genera integer type t d Probem OPQR-G: Given: Graph: G( V, E ) Cost/deay function: { Q ( d )} E Source-to-destination deay: { D Set of candidate routes: { R } Output: Deay partition: Set of routes: Obective: Min: Constraints: d x x = 1, D x { d } { x }, u, v + (1 x = {0,1}, u, v ) M d x 0 a u, v V Q ( d ) } E u, v V Semester Advanced Teecommunications Side 11

12 Greedy OPQR-G Step 1 - INITIALIZATION Run OPQR for each OD pair in isoation to determine: a singe near-optima set of routes for a OD pairs: r = { r} u, v V and a set of initia deay aocations on the inks: Step 2 STRETCH d = min{ d _ r } u, v V Performs re-adustment of the deay partition in the network in order to reduce the overa network cost Semester Advanced Teecommunications Side 12

13 Exampe Unicast OPQR for each OD pair wi yied a feasibe soution, however, it might be too stringent! Let: D D AE BF = 10 = 7 2 min { 2,4} = 2 Source B ~ D ~ D A AE BF = 8 = s s AE BF C = D = D AE BF ~ D ~ D Destination Semester Advanced Teecommunications Side 13 AE 4 BF 2 = 2 = 0 D 3 3 F E SLACK TIGHT

14 Heuristics Greedy OPQR-G agorithm provides pseudo-poynomia time soution. Compexity of Greedy OPQR-G is: O (( n 2 ) mu ( U + og D)) Two LP-based agorithms that use CPLEX LP 1 LP 1 improves upon STRETCH and guarantees optimaity of the deay partitioning for the set of routes pre-determined by INITIALISATION LP 2 Performs re-adustment of the routes as we as the deays Manages to sove the overa probem by empoying an iterative agorithm which executes OPQR-G subprobem (formuated as LP) at each iteration step Semester Advanced Teecommunications Side 14

15 LP-1 Agorithm LP-1 first determines the routes between OD pairs as in INITIALIZATION. Probem OPQR-G LP-1 Given: Graph: G( V, E) Cost/deay functions: { Q ( End-to-end deay bounds: Set of routes: r { r } = u, v V Find {d } E such that: Minimise: Q ( d u, v V r Subect to r d d 0 D ) E d )} { D Semester Advanced Teecommunications Side 15 E } u, v V u, v V

16 LP-2 Agorithm LP-2 empoys iterative approach in soving the quadratic probem. Probem OPQR-G LP-2 Given: Graph: G( V, E) Cost/deay functions: { Q ( d End-to-end deay bounds: Set of candidate routes: r Find {d } E and {x } u,v V such that: Minimise: n Q ( d ) Subect to E d x x D x + (1 x E Semester Advanced Teecommunications Side 16 = 1, )} E { D } u, v V = { r } u, v V = {0,1}, ) M d 0

17 Step 2: Probem CA CA determines, for each deay-sensitive cass, bandwidth aocations on the inks together with tota continuous ink capacities WAN Traffic Source Traffic Destination QoS-sensitive traffic Semester Advanced Teecommunications Side 17

18 Capacity Aocation (CA Probem) Traffic input descriptors Routing information Chaenge: Burstiness of IP traffic shoud be captured Efficient and scaabe CA agorithm Network topoogy Decomposition mode Link QoS constraints Link dimensioning mode Semester Advanced Teecommunications Side 18

19 Traffic Descriptors Anayticay tractabe: Poisson MAP MMPP IPP Renewa Sef-simiar, autoregressive-type and transformexpand-sampe traffic modes - in a Monte Caro simuation context Semester Advanced Teecommunications Side 19

20 CA Agorithm - Summary Renewa assumption We mode a cass fow as a genera (GI) arriva process: Mean packet arriva rate Traffic Decomposition QNA mode for anaysis of GI/G/1 queueing network W. Whitt SQV of packet inter-arriva time First two moments of packet size Cass aggregation + conditioning Modeing externa fows Method for characterising externa fows as GI process based on empirica data Individua IP fows SQV characterises traffic burstiness i.e., variabiity of the arriva stream Cass fow Modeing interna fows Dimensioning mode Iterative method for computation of interna fows Method for computation of required bandwidths for deay-sensitive traffic casses Our work Semester Advanced Teecommunications Side 20

21 Step 3: Probem CM Cost minimisation: to account for discrete ink capacities WAN Minimum cost pacement of best-effort traffic on the existing capacitated inks and/or to insta additiona ink capacities in the network if required Traffic Source Traffic Destination QoS-sensitive traffic Best-effort traffic θ = mod c Cˆ mod k θ, k I setup b c where is the smaest vaue of which is to the argument vaue

22 Probem CM Formuation Mathematicay, the CM probem can be formuated as finding the k x, y vaues that satisfy the foowing: Minimise: Subect to: K + y γ where K = k k setup E k I E U ˆ ρθ y + (1 y ) θ We have soved the optimisation probem defined above using the popuar LP sover too ILOG CPLEX TM 7.1 for finding an optima soution Semester Advanced Teecommunications Side 22 k k k I U = bc + x a λ c Cˆ R (, ) Π where R k I x y x k = 1 = 1 k {0,1}, y {0,1} γ Fixed setup cost for the network Feasibiity of fow on each ink Ony one route is chosen for each OD pair Ony one ine type is chosen for each ink Soution space for the decision variabes

23 Numerica Resuts Step 1: Probem OPQR-G Derived ower bound (LB) on the optima cost was used as a benchmark for performance anaysis of the heuristics Different network topoogies and sizes up to 150 nodes Different range of deay requirements Different ink cost/deay functions Resuts: In terms of optimaity LP-1 and LP-2 provide sighty better soution than Greedy OPQR-G (LP-1 and LP-2 worst case RE in terms of LB is 15%, as opposed to 35% for Greedy OPQR-G) Greedy agorithm is quite powerfu with respect to scaabiity and soution time and performs we in terms of cost LP-based agorithms do not scae we Semester Advanced Teecommunications Side 23

24 Exampe Soution Cost RE (%) for Agorithm OPQR-G with a 28-node Network OD pairs Min Max Ave 40 OD pairs Min Max Ave Sce1 (10-15)ms Sce2 (15-25)ms Sce3 (25-40)ms Sce4 (40-70)ms Sce5 (70-100)ms OD pairs Min Max Ave RE=100(heuristic-LB)/LB Semester Advanced Teecommunications Side 24

25 Numerica Resuts (Contd) Probem CM Tests were performed for various probem sizes in order to anayse the scaabiity of the LP-based agorithm devised for the soution of the CM probem networks with a size of between 15 and 30 nodes required from 10 to 70 seconds to sove networks with 50 nodes and 186 inks took ess than 5 minutes to sove at the point of infeasibiity, the run time was 85 minutes - probem instance from a 120 node network and 412 inks with a set of 20 avaiabe ine types for the inks and a set of 5 candidate routes for the OD pairs. Probem CA To assess the capabiity of the proposed CA agorithm in deivering the end-to-end deay QoS guarantees for the traffic casses we empoyed a simuation study using a queueing network simuator. Semester Advanced Teecommunications Side 25

26 Probem CA - Case Study Various traffic oads consisting of n traffic streams between OD (1,5) of type i and n between OD (2,6) of type, where i,={1,2,3,4,5} and n={10,20} No Mean SQV OD pair (1,6) (2,7) Deay 110msec 95msec Semester Advanced Teecommunications Side 26

27 Simuation Resuts - 1 End-to-end packet deay RE (%) for various traffic inputs and 0.30 < ρ < 0.65 (msec) ARE = 5.04% Target deay for OD (1,6) Simuation deay for OD (1,6) Target deay for OD (2,7) Simuation deay for OD (2,7) ARE = 9.37% 80 (1,2) (1,3) (1,4) (1,5) (2,3) (2,4) (2,5) (3,4) (3,5) (4,5) Simuation deay Target deay RE(%) = 100% Target deay Traffic inputs (i, ) Semester Advanced Teecommunications Side 27

28 Simuation Resuts - 2 (msec) End-to-end packet deay RE (%) for various traffic inputs and 0.50 < ρ < ARE = 7.22% Target deay for OD (1,6) Simuation deay for OD (1,6) Target deay for OD (2,7) Simuation deay for OD (2,7) ARE = 10.72% (1,2) (1,3) (1,4) (1,5) (2,3) (2,4) (2,5) (3,4) (3,5) (4,5) Simuation deay Target deay RE(%) = 100% Target deay Traffic inputs (i, ) Semester Advanced Teecommunications Side 28

29 The Panning Too Agorithm menu Node and Subnet Paette Drawing area Link and traffic stream paette Network specification manager Node Legend Grid View Data access tabs Engineering box Semester Advanced Teecommunications Side 29

30 Concusions Studied the issues that surround IP network design for QoS Capacity panning mode was deveoped for IP QoS networks: Guaranteed performance (deay, itter, or a combination of both) Burstiness of IP traffic is modeed to an acceptabe eve for panning purposes Primary decisions incuded: Cass-based bandwidth aocations on the inks Tota ink capacities and How traffic of each cass is to be routed through the network Resuts demonstrate capabiity of the mode in guaranteeing QoS for the casses Mode is being integrated into a network panning too Future work: Enhancing the heuristics in order to improve on the speed and scaabiity of the design mode Modifying the mode to take into account uncertainty in traffic demand parameters Semester Advanced Teecommunications Side 30

31 Numerica Resuts (Contd) Probem CM Tests were performed for various probem sizes in order to anayse the scaabiity of the LP-based agorithm devised for the soution of the CM probem networks with a size of between 15 and 30 nodes required from 10 to 70 seconds to sove networks with 50 nodes and 186 inks took ess than 5 minutes to sove at the point of infeasibiity, the run time was 85 minutes - probem instance from a 120 node network and 412 inks with a set of 20 avaiabe ine types for the inks and a set of 5 candidate routes for the OD pairs. Probem CA To assess the capabiity of the proposed CA agorithm in deivering the endto-end deay QoS guarantees for the traffic casses we empoyed a simuation study using a queueing network simuator. Semester Advanced Teecommunications Side 31