Distributed adaptive multi criteria load balancing analysis and end to end simulation
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Distributed adaptive multi-criteria load balancing: analysis and end to end simulation. INFOCOM2006 - Barcelona, April 25-27 2006. S.RANDRIAMASY – ALCATEL R&I L.FOURNIE and D.HONG – N2NSOFT. Context. Goal Improve best effort traffic by absorbing local temporary traffic increase

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Distributed adaptive multi criteria load balancing analysis and end to end simulation

Distributed adaptive multi-criteria load balancing: analysis and end to end simulation

INFOCOM2006 -Barcelona, April 25-27 2006

S.RANDRIAMASY – ALCATEL R&I

L.FOURNIE and D.HONG – N2NSOFT


Context
Context and end to end simulation

  • Goal

    • Improve best effort traffic by absorbing local temporary traffic increase

    • Increase network operation performances: at the core network and end user levels

    • Decrease network cost by downsizing link dimensioning

  • Methodology

    • Load balancing triggered on “critical” links (overloaded or prohibited)

      and based on multi-path and multi-objective routing

    • On-line and distributed on IP routers, associated to OSPF-TE

    • LB-aware link capacity allocation


On line load sensitive multi path routing

R2 and end to end simulation

R4

R5

R3

R1

R6

R7

R8

On-line load-sensitive multi-path routing

  • Default routing = Shortest Path

  • Requires synchronized flooding of link-states

  • Triggering : at source I of any link (I,J) with load > ThLoad

    • Until target traffic repartition reached or load < ThLoad

    • Progressive shifting on alternative paths

    • For any destination J is a next hop to

  • Routing stays in Multi-Path mode

    • As long as load < ThLoad

  • Stopping : when load < ThLoadBack << ThLoad

    • Routing progressively reverses from multi-path to default single path

  • DMLB triggered before congestion ThLoad [80,90%]

  • Can also be triggered and stopped upon operator request


Routing with multiple criteria rmc
Routing with Multiple Criteria (RMC) and end to end simulation

  • Path cost = vectorZ=(z1, z2, z3, z4)

    • Available bandwidth (MAX-MIN)

    • Number of hops (MIN-)

    • Transit delay (MIN-)

    • Administrative cost (MIN-)

  • Step 1: extraction of all Pareto-optimal paths

  • Step 2: path rating w.r.t. distance to ideal path

    Step 3: path ranking and selection of the K best ones

    Output of RMC for each destination

    Set ok K efficient paths = input to multi-path routing


Unequal flow distribution progressive shifting

Cost(P1) 80 and end to end simulation

Packet IP header

Mapping of H

NH1

TTS1 20%

Cost(P2) 60

NH2

TTS2 27%

Cost(P1) 30

NH3

H(S, D, pS, pD, pID)

TTS3 53%

Random picking of TBM flow bins per iteration

100 flow bins

Unequal flow distribution & progressive shifting

  • Hashing on flow attributes: (IP source and dest., source and dest. port, protocol ID)

  • Flows are mapped into M “flow bins” (default = 100)

  • Repartition of bins among paths : “Target Traffic Share” reflecting their cost

  • Every 10s, TBM flow bins (5 or 10) shifted away from overloaded path

  • Random picking of deviated bins equity between flows


Multiple distributed multi path routing decisions

Incoherent routing decisions: and end to end simulation

S chooses path S-A-D, whereas A prefers path A-B-D

R2

R4

R5

R3

R1

R6

R7

R8

Multiple distributed multi-path routing decisions

  • Routers can have a different view of the network state

Several routers may compete to route the same flows differently

  • Ambiguity on R3 towards R5

  • R1 advises R4 as the next hop

  • R7 advises R8 as the next hop


Coherency of multi path routing decisions

B and end to end simulation

[71, 100]

LOOP

[71, 85]

F

[85, 100]

C

Coherency of multi-path routing decisions

  • Downstream multi-path routing (MPR) advertisement

    • A router R in DMLB mode sends decisions to the downstream routers

    • MPR decisions include: sender ID, path ID, set of concerned bins

      flow-bin centered routing decision

  • MPR decisions re-ordered/processed after arrival time-slot

    • MPR advertisements may arrive out of order w.r.t. their emission date

  • Prioritizing of multiple MPR decisions on a flow bin

    • Ranking rule that is global over the routers

      • No ambiguity

      • No loop between downstream MPR routers

  • 3D routing 2Dforwarding

    • Fwd decision depends on: D, hash value



Simulations on european geant 2001 topology
Simulations on European GEANT (2001 topology) and end to end simulation

Large scale test network: 20 core nodes, 27 links,

  • Different link capacities

  • up to 300 000 terminal nodes (ADSL line, modem, server, LAN).

    • 900 000 flows,

    • 100.000-3.000.000 parallel sessions

    • 100 to 1000 Tbits transported

    • Simulation time: [1000, 7000] secs.

  • Applications : HTTP, P2P, FTP

  • Network-level evaluation:

    • Network capacity, Packet loss, Link load balance, optimal congestion threshold/goodput,

  • User-level evaluation:

    • Goodput perapplication, TCP fairness, RTT, packet loss.


  • Packet losses
    Packet losses and end to end simulation

    Bit Error Rate: terminal noise  reference for acceptable level

    Core losses > BER

    28 Gb  43 Gb

    + 53% supported demand

    Terminal overflow

    negligible


    Lb aware link dimensioning 1 2
    LB-aware link dimensioning 1/2 and end to end simulation

    C = mean rate + reserve

    reserve: absorbs traffic variations and bursts

    When DMLB is available:

    C*ThLoad = maximum non critical link load

    Outgoing links mutually absorb their link overload

    C*ThLoad = m + reserve_MP

    with reserve_MP < reserve_SP

    MP routing: allocates capacity for outgoing links as if their traffic/demands were aggregated on one single link.


    Lb aware link dimensioning 2 2
    LB-aware link dimensioning 2/2 and end to end simulation

    • C(G+(E))= SL_outgoing_E C(L)*ThLoad = F(D(Magg, Sagg))

    • Distribution on links : e.g. proportional to D(L)/D(E)

    •  Generic model

    • Numerical example: M, S same for all demands

      • Guerin model: F(D(M, S)) = M + aS

      • a depends on overflow proba Pe = 10-7

      • Traffic mix of German network

        • M=1 Gbit/s, S =0.485 Gbits/s, a = 5.916

      • SP-based capacity outgoing E: CSP(E)= 9.926 Gbits

      • MP-based capacity outgoing E: CMP(E)*ThLoad = 8.855Gbit/s

      • Gain = 10.8%

      • If 3 demands are shared on 3 links: CSP(E) = 11.8 Gbit/s, Gain = 25 %


    Conclusion
    Conclusion and end to end simulation

    • Load sensitive Distributed Multi-Criteria Load Balancing

      • Used « on top » of default standard single/shortest path routing

    • Studied for pure IP - MPLS implementation easier

    • Allows to better

      • Balance traffic over the network

      • Prevent link congestion

      • Route more traffic

      • Allocate less link capacity

    • Extensions

      • Generalized « LB-aware » link dimensioning

      • Mobile core networks

      • Contribution in network resiliency after link breakdown


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