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Delivering the Grid Promise with Optical Burst Switching Chris Develder M. De Leenheer, T. Stevens, J. Baert, P. Thysebaert, F. De Turck, B. Dhoedt, P. Demeester C. Develder et al., "Delivering the Grid promise with OBS", WOPBS'06 at

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  • C. Develder et al., "Delivering the Grid promise with OBS", WOPBS'06 at COIN-NGN 2006
  • Dept. Of Information Technology – Ghent University – IBBT

Delivering the Grid Promise with Optical Burst Switching

Chris Develder

  • M. De Leenheer, T. Stevens, J. Baert,
  • P. Thysebaert, F. De Turck, B. Dhoedt,
  • P. Demeester
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  • C. Develder et al., "Delivering the Grid promise with OBS", WOPBS'06 at COIN-NGN 2006
  • Dept. Of Information Technology – Ghent University – IBBT
  • p. 2

Introduction (1)

eScience:

By 2015 it is estimated that particle

physicists will require exabytes (1018) of storage and petaflops per second of computation [1]

CERN’s LHC Computing Grid (LGC) will

start operating in 2007 and will generate 15 petabytes annually (that’s ~2Gbit/s) [2]

LHC = Large Hadron Collidor: particle accellerator 50 CDROMs = 35 GB 6 cm (~2.4 in)

Concorde (15 km or ~9.3 mi) Balloon (30 km or 18.6 mi) CD stack with 1 year LHC data (~ 20 km or 12.5 mi)

  • Mt. Blanc

(4.8 km,

  • r 3 mi)
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Introduction (2)

Consumer service:

  • Eg. video editing: 2Mpx/frame for HDTV, suppose effect

requires 10 flops/px/frame, then evaluating 10 options for 10s clip is 50 Gflops (today’s high performance PC: <5 Gflops/s) [3]

Online gaming: e.g. Final Fantasy XI: 1.500.000 gamers Virtual reality: rendering

  • f 3*108 polygons/s →

104 GFlops Multimedia editing

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Introduction (4)

Conclusion:

Grid opportunities ranging from academia over corporate

business to home users

Optical data speeds ≥ internal PC bus speeds

⇒ network speed no bottleneck

CPU data users eScience grids service grids business consumer

Figure taken from [5]

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  • C. Develder et al., "Delivering the Grid promise with OBS", WOPBS'06 at COIN-NGN 2006
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Introduction Network Architecture Routing Dimensioning Control Plane Conclusions

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Grid Network Infrastructure

GRNI GUNI GUNI GUNI GUNI GRNI GRNI GRNI GW GW GW GW GW GW GW

Grid User-Network Interface Grid Resource-Network Interface Interdomain Gateway

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Grid Network Architecture

GUNI = Grid User Network Interface

Interoperable procedures between user and Grid Submits jobs (with requirements, e.g. data/CPU, time

constraints, …)

Directly via control plane, or middleware

GRNI = Grid Resource Network Interface

Resources can dynamically enter/leave network Announces processing and/or storage resources Signaling & control interface between NE and network

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Optical Network Architecture

Optical Circuit Switching (OCS)

continuous bit-stream pre-established light-paths should be dynamic

Optical Burst/Packet Switching (OBS/OPS)

chunks of bits, in bursts/packets forwarding based on header e.g. label switching, GMPLS

Hybrids

f f c c b a d b e c f

Figures taken from [6]

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Optical Circuit Switching

Pro:

Guaranteed service quality once set-up (cf. reserved

lambda), thus fixed latency, no jitter, etc.

Fixed signaling overhead, independent of (large) job size

Con:

Signaling overhead† not acceptable for relatively small

jobs

Requires (complex) grooming if frequent set-up and tear-

downs are to be avoided (i.e. if too slow)

Less flexible, dynamic than OBS/OPS, cf. light-path set-

up and tear-down

†: [7] cites 166ms/switch → RSVP-TE speedup needed

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OBS/OPS

Pro:

Extremely flexible, dynamic Inherent statistical multiplexing of available bandwidth

(over multiple lambdas)

Con:

Packet/Burst header processing overhead

Requires job aggregation if job size too small compared to

header overhead

Difficult to deliver strict QoS guarantees without 2-way

reservation

Technology not that mature

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Hybrid OCS/OBS

Choosing between OCS and OBS depends on…

Optical technology (OBS requires faster switches, burst

mode Rx/Tx and regenerators, …)

Job sizes:

Hybrid architectures can offer a compromise

Job size Signaling time Job transmission time OBS-based OCS-based

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Hybrid OBS/OCS

Parallel: choice to either set-up OCS circuit

between source & destination, or use OBS

Note: can be overlay, where OBS makes use of OCS

connections between OBS nodes

Note: CHEETAH [15] proposes a similar approach with IP and SONET as parallel layers

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A B C D

Hybrid OBS/OCS: ORION

Overspill Routing In Optical Networks [8]:

A B C D Burst switching Circuit switching A→D B→D A→B

  • verspill

C→D

  • verspill
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Grid-OBS specifics

Differences with “classical” OBS:

Anycast routing: user generally doesn’t care where job is

executed

Burst starvation: not only network contention, also Grid

resource contention

Future reservation†: some jobs have very loose response

time requirements, others are known long beforehand

†: note that current control planes such as GMPLS do not allow this (yet)

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  • C. Develder et al., "Delivering the Grid promise with OBS", WOPBS'06 at COIN-NGN 2006
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Introduction Network Architecture Routing Dimensioning Control Plane Conclusions

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Problem Statement

Problem:

Given a job, submitted by a user to an anycast address Find a set r containing at least one (and preferably one)

suitable Grid site location accepting such jobs

Sub-problems:

Routing/deflection strategies Distributed multi-constrained routing algorithms

Users Grid Resources

?

JOB

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Routing Strategies

Soft Assignment (SA):

Select a single destination node D (random, or some

weighted function)

Other nodes along the path to D may execute job; or

alter the destination to D’ to solve contention or starvation (→ deflection)

Hard Assignment (HA):

Same selection as SA, but no modification (→ unicast)

No Assignment (NA):

No explicit destination is chosen, but burst is passed on

until a free Grid resource is found, or a pre-set slack time has expired

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Soft Assignment performs best (least blocking) No Assignment outperforms HA for bigger loads

For details and more results, see [10], available from http://www.ibcn.intec.ugent.be/css_design/research/publications/index.php

Routing Strategies: results

blocking hop count

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Anycast SAMCRA

Problem:

Incorporation of other metrics than just Grid resource

availability leads to a multiple-constraint anycast routing problem (unicast multiple-constraint is already NP-complete)

Our solution:

Introduce virtual topology to translate to unicast

Site B Site A Site C Site A+B+C

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Anycast SAMCRA

Problem:

Incorporation of other metrics than just Grid resource

availability leads to a multiple-constraint anycast routing problem (unicast multiple-constraint is already NP-complete)

Our solution:

Introduce virtual topology to translate to unicast Use a Self-Adaptive Multiple Constraint Routing

Algorithm (SAMCRA)

Use a novel path ordering avoiding sub-optimality and

loops [11]

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Anycast SAMCRA: results

Comparison with a Maximal

Maximal-

  • Flow upper bound

Flow upper bound shows that even distributed SAMCRA comes very close to (pseudo-)optimal acceptance rate

Simpler heuristics, taking only 1 measure into

account, do not come as close

For details and more results, see [11]

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Introduction Network Architecture Routing Dimensioning Control Plane Conclusions

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Case Study: excess load [9]

Network dimensioning for excess load Assuming

Jobs arrive according to a Poisson process Each Grid site is dimensioned for a steady-state load A single site at a time suffers from excess load This excess is offloaded to k other Grid sites

Find

The minimal network dimension that can cater for each of

the individual grid site overload scenarios

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Methodology

For each scenario: generate series of jobs Integer Linear Programming (ILP):

Per-job decision variable on which site to execute it Global ILP solution over all overload scenarios

Heuristic:

As ILP, but only solve individual scenarios (in parallel) Take max. network dimensions over all scenarios

Divisible Load Theory (DLT):

Real-value relaxation: workload is assumed to be

arbitrarily divisible (total load = aggregate of all jobs)

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Cost vs. average connectivity for random 13-

node networks:

Conclusion:

DLT very close to optimal ILP solution, far more scalable Heuristic scales even better, but results of less quality

Results

For details and more results, see [9], available from http://www.ibcn.intec.ug ent.be/css_design/resea rch/publications/index.p hp

cost

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  • C. Develder et al., "Delivering the Grid promise with OBS", WOPBS'06 at COIN-NGN 2006
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Introduction Network Architecture Dimensioning Routing Control Plane Conclusions

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Conclusions

Architecture:

OBS seems a very promising candidate Especially if it can be integrated with OCS in a

hybrid form

Routing

Anycast routing requires deployment of new

algorithms

Excess load dimensioning algorithm Still many research opportunities

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Challenges

Integrated OCS/OBS/hybrid control plane

Interworking, migration…

Anycast OBS vs OCS?

Performance comparison: job acceptance rate, response

times, network utilization, overhead,…

Resilience

Job migration, protection/restoration approaches…

Standardisation

E.g. GoOBS architecture, burst format, routing protocols,

inter-domain routing

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Future work

Dimensioning algorithms Hybrid OBS/OCS architectures Resilience [19]:

Fault management Protection and restoration

Control plane Security and authentication

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Phosphorus

Phosporus = new European optical Grid project,

  • fficial start date 1 Oct. 2006 (aka ‘Lucifer’ [20])

Test-beds Research Infrastructures

NOBEL EGEE-II DEISA GridCC

BB Network Layer Grid Layer

NOBEL–II GÉANT, GÉANT2, EUMEDconnect, SEEREN2 MUPBED CBDF GLIF

E n L I G H T e n e d

DRAGON CA*net 4

  • Phosporus will interact

with:

  • GÉANT2 (GN2 JRA3,

JRA1 & JRA 5)

  • International activities:

DRAGON, EnLIGHTened

  • Possible relationships

with other EU projects

  • focused on network

layer technologies: NOBEL 1 & 2, EuQoS

  • focused on Grid layer:

EGEE-II, GridCC

  • test-bed oriented:

MUPBED

PHOSPHORUS

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That’s all folks!

… any questions?

OPTICAL OPTICAL AHEAD AHEAD

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References 1-8

  • [1] G. Fox, A.J.G. Hey, F. Berman, Grid computing: making the global infrastructure a

reality, John Wiley & Sons, Mar. 2003, ISBN: 0-470-85319-0.

  • [2] LCG - LHC Computing Grid Project, http://lcg.web.cern.ch/LCG/
  • [3] M. De Leenheer, et al., A View on Enabling Consumer Oriented Grids through

Optical Burst Switching, IEEE Commun. Mag., Mar. 2006, pp. 124-131.

  • [4] D. Simeonidou, et al., Dynamic optical-network architectures and technologies for

existing and emerging grid services, J. of Lightwave Techn., Vol. 23, No. 10, Oct. 2005, pp. 3347–3357.

  • [5] J. Baert, et al., Hybrid optical switching for data-intensive media grid

applications, Proc. Workshop on Design of Next Generation Optical Networks, Ghent, Belgium, 6 Feb. 2006, pp. 9-14.

  • [6] C. Develder, et al., Node architectures foroptical packet and burst switching,
  • Tech. Digest Int. Topical Meeting on Photonics in Switching (PS2002), (invited) paper

PS.WeA1, Cheju Island, Korea, 21-25 Jul. 2002, pp. 104-106.

  • [7] M. Veeraghavan, et al., On the Use of Connection-Oriented Networks to Support

Grid Computing, IEEE Commun. Mag., Mar. 2006, pp. 118-123.

  • [8] E. Van Breusegem, et al., Overspill routing in optical networks: A true hybrid
  • ptical network design, IEEE J. Selected Areas in Commun., Apr. 2006, pp. 13-26.
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References 9-14

  • [9] P. Thysebaert, et al., Using divisible load theory to dimension optical transport

networks for grid excess load handling, Proc. Int. Conf. on Autonomic and Autonomous Systems & Int. Conf. on Netw. (ICAS/ICNS 2005), Papeete, Tahiti, 23-28

  • Oct. 2005.
  • [10] F. Farahmand, et al., A multi-layered approach to optical burst-switched based

grids, Proc. of Workshop on Optical Burst/packet Switching (WOBS2005), held on Broadnets 2005, 2nd Int. Conf. on Broadband Commun., Netw. and Sys.net, Boston, USA, 3-7 Oct. 2005, pp. 127-134.

  • [11] T. Stevens, et al., Distributed Job Scheduling based on Multiple Constraints

Anycast Routing, accepted for Broadnets 2006.

  • [12] P. Szegedi, et al., Signaling Architectures and Recovery Time Scaling for Grid

Applications in IST Project MUPBED, IEEE Commun. Mag., Mar. 2006, pp. 74-82.

  • [13] T. Lehman, et al., DRAGON: A Framework for Service Provisioning in

Heterogeneous Grid Networks, IEEE Commun. Mag., Mar. 2006, pp. 84-90.

  • [14] I.W. Habib, et al., Deployment of the GMPLS Control Plane for Grid Applications

in Experimental High-Performance Networks, IEEE Commun. Mag., Mar. 2006, pp. 65- 73.

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References 15-20

  • [15] X. Zheng, et al., CHEETAH: Circuit-switched high-speed end-to-end transport

architecture testbed, IEEE Commun. Mag., Aug. 2005

  • [16] I. Foster, et al., The Physiology of the Grid An Open Grid Services Architecture

for Distributed Systems Integration, Globus Draft, Jun. 2002, available from http://www.globus.org/ogsa/

  • [17] J. Recio, et al., Evolution of the User Controled Lightpath Provisioning System,
  • Proc. 7th Int. Conf. on Transparent Optical Networks (ICTON), Barcelona, Jul. 2005.
  • [18] –, Application Brief: Dynamic Resource Allocation Controller (DRAC), available

from www.nortel.com/solutions/optical/collateral/nn-110181-1130-04.pdf

  • [19] J.P. Vasseur, M. Pickavet, P. Demeester, Network Recovery / Protection and

Restoration of Optical, SONET-SDH, IP, and MPLS, Morgan Kaufman, Aug. 2004, ISBN: 0-12-715051-X.

  • [20] N. Ciulli, Grid services enabled photonic infrastructure in Europe, Int. Workshop
  • n the Future of Optical Networking, held at OFC 2006, Anaheim, CA, USA, Mar. 2006

Note: see http://www.ibcn.intec.ugent.be/css_design/research/publications/ for our

  • wn publications