Talk Slides on Bulk-Data Metanet: Virtualization by Example

Bulk-Data Metanet: Virtualization by ExampleBulk-Data Metanet: Virtualization by Example

Sergey GorinskySergey Gorinsky

Applied Research LaboratoryApplied Research Laboratory

Department of Computer Science and EngineeringDepartment of Computer Science and Engineering

Washington University in St. LouisWashington University in St. Louis

St. Louis, MO 63130-4899, USASt. Louis, MO 63130-4899, USA

2006/11/8, NSF FIND kickoff meeting2006/11/8, NSF FIND kickoff meeting

Sergey Gorinsky (Washington University in St. Louis), “Bulk-Data Metanet: Virtualization by Example ”, 2006/11/8 2

OverviewOverview

● Bulk-Data TransfersBulk-Data Transfers

● Bulk-Data Metanet as Part of the Overall ArchitectureBulk-Data Metanet as Part of the Overall Architecture

● Inefficacies of Internet Services for Bulk DataInefficacies of Internet Services for Bulk Data

● Potential of Transfer SchedulingPotential of Transfer Scheduling

● Fruits of VirtualizationFruits of Virtualization

● Research AgendaResearch Agenda


Bulk-Data TransfersBulk-Data Transfers

● DefinitionDefinition

▪ transfer time is much longer than round-trip time (RTT)transfer time is much longer than round-trip time (RTT)- deliberately an imprecise definitiondeliberately an imprecise definition

● Performance metricPerformance metric

▪ transfer timetransfer time- and not, e.g., throughput during any smaller time intervaland not, e.g., throughput during any smaller time interval

● Sample applicationsSample applications

▪ large-scale science large-scale science - e.g., astronomical data setse.g., astronomical data sets

▪ software downloadsoftware download- e.g., operating systemse.g., operating systems


Bulk-Data Metanet as Part of the Overall ArchitectureBulk-Data Metanet as Part of the Overall Architecture

Bulk-Data Metanet

Bulk-Data Metanet

User User User

Substrate

Optical flow switching network GMPLS network Another physical network

User User User

Another end-to-end

metanet

Another end-to-end

metanet


Inefficacies of Internet Services for Bulk DataInefficacies of Internet Services for Bulk Data

● In realizing the intended ideal of fair efficient allocationIn realizing the intended ideal of fair efficient allocation

▪ TCP discrimination against flows with long RTTsTCP discrimination against flows with long RTTs

▪ slow convergence of TCP throughput to fair efficient ratesslow convergence of TCP throughput to fair efficient rates

▪ solutions proposed for the abovesolutions proposed for the above- numerous congestion control proposals of various efficiency numerous congestion control proposals of various efficiency

and degree of network supportand degree of network support

● In pursuing a wrong idealIn pursuing a wrong ideal

▪ instantaneously fair allocations do not minimize transfer timesinstantaneously fair allocations do not minimize transfer times

▪ prioritized service can improve the average transfer timeprioritized service can improve the average transfer time- e.g., Shortest Job First scheduling of CPUe.g., Shortest Job First scheduling of CPU- danger: starvation of some (long) transfersdanger: starvation of some (long) transfers


Search for an Ideal Allocation for Bulk-Data TransfersSearch for an Ideal Allocation for Bulk-Data Transfers

● Farsighted Internet congestion control Farsighted Internet congestion control

(Infocom 2005 paper by Key, Massoulie, and Vojnovic)(Infocom 2005 paper by Key, Massoulie, and Vojnovic)

▪ transmits nothing when congestion is heavytransmits nothing when congestion is heavy▪ transmits more than TCP when congestion is lighttransmits more than TCP when congestion is light▪ can starve large transfers under persistent congestioncan starve large transfers under persistent congestion▪ provides no benefits within the class of bulk-data transfersprovides no benefits within the class of bulk-data transfers

● Isolated specialized networks Isolated specialized networks

(UltraScience Net, CHEETAH, OSCARS, DRAGON)(UltraScience Net, CHEETAH, OSCARS, DRAGON)

▪ schedule transfers based on message sizes and topologyschedule transfers based on message sizes and topology▪ rely on GMPLS to establish dedicated end-to-end channelsrely on GMPLS to establish dedicated end-to-end channels▪ are technologically limited to transfers times of minutes or moreare technologically limited to transfers times of minutes or more▪ have limited reach and high costhave limited reach and high cost


Potential of Transfer Scheduling: Simulation ViewPotential of Transfer Scheduling: Simulation View

● ConstraintsConstraints

▪ no transfer finishes later than with maxmin-fair ratesno transfer finishes later than with maxmin-fair rates▪ network has a single bottlenecknetwork has a single bottleneck

● Virtual Finish Time First (ViFi) algorithmVirtual Finish Time First (ViFi) algorithm

▪ transfer messages one at a time with preemption in the order of transfer messages one at a time with preemption in the order of their maxmin-fair finish timestheir maxmin-fair finish times

● Simulation settingsSimulation settings

▪ 3000 messages on 10 Tbps path3000 messages on 10 Tbps path▪ Poisson arrivals with the average rate of 1 message per secondPoisson arrivals with the average rate of 1 message per second▪ uniformly distributed message weights from set {1, 2, 3, 4}uniformly distributed message weights from set {1, 2, 3, 4}▪ Pareto-distributed message sizes with Pareto index 1.5 and Pareto-distributed message sizes with Pareto index 1.5 and

minimum size 500 GBminimum size 500 GB


Number of Pending Messages (one experiment)Number of Pending Messages (one experiment)


Distributions of Transfer Times (10000 experiments)Distributions of Transfer Times (10000 experiments)


Fruits of VirtualizationFruits of Virtualization

● Better services and lower costs for usersBetter services and lower costs for users

▪ specialized service improving transfer timesspecialized service improving transfer times▪ lower costs than in isolated specialized networkslower costs than in isolated specialized networks

● New sources of revenue for substrate providersNew sources of revenue for substrate providers

▪ ability to sell more advanced technology (e.g., optical flow ability to sell more advanced technology (e.g., optical flow switching) to metanet providers at a higher price despite limited switching) to metanet providers at a higher price despite limited deploymentdeployment

● Lower costs and new revenues for metanet providersLower costs and new revenues for metanet providers

▪ dynamic lease and release of physical infrastructuresdynamic lease and release of physical infrastructures▪ access to various types and sets of end-to-end resources (e.g., access to various types and sets of end-to-end resources (e.g.,

both GMPLS and optical flow switching)both GMPLS and optical flow switching)▪ ability to attract new types of users (e.g., software downloaders)ability to attract new types of users (e.g., software downloaders)


Research AgendaResearch Agenda

● Algorithms for optimal transfer schedulingAlgorithms for optimal transfer scheduling▪ hard problem in general topologieshard problem in general topologies▪ efficient implementations for online operationefficient implementations for online operation

● Security and robustnessSecurity and robustness▪ human factorhuman factor▪ enforcing the resource allocation scheduleenforcing the resource allocation schedule

● Efficiency coordination and timingEfficiency coordination and timing▪ distributed activation and renegotiation of the allocation scheduledistributed activation and renegotiation of the allocation schedule▪ new algorithms for transmission control at hosts and routersnew algorithms for transmission control at hosts and routers

● Interface between the metanet and physical substrateInterface between the metanet and physical substrate▪ link capacities, processing power, reconfiguration time, buffer link capacities, processing power, reconfiguration time, buffer

space, geographical location?space, geographical location?


Additional SlidesAdditional Slides


Scheduling versus Maxmin-Fair Sharing: An ExampleScheduling versus Maxmin-Fair Sharing: An Example

0 1 11 13 16 23 3026 27

Time

29

Cap

acity

= 6

Cap

acity

= 6

Maxmin-fair

ViFi

Arrival time 0, size 72, weight 2 Arrival time 11, size 18, weight 1

Arrival time 1, size 72, weight 3 Arrival time 26, size 18, weight 4

Technology

Talk Slides on Bulk-Data Metanet: Virtualization by Example