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GEC17 Using ExoGENI. Ilya Baldin ibaldin@ ExoGENI Overview. Testbed. 14 GPO-funded racks built by IBM Partnership between RENCI, Duke and IBM IBM x3650 M4 servers (X-series 2U) 1x146GB 10K SAS hard drive +1x500GB secondary drive 48G RAM 1333Mhz Dual-socket 8-core CPU

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gec17 using exogeni

GEC17 Using ExoGENI

  • 14 GPO-funded racks built by IBM
    • Partnership between RENCI, Duke and IBM
    • IBM x3650 M4 servers (X-series 2U)
      • 1x146GB 10K SAS hard drive +1x500GB secondary drive
      • 48G RAM 1333Mhz
      • Dual-socket 8-core CPU
      • Dual 1Gbps adapter (management network)
      • 10G dual-port Chelseo adapter (dataplane)
    • BNT 8264 10G/40G OpenFlow switch
    • DS3512 6TB sliverable storage
      • iSCSI interface for head node image storage as well as experimenter slivering
    • Cisco(UCS-B) and Dell configuration also exist
  • Each rack is a small networked cloud
    • OpenStack-based with NEuca extensions
    • xCAT for baremetal node provisioning
  • ExoGENI is a collection of off-the shelf institutional clouds
    • With a GENI federation on top
    • xCAT – IBM product
    • OpenStack- RedHat product
  • Operators decide how much capacity to delegate to GENI and how much to retain for yourself
  • Familiar industry-standard interfaces (EC2)
  • GENI Interface
    • Mostly does what GENI experimenters expect
exogeni unique features
ExoGENI unique features
  • ExoGENI racks are separate aggregates but also act as a single aggregate
    • Transparent stitching of resources from multiple racks
  • ExoGENI is designed to bridge distributed experimentation, computational sciences and Big Data
    • Already running HPC workflows linked to OSG and national supercomputers
    • Newly introduced support for storage slivering
    • Strong performance isolation is one of key goals
exogeni experimenter tools
ExoGENI experimenter tools
  • GENI tools: Flack, GENI Portal, omni
    • Give access to common GENI capabilities
    • Also mostly compatible with
      • ExoGENI native stitching
      • ExoGENI automated resource binding
  • ExoGENI-specific tools: Flukes
    • Accepts GENI credentials
    • Access to ExoGENI-specific features
      • Elastic Cluster slices
      • Storage provisioning
      • Stitching to campus infrastructure
exogeni activities
ExoGENI activities

Presentation title goes here

  • Compute nodes
    • Up to 100 VMs in each full rack
    • A few (2) bare-metal nodes
    • BYOI (Bring Your Own Image)
  • True Layer 2 slice topologies can be created
    • Within individual racks
    • Between racks
    • With automatic and user-specified resource binding and slice topology embedding
    • Stitching across I2, ESnet, NLR, regional providers. Dynamic wherever possible
  • OpenFlow experimentation
    • Within racks
    • Between racks
    • Include OpenFlow overlays in NLR (and I2)
    • On-ramp to campus OpenFlow network (if available)
  • Experimenters are allowed and encouraged to use their own virtual appliance images
  • Since Dec 2012
    • 2500+ slices
topology embedding and stitching
Topology embedding and stitching

Workflows, services,



cloud hosts with network control

Computed embedding

Virtual colo

campus net to circuit fabric

Virtual network exchange

slice half way around the world
Slice half-way around the world
  • ExoGENI rack in Sydney, Australia
  • Multiple VLAN tags on a pinned path from Sydney to LA
  • Internet2/OSCARS ORCA-provisioned dynamic circuit
    • LA, Chicago
  • NSI statically pinned segment with multiple VLAN tags
    • Chicago, NY, Amsterdam
    • Planning to add dynamic NSI interface
  • ExoGENI rack in Amsterdam
    • ~14000 miles
    • 120ms delay
exogeni slice isolation
ExoGENI slice isolation
  • Strong isolation is the goal
  • Compute instances are KVM based and get a dedicated number of cores
  • VLANs are the basis of connectivity
    • VLANs can be best effort or bandwidth-provisioned (within and between racks)
scientific workflows
Scientific Workflows
  • Workflow Management Systems
    • Pegasus, Custom scripts, etc.
  • Lack of tools to integrate with dynamic infrastructures
    • Orchestrate the infrastructure in response to application
    • Integrate data movement with workflows for optimized performance
    • Manage application in response to infrastructure
  • Scenarios
    • Computational with varying demands
    • Data-driven with large static data-set(s)
    • Data-driven with large amount of input/output data
workflow slices
Workflow slices
  • 462,969 condor jobs since using the on-ramp to engage-submit3 (OSG)

Hardware-in-the loop slices

  • Hardware-in-the-Loop Facility Using RTDS & PMUs (FREEDM Center, NCSU)

New GENI-WAMS Testbed

Latency &

Processing Delays

Packet Loss

Network Jitters




AranyaChakrabortty, Aaron Hurz (NCSU)

YufengXin (RENCI/UNC)

thank you
Thank you!
    • ExoBlog