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GEC17 Using ExoGENI. Ilya Baldin ibaldin@ renci.org. 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

IlyaBaldinibaldin@renci.org

testbed
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
      • 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
  • http://wiki.exogeni.net
exogeni
ExoGENI
  • 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

experimentation
Experimentation
  • 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,

etc.

Multi-homed

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)
slide22

Hardware-in-the loop slices

  • Hardware-in-the-Loop Facility Using RTDS & PMUs (FREEDM Center, NCSU)
slide23

New GENI-WAMS Testbed

Latency &

Processing Delays

Packet Loss

Network Jitters

Cyber-security

Man-in-middle

attacks

AranyaChakrabortty, Aaron Hurz (NCSU)

YufengXin (RENCI/UNC)

thank you
Thank you!
  • http://www.exogeni.net
    • ExoBlog http://www.exogeni.net/category/exoblog/
  • http://wiki.exogeni.net