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Resource Representations in GENI: A path forward

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  1. Resource Representations in GENI:A path forward Ilia Baldine, YufengXin, Renaissance Computing Institute, UNC-CH

  2. Slicing of a network

  3. Link slivering

  4. Agreements – resource representationcycle Possibly fuzzy request Collective possibly filtered manifest Ads from substrate providers Detailed manifest from substrate provider More specific request to substrate provider(s)

  5. GENI Resource representation mechanisms • ‘Traditional’ Network resources • Ethernet links, tunnels, vlans • Edge compute/storage resources • Measurement resources • Including collected measurement data objects • Wireless resources • WiFi, WiMax, motes etc • Lack of agreement on what resources represent will be a significant impediment to interoperability • Agreement on a format is important, but can be dealt with on the engineering level PG Rspec V1 PG Rspec V2 PL RSpec ORCA NDL-OWL OMF

  6. Network resources • Key distinctions • Number of layers • Describing adaptations between layers • Syntax • Tools PL NDL-OWL PG

  7. Aside: why adaptations are critical? • Network adaptations are part of the description of stitching capability • Needed for properly computing paths between aggregates connected by network providers at different layers • E.g. if a host has an interface that has an Ethernet to VLAN adaptation, this interface is capable of stitching to vlans • Consistent way to describe connectivity across layers (tunnels, DWDM, optical) • Also • Important for matching requests to substrate capabilities • E.g. creating a VM is a process of ‘adaptation’ of real hardware to virtualization layer

  8. Network resources: a practical solution • Stay primarily within Ethernet layer • Accept one format to be used between control frameworks • Perform bi-directional format conversion • Only partial may be possible • Hosted services that perform conversion on demand • E.g. NS2/RSpec v1 and v2 request converter to NDL-OWL • • Works well for several types of links, nodes, interfaces, ip addresses and simple link attributes

  9. Agreeing on wire format • RSpec v2 with extensions is a viable possibility • Conversion fromRSpec v2 is relatively straightforward pending agreement on edge resources • Conversion toRSpec v2 is likely to be partial but probably sufficient for the time being • Nothing below Layer2 is visible to experimenter

  10. Next challenge: Edge compute resources • Ads: • Aggregates can produce (adapt to) different types of nodes • E.g. PL VServer, PG bare hardware node types, ORCA’sXen/KVM virtual machines (and hardware nodes) • Constraints are possible on disk, memory size, number and type of CPU cores • Properties may include location, ownership etc. • Note: internal topology may or may not be part of the site ad. E.g. clouds have no inherent topology that needs to be advertized • Requests: • Based on constraints on type of node, disk, memory size, core type and count, location

  11. Advertising edge resources • A server can be an individual node or a cloud of servers • A site may choose to advertise individual servers/nodes or server clouds • Clouds have no inherent topology, just constraints on the type of topology they can produce and adaptations for nodes • Servers/nodes or server clouds are adaptable to different types of nodes distinguished by • Virtualization (Xen, KVM, VServer, None etc) • Possibly memory, disk sizes, core types and counts, OS

  12. Requesting edge resources • A request for a node specifies multiple constraints on that node • Type of virtualization preferred • Memory, disk size • CPU Type • Core count • OS • Allows policy to pick best sites based on request and resource availability.

  13. Semantic Shortcut examples PlanetLabCluster Produces PL Nodes ProtoGeniCluster Produces PG nodes • Semantic shortcuts • PL node • Virtualiztion: Vserver • Simple PG node • Virtualization: None • CPU type: x86 or ?? or ?? • EC2M1Small • Virtualization: KVM or XEN • CPU count: 1 • Memory size: 128M • EC2M1Large • Virtualization: KVM or XEN • CPU count: 2 • Memory size: 512M

  14. Other considerations • Emerging standards: • OVF – portable appliance images across heterogeneous environments • CF should be able to generate OVF based on combination of request data and substrate information • Higher-level programming environments: • Google App Engine, AppScale • Distributed map/reduce • …

  15. Next steps • Align edge compute resource descriptions • Enable conversion as a GENI-wide service • Test full interoperation PG<->ORCA by GEC11 • Get the conversation started on aligning abstraction models for for • Wireless resources • Max Ott, Hongwei Zhang, ? • Storage (physical and cloud) • Mike Zink, ?