rick mcgeer - tu berlinstefan/rick-mcgeer.pdf · rick mcgeer chief scientist, us ignite december 9,...
TRANSCRIPT
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Rick McGeer Chief Scientist, US IGNITE
December 9, 2013
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Distributed Clouds and
Software Defined
Networking
Complementary Technologies for
the Next-Generation Internet
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Or, A Post-Hoc
Justification for the Last
10 Years of My Life
3
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4
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5
The Future is Distributed
Clouds integrated with
Software-Defined-
Networks!
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6
SDN is a set of
abstractions over the
networking control
plane
Proxies are an
essential element of
the Internet
Architecture
Shouldn’t
there be an
abstraction
architecture
for proxies?
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Network Challenges
• Original Concept of the Network: dumb pipe between smart endpoints – Content-agnostic routing
– Rates controlled by endpoints
– Content- and user-agnostic forwarding
• Clean separation of concerns – Routing and forwarding by network elements
– Rate control, admission control, security at endpoints
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Clean separation of
concerns doesn’t work very
well
• Need application-aware stateful forwarding (e.g., multicast)
• Need QoS guarantees and network-aware endpoints – For high-QoS applications
– For lousy links
• Need in-network security and admission control – Endpoint security easily overwhelmed…
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Some Examples
• Load-balanced end-system multicast
• Adaptive/DPI-based Intrusion Detection
• In-network transcoding to multiple devices
• Web and file content distribution networks
• Link-sensitive store-and-forward connection-splitting TCP proxies
• Email proxies (e.g., MailShadow)
• In-network compression engines (Riverbed)
• Adaptive firewall
• In-situ computation for data reduction from high-bandwidth sensors (e.g., high-resolution cameras)
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Common Feature
• All of these examples require some combination of in-network and endpoint services – Information from the network
– Diversion to a proxy
– Line-rate packet filtering
• All require endpoint processing – Stateful processing
– Connection-splitting
– Filesystem access
• Three central use cases – Optimization of network resources, especially bandwidth
– Proximity to user for real-time response
– In-situ sensor processing
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Historic Solution:
Middleboxes
• Dedicated network appliances to perform specific function
• Gets the job done, but…
– Appliances proliferate (one or more per task)
– Opaque
– Interact unpredictably…
• Don’t do everything
– E.g., generalized in-situ processing engine for data reduction
• APST, 2005: “The ability to support…multiple coexisting overlays [of
proxies]…becomes the crucial universal piece of the [network] architecture.”
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OpenFlow and SDN
• L2/L3 Technology to permit software-defined control of network
forwarding and routing
• What it’s not:
– On-the-fly software decisions about routing and forwarding
– In-network connection-splitting store-and-forward
– In-network on-the-fly admission control
– In-network content distribution
– Magic….
• What it is:
– Table-driven routing and forwarding decisions (including drop and multicast)
– Callback protocol from a switch to a controller when entry not in table (“what do I
do now?”)
– Protocol which permits the controller to update the switch
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In-Network Processing
• L4/L7 Services provided by nodes in the network – TCP/Application layer proxies
– Stateful/DPI based intrusion detection
– Application-layer admission control
– Application-layer load-balancing
– ….
• Key features – Stateful processing
– Transport/Application layer information required
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Middleboxes and the
Network
• Classic View: Proxies and Middleboxes are a necessary evil that breaks the “end-to-end principle” (Network should be a dumb pipe between endpoints)
• Modern View (Peterson): “Proxies play a fundamental role in the Internet architecture: They bridge discontinuities between different regions of the Internet. To be effective, however, proxies need to coordinate and communicate with each other.”
• Generalized Modern View (this talk): Proxies and Middleboxes are special cases of a general need: endpoint processing in the network. We need to merge the Cloud and the Network.
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Going From Today to
Tomorrow
• Today: Middleboxes
• Tomorrow: In-network general-purpose processors fronted by OpenFlow switches
• Advantages of Middleboxes – Specialized processing at line rate
• Disadvantages of middleboxes – Nonexistent programming environment
– Opaque configuration
– Vendor-specific updates
– Only common functions get done
– Interact unpredictably…
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Anatomy of a Middlebox
Embedded
Linux
Processing
Engine
ASIC
L2/L3/
DPI
Packet
Filter
Packet Output
Incoming
Packets
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Generalized Architecture
L2/L3
Packet
Filter
Outgoing Packets
Software
on a
Processing
Engine
Incoming
Packets
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The Future
Controllers
+
Small
Cloud
OpenFlow
Switch
Packet Output
Incoming
Packets
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Advantages of the
Generalizing and Factoring
the Middlebox
• Transparent
• Open programming environment: Linux + OpenFlow
• Much broader range of features and functions
• Interactions between middleboxes mediated by OpenFlow rules – Verifiable
– Predictable
• Updates are software uploads
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OpenFlow + In Network
Processing
+ Line-rate processing
+ Largely implementable on COTS switches
+ Packet handling on a per-flow basis
+ Rapid rule update
+ Unified view of the network
+ L2-L7 services
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But I Need Proxies
Everywhere…
• Proxies are needed where I need endpoint processing – In-situ data reduction
– Next to users
– Where I need filtering
• Can’t always predict these in advance for every service!
• So I need a small cloud everywhere, so I can instantiate a middlebox anywhere
• Solution = Distributed “EC2” + OpenFlow network
• “Slice”: Virtual Network of Virtual Machines
• OpenFlow creates Virtual Network
• “EC2” lets me instantiate VM’s everywhere
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Shenker’s SDN Architecture
26
OpenFlow
Network "Operating System"
Physical Network
Virtual Network
Specification of a virtual
network, with explicit
forwarding instructions
Translation onto
OpenFlow rules on
physical network
Effectuation on physical
network
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Perfect for L1-L3
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Application
IP
MAC
Transport
PHYOpenFlow
Network "Operating System"
Physical Network
Virtual Network
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Key Function we want: Add
Processing Anywhere in the
Virtual Network
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OpenFlow + Cloud Managers
Distributed System "Operating System"
Physical Distributed
Cloud
Virtual Distributed
SystemApplication
IP
MAC
Transport
PHY
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Going from Virtual Network
to Virtual Distributed
System
29
OpenFlow + Cloud Managers
Distributed System "Operating System"
Physical Distributed
Cloud
Virtual Distributed
System
Specification of a virtual
distributed cloud, with
explicit forwarding
instructions BETWEEN
specified VMs
Translation onto OpenFlow
rules on physical network
AND instantiation on physical
machines at appropriate sites
Effectuation on physical
network AND physical
clouds
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Key Points
• Federated Clouds can be somewhat heterogeneous – Must support common API
– Can have some variants (switch variants still present a common interface through OpenFlow)
• DSOS is simply a mixture of three known components: – Network Operating System
– Cloud Managers (e.g., ProtoGENI, Eucalytpus, OpenStack)
– Tools to interface with Network OS and Cloud Managers (nascent tools under development)
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Implications for
OpenFlow/SDN
• Southbound API (i.e., OpenFlow): minimal and anticipated in 1.5 – “Support for L4/L7 services”, aka, seamless redirection
• Northbound API – Joint allocation of virtual machines and networks
– Location-aware allocation of virtual machines
– WAN-aware allocation of networks
– QoS controls between sites
• Build on/extend successful architectures – “Neutron for the WAN”
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Implications for Cloud
Architectures
• Key problem we’ve rarely considered: how do we easily instantiate and stitch together services at multiple sites/multiple providers?
• Multiple sites is easy, multiple providers is not
• Need easy way to instantiate from multiple providers – Common AUP/Conventions? Probably
– Common form of identity/multiple IDs? Multiple or bottom-up (e.g. Facebook)
– Common API? Absolutely
• Need to understand what’s important and what isn’t – E.g. very few web services charge for bandwidth
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Initial Attempts
• Ignite Technical Architecture/GENI Racks
• GENI Mesoscale
• SAVI
• JGN-X
• …
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With Credit To…
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GENI Mesoscale
• Nationwide network of small local clouds
• Each cloud
– 80-150 worker cores
– Several TB of disk
– OpenFlow-native local switching
• Interconnected over OpenFlow-based L2 Network
• Local “Aggregate Manager” (aka controller)
• Two main designs with common API
– InstaGENI (ProtoGENI-based)
– ExoGENI (ORCA/OpenStack-based)
• Global Allocation through federate aggregate managers
• User allocation of networks and slices through tools (GENI portal,
Flack) 35
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GENI And The Distributed
Cloud Stack
• Physical Resources
– GENI Racks, Emulab, GENI backbone
• Cloud OS
– ProtoGENI, ExoGENI…
• Orchestration Layer
– GENI Portal, Flack…
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37 ©2010 HP Created on xx/xx/xxxx of 222
Instageni rack topology
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Existing
ISP
connects
Layer 2
Ignite
Connect
(1 GE or
10GE)
Layer 3 GENI
control plane
Layer 2 connect
to subscribers
Existing head-end
New GENI / Ignite rack pair
OpenFlow switch(es)
Flowvisor
Remote management
Instrumentation
Aggregate manager
Measurement
Programmable servers
Storage
Video switch (opt)
Home
Most
equipment not
shown
U.S. Ignite City Technical Architecture
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GENI Mesoscale Deployment
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Distributed Clouds and
NSFNet: Back to the Future • GENI today is NSFNet circa 1985
• GENI and the SFA: Set of standards (e.g., TCP/IP)
• Mesoscale: Equivalent to NSF Backbone
• GENIRacks: Hardware/software instantiation of
standards that sites can deploy instantly
– Equivalent to VAX 11 running Berkeley Unix
– InstaGENI cluster running ProtoGENI and OpenFlow
• Other instantiations which are interoperable
– VNode (Aki Nakao, University of Tokyo and NICT)
– Tomato (Dennis Schwerdel, TU-Kaiserslautern)
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JGN-X (Japan)
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SAVI (Canada)
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Ofelia (EU)
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“Testbeds” vs “Clouds”
• JGN-X, GENI, SAVI, Ofelia, GLab, OneLab are all described as “Testbeds” – But they are really Clouds
– Tests require realistic services
• History of testbeds: – Academic ResearchAcademic/Research
servicesCommercial services
– Expect similar evolution here (but commercial will come faster)
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Programming Environment
for Distributed Clouds
• Problem: Allocating and configuring distributed clouds is a pain – Allocate network of VM’s
– Build VM’s and deploy images
– Deploy and run software
• But most slices are mostly the same
• Automate commonly-used actions and pre-allocate typical slices
• 5-minute rule: Build, deploy, and execute “Hello, World” in five minutes
• Decide what to build: start with sample application
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TransGeo: A Model
TransCloud Application
• Scalable, Ubiquitous Geographic Information System
• Open and Public – Anyone can contribute layers
– Anyone can host computation
• Why GIS? – Large and active community
– Characterized by large data sets (mostly satellite images)
– Much open-source easily deployable software, standard data formats
– Computation naturally partitions and is loosely-coupled
– Collaborations across geographic regions and continents
– Very pretty…
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TransGeo Architecture
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TransGeo Sites (May 2013)
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Opening up TransGEO: The
GENI Experiment Engine
• Key Idea: Genericize and make available the infrastructure behind the TransGEO demo – Open to every GENI, FIRE, JGN-X, Ofelia, SAVI…experimenter
who wants to use it
• TransGEO is a trivial application on a generic infrastructure – Perhaps 1000 lines of Python code on top of
• Key-Value Store
• Layer 2 network
• Sandboxed Python programming environment
• Messaging Service
• Deployment Service
• GIS Libraries
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GENI Experiment Engine
• Permanent, Long-Running, Distributed File System
• Permanent, Long-Running, GENI-wide Message Service
• Permanent, Long-Running, Distributed Python Environment
• Permanent, world-wide Layer-2 VLANs on high-performance
networks
• All offered in slices
• All shared by many experimenters
• Model: Google App Engine
• Advantage for GENI: Efficient use of resources
• Advantage for Experimenters: Up and running in no time
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GENI Experiment Engine Architecture
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Staged Rollout
• Permanent Layer-2 Network Spring 2014
• Shared File System based on (Swift)
Spring 2014
• Python environment Summer 2014
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Thanks and Credits
Joe Mambretti, Fei Yeh, Jim Chen Northwestern/ iCAIR
Andy Bavier, Marco Yuen, Larry Peterson,
Jude Nelson, Tony Mack
PlanetWorks/Princeton
Chris Benninger, Chris Matthews, Chris
Pearson, Andi Bergen, Paul Demchuk, Yanyan
Zhuang, Ron Desmarais, Stephen Tredger,
Yvonne Coady, Hausi Muller
University of Victoria
Heidi Dempsey, Marshall Brinn, Vic Thomas,
Niky Riga, Mark Berman, Chip Elliott
BBN/GPO
Rob Ricci, Leigh Stoller, Gary Wong University of Utah
Glenn Ricart, William Wallace, Joe Konstan US Ignite
Paul Muller, Dennis Schwerdel TU-Kaiserslautern
Amin Vahdat, Alvin AuYoung, Alex Snoeren,
Tom DeFanti
UCSD 55
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Thanks and Credits
Nick Bastin Barnstormer Softworks
Shannon Champion Matrix Integration
Jessica Blaine, Jack Brassil, Kevin Lai,
Narayan Krishnan, Dejan Milojicic, Norm
Jouppi, Patrick Scaglia, Nicki Watts, Michaela
Mezo, Bill Burns, Larry Singer, Rob Courtney,
Randy Anderson, Sujata Banerjee, Charles
Clark
HP
Aki Nakao University of Tokyo
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Conclusions
• Distributed Clouds are nothing new… – Akamai was basically the first Distributed Cloud
– Single Application, now generalizing
• But this is OK… – Web simply wrapped existing services
• Now in vogue with telcos (“Network Function Virtualization”)
• What’s new/different in GENI/JGN-X/SAVI/Ofelia…. – Support from programmable networks
– “Last frontier” for software in systems
• Open Problems – Siting VMs!
– Complex network/compute/storage optimization problems
• Needs – “http”-like standardization of APIs at IaaS, PaaS layers
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Links
http://www.youtube.com/watch?v=eXsCQdshMr4
http://pages.cs.wisc.edu/~akella/CS838/F09/838-
Papers/APST05.pdf
http://citeseerx.ist.psu.edu/viewdoc/download?d
oi=10.1.1.20.123&rep=rep1&type=pdf
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