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Lowering the Barrier to Wireless and Mobile Experimentation Brian White, Jay Lepreau, Shashi...
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![Page 1: Lowering the Barrier to Wireless and Mobile Experimentation Brian White, Jay Lepreau, Shashi Guruprasad University of Utah HotNets-I October.](https://reader033.fdocuments.us/reader033/viewer/2022042702/56649d475503460f94a2299c/html5/thumbnails/1.jpg)
Lowering the Barrier to Wireless and Mobile
Experimentation
Brian White, Jay Lepreau, Shashi Guruprasad
University of Utah
www.netbed.org
HotNets-I
October 28, 2002
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Key Idea
One or more shared wireless testbeds– Would greatly enhance experimental
wireless research– Are practical– Research in wireless can have impact
Same for sensors Same for mobility
– But with added complexities and expense “W/S/M” = wireless/sensor/mobile
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The Opportunity: W/S/M are Ripe for Research Impact
New areas, lots of open problems– Constrained resources
• Power• Bandwidth
– …
Burgeoning importance– WiFi, Sensors, Military, …
Not ossified!
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Barriers to Wireless & Mobile Experimentation
Poor simulation models, lack of validation– Indoor propagation models especially– No models of new technologies
Lack of realistic mobile scenarios– Randomized, simulated
Tedious experimental setup– Wireless horrible like wired, but worse– Mobile even worse
Lack of availability and scale
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Current State of the World
Mobicom community doesn’t build systems– Almost all is simulation– Limited impact?
A few mobile testbeds proposed, not built Wireless and sensor testbeds
– Only at UCLA, USC, Intel Research, Rutgers, …
– Small to modest scale– Not shared or remotely accessible– Not automated
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W/S/M Testbed Opportunities
Emulab/Netbed automation, control, uniform and rich interface– “OS for network experimentation”
Limited scale realistic in these domains
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Netbed/Emulab Background
An instrument for experimental CS research: networks, dist systems, smart storage systems, OS’s, …
Universally available to any remote experimenter (via Web, ssh)
Space-shared and time-shared All node software replaceable by users Simple to use!
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Stats (as of June 2002, now ~10 more)
48 active projects, from 35 institutions 18 additional projects registered 276 registered users July 01 – June 02, users:
– Ran 2176 “experiments”– Allocated 17,299 nodes– Exchanged 2115 email msgs with our operations staff
About 40/30/30%dist sys/activenets/traditional networking
Resulting papers at SOSP, OSDI, Infocom, ICDCS, … 3 networking classes
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New
Modelnet
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Lesson: value of sophisticated software for efficiency
Versus manual configuration:– 3.5 hours manual vs. 3 minutes (70x)
To serve the last 12 months’ load, without time-sharing cluster would have required 1064 nodes instead of 168.
Without space-sharing, would have required 19.1 years instead of 1.
Scaling of local (emulated) expt creation: 2.2 minutes for 1 node, 6.6 minutes for 80 nodes (3.3 secs/node)
Simulated node/link scaling via nse: 90-100x
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Lesson: “an OS/VM for network experimentation”
Same software easily maps to other mechanisms!– Emulated nodes and links– Wide-area nodes and links– Virtual machines– Simulated nodes and links– ModelNet (coming)– Wireless and mobile
Next: evolve software into components
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Common Abstractions,Map to Different Mechanisms
Nodes– Machines, Accts, VMs
Addresses– IPv4, IPv6, link, port,
… Links
– VLANs, tunnels, Internet paths, special channels or HW
Topology Topology generators Queues Queuing disciplines
Traffic generators Applications Monitors: links, nodes Topology, traffic vis. Routing Events Sync, startup, replay Control channel/net
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Common Abstractions (cont’d)
“Experiment”– Config, active entities– Life cycle– Default environment– Customization:
• Per-expt• Per-node• Per-run
– Hard state– Soft state– Initial/clean state
Restart– Node, Apps, Traf gens,
events
Projects
Users– PI, TA/lieut, members– Credentials (keys)
Experiments
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Ok, enough history…
What about Wireless & Mobility?
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Our Approach: Exploit a Dense Mesh of Devices
Density enables broad range of emulation
Wireless Deploy devices throughout building or campus or
desert Employ diversity: 900 MHz, IEEE 802.11, software
radios Separate control plane, including power
Mobile Leverage passive “couriers”
• Assign PDAs to students walking to class• Equip public transit system with higher-end devices
Provides a realistic mobile testbed
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Primary Challenges
User interface– Combinatorial optimization challenge
RF interference
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Three Possible User Interfaces
Manually select from deployed devices Red (taken), Yellow (some interference),
Green (avail)
Specify desired spatial layout Netbed selects closest mapping
Specify desired device and path properties
Netbed selects closest approximation
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Virtual to Physical Mapping
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Find Best Matching Links
1. Measure NxN path characteristics(e.g. power, interference, bit error rate)
2. Users requests certain node/link characteristics
3. Use combinatorial optimization approach to find best matching set of nodes & links
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Find Best Matching Set:Scaling of Genetic Algorithm
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Problem: Interference
Inherent!– Popular technologies are of the most
interest but the most likely to be present
Three reasonable recourses:– Isolated geographic area/building
• Hanger, desert
– Negotiating for exclusive access to some channels
– Study upcoming technologies instead
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Possible Dual Role for Sensors
1. Objects of research themselves
2. Aid the RF research: Monitor the physical environment and RF
characteristics, simultaneously, for very long times. E.g, • People present?• Door open or closed?• Rain?• Temperature
Derive models for the environmental dependencies of RF characteristics
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Mobility
Passive couriers that move semi-predictably in time and space– Students to classes– Busses on roads
Robots – RC cars
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Mobile Scenarios
Reproducible?– Not perfect– “Somewhat repeatable”– The perfect is the enemy of the good!
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Provides Realistic Mobile Scenarios!
Turn apparent drawback into an asset
By definition, provides behavior representative of real world
No inaccurate simulation models
Regular, not repeatable. Study predictability of group movements: important for ad hoc networking
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Summary
Shared wireless/sensor/mobile testbeds could have large impact
Programmatic control, automated mgmt, and complete virtualization yield a– Qualitatively new environment– That opens up new possibilities
Opportunity for impact