Transcript QoS Support in 802.11 Wireless LANs

Computer networks
Malathi Veeraraghavan
Univ. of Virginia
[email protected]
Fall 2013 (updated Jan. 2014)
• Funded projects (GRA openings)
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NSF SDCI: 2 years left
DOE HNTES: 4 years left (new grant awarded)
NSF CC-NIE (new): 3 years
NSF SCRP: 2 years left
NSF JUNO: 3 years (just starting)
• Applied orientation
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Outline
• Big picture
• Four projects
– What is the problem?
– Why solve it? (Motivation)
• Methods used
– As a GRA, what would I do?
• Processes & style
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Big picture
• Networks to support scientific
research community
– High-speed
– Low-latency
• Who is in the science community?
– DOE Office of Science
• Basic energy sciences, high-energy physics,
fusion energy sciences, bio & environ. research
– NSF Office of Cyber Infrastructure (OCI)
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Both agencies
(NSF OCI and DOE) support
• Supercomputing centers
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nersc.gov
olcf.gov
alcf.gov
XSEDE (NSF OCI)
• High-speed networks
– Backbone: ESnet, Internet2
– Campus and regional nets: DYNES
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NSF Software Dev. for
Cyber Infrastructure (SDCI)
• Problem & motivation (what & why):
1. Climate scientists run simulations that
require > 5000 cores
• Intra-datacenter network identified as
bottleneck (InfiniBand cluster: 72K cores)
• MPI communications: need to reduce latency
and variance in latency
2. Scientists move tera-to-peta byte
sized files: move these fast
• 100 Gbps: current state of the art in link
speed but not throughput (software!)
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DOE Hybrid Network Traffic
Engineering System (HNTES)
• Problem & motivation:
– Find high-rate, large-sized (alpha) flows
within a network and isolate
– Why?
• As link rates increase, spread between
fastest flow and slowest flow increases
• Fast flows can delay slow flows (user sees
poor quality for real-time flows)
• On links to providers: Service Level
Agreements (SLAs) can be violated when
fast flows appear
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NSF Campus Cyberinfrastructure – Network
Infrastructure & Engineering (CC-NIE)
• Problem & motivation
– Design protocols/apps to multicast data
reliably to hundreds of receivers
– Save network & computing resources
when compared to unicast delivery from
one sender to hundreds of receivers
• Application: Weather data
distribution
– UCAR sends real-time weather data
almost continuously to 170 institutions
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NSF Scheduled Circuit
Routing Protocol (SCRP)
• Problem & motivation
– Scientific networking community has
been building out a new type of
internetwork with circuits and virtual
circuits (airlines)
• why: service guarantees (think fedex)
– Contrast with Internet (roadways)
– Routing problem: what should one
organization’s network tell another to
enable path computation for circuits?
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NeTS: JUNO: Collaborative Research:
ACTION: Applications Coordinating
with Transport, IP, and Optical Networks
• This project is a joint collaboration
with U. Texas at Dallas, and two
universities in Japan
• The UVA portion of the project will
develop application and transport
protocols for optical networks
• Starting Feb. 1, 2014
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Outline
• Big picture
• Four projects
– What is the problem?
– Why solve it? (Motivation)
Methods used
– As a GRA, what would I do?
• Processes & style
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Methods used: Stats
• Science before engineering:
– Theodore von Karman:
• “Scientists study the world as it is; engineers
create the world that never has been”
– Data collection & statistics
• Rely on contacts at DOE labs, universities,
network operators for operational data
• Write R programs to analyze procured data
• Use fir research cluster for parallel computing
• Skills needed: stats/R language/parallel
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prog.
Methods used: run experiments
• Run existing software used by scientists to obtain
measurements
• Use national supercomputers and network
testbeds
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NCAR Wyoming SC: MPI programs (climate)
U. Utah Emulab
ESnet 100G network testbed
U. New Mexico: PROBE
ExoGENI racks: OpenFlow switches
DYNES: 10 high-performance hosts/switches across US
• Skills needed: learn/run new software programs;
write shell scripts; cron jobs; use rigorous
scientific methods in executing expts.
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Methods used: simulations
• For NSF SCRP project
– Problem requires large-scale thinking
– Cannot implement
– Cannot collect data as system does not
yet exist
– Then simulate
• Skills needed: C++ programming,
parallel programming, prob & stats,
rigorous scientific methods
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Methods used: engineering
• Come up with engineering solutions for
problems identified from scientific discovery
through analysis of operational data and
experimentally collected data
• Implement software
• Evaluate solutions on testbeds
• Two key points
– Exploratory not confirmatory (watch out for bias)
– Always quantify the negative!
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Methods: Write papers
• Conference first, then journal
• Collab Web site for grad students
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how to organize a paper
hierarchical
think of reviewers
know your community’s work
literature search (when?)
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Outline
• Big picture
• Four projects
– What is the problem?
– Why solve it? (Motivation)
• Methods used
– As a GRA, what would I do?
Processes & style
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Processes
• Goals as a graduate student
– Focus on next step
• quals
• proposal defense
• dissertation
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Want Masters en route: MCS or MS
Career goal: academics or industry
Community, community, community
Ask the process question for each step
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Advising style
• Close collaboration with GRA
– Research grants have milestones/deliverables
– Generate ideas/papers/software that others
use – who is the customer? what is the product?
• New ideas from GRA
– Develop proposals: Security for DHS; Vehicular
• Communicate – be open
• Full-time access (no substitute for hard
work) – two-way commitment
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Summary
• High-speed, low-latency networking for
– Scientific applications: scientists
– Network utilization: providers, campus, datacenter
– Bottom-up: new optical comm. technologies
• Techniques used
– Obtain operational data/experimental
measurements and analyze statistics – find the
real problem
– Develop engineering solution
– Evaluate through experiments or simulations
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