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Computer networks

Computer networks. Malathi Veeraraghavan Univ. of Virginia mv5g@virginia.edu Fall 2013 (updated Jan. 2014). F unded projects (GRA openings) NSF SDCI: 2 years left DOE HNTES: 4 years left (new grant awarded) NSF CC-NIE (new): 3 years NSF SCRP: 2 years left

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Computer networks

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  1. Computer networks Malathi Veeraraghavan Univ. of Virginia mv5g@virginia.edu Fall 2013 (updated Jan. 2014) • Funded projects (GRA openings) • 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

  2. Outline • Big picture • Four projects • What is the problem? • Why solve it? (Motivation) • Methods used • As a GRA, what would I do? • Processes & style

  3. 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)

  4. Both agencies (NSF OCI and DOE) support • Supercomputing centers • nersc.gov • olcf.gov • alcf.gov • XSEDE (NSF OCI) • High-speed networks • Backbone: ESnet, Internet2 • Campus and regional nets: DYNES

  5. NSF Software Dev. for Cyber Infrastructure (SDCI) • Problem & motivation (what & why): • 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 • 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!)

  6. 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

  7. 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

  8. 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?

  9. NeTS: JUNO: Collaborative Research: ACTION: Applications Coordinatingwith 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

  10. Outline • Big picture • Four projects • What is the problem? • Why solve it? (Motivation) • Methodsused • As a GRA, what would I do? • Processes & style

  11. 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 prog.

  12. Methods used: run experiments • Run existing software used by scientists to obtain measurements • Use national supercomputers and network testbeds • 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.

  13. 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

  14. 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!

  15. Methods: Write papers • Conference first, then journal • Collab Web site for grad students • how to organize a paper • hierarchical • think of reviewers • know your community’s work • literature search (when?)

  16. Outline • Big picture • Four projects • What is the problem? • Why solve it? (Motivation) • Methods used • As a GRA, what would I do? • Processes & style

  17. Processes • Goals as a graduate student • Focus on next step • quals • proposal defense • dissertation • Want Masters en route: MCS or MS • Career goal: academics or industry • Community, community, community • Ask the process question for each step

  18. 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

  19. 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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