Computer networks 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 1 Malathi Veeraraghavan Univ. of Virginia Fall 2013 (updated Jan. 2014)

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

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) 3

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 4

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!) 5

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 6

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 7

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? 8

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,

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

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. 11

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. 12

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 13

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! 14

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?) 15

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

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 17

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 18

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 19