BEGIN:VCALENDAR
VERSION:2.0
PRODID:icalendar-ruby
CALSCALE:GREGORIAN
X-WR-CALNAME:Data-Centric Networking: Theory\, Algorithms and Applications
X-WR-TIMEZONE:Eastern Time (US & Canada)
BEGIN:VEVENT
DTSTAMP:20260515T075835Z
UID:tag:localist.com\,2008:EventInstance_39763362640697
DTSTART:20220429T190000Z
DTEND:20220430T081500Z
DESCRIPTION:In the era of big data\, domain experts in various engineering 
 and science fields are facing unprecedented challenges in data access\, di
 stribution\, processing and analysis\, and in the coordinated use of limit
 ed computing\, storage and network resources. To meet this challenge\, dat
 a-centric network design approaches such as Named Data Networking (NDN) ha
 ve been proposed\, which focus on enabling end users to obtain the data th
 ey want\, rather than simply on communication between specific nodes.\n\nI
 n this talk\, we present new frameworks for the optimization of key functi
 onalities supported by data-centric networking\, which are broadly applica
 ble to content delivery networks\, peer-to-peer networks\, wireless hetero
 geneous networks\, and distributed computing networks. The frameworks enab
 le joint (in-network) caching\, request routing\, and congestion control\,
  for optimizing metrics including routing costs\, data retrieval delay\, a
 nd content-based fairness. We meet the challenge of the underlying NP-hard
  problems by exploiting submodularity\, matroid structure\, DR-submodulari
 ty\, and by leveraging tools including concave relaxation\, stochastic gra
 dient ascent\, continuous greedy and Lagrangian barrier algorithms. We dev
 elop polynomial-time approximation algorithms with proven optimality guara
 ntees\, with particular emphasis on adaptive and distributed implementatio
 ns. We further discuss the extension of these frameworks for jointly optim
 al wireless user association\, interference management and content caching
  in wireless heterogeneous networks\, and for jointly optimal computation 
 scheduling\, caching and request forwarding in distributed computing netwo
 rks.\n\nFinally\, we discuss an ongoing project which applies the optimiza
 tion frameworks and algorithms to facilitate data distribution and computa
 tion in the Large Hadron Collider (LHC) high-energy physics network\, one 
 of the largest data applications in the world.
LOCATION:Harvard John A. Paulson\, School of Engineering and Applied Scienc
 e\, SEC LL2.221
SUMMARY:Data-Centric Networking: Theory\, Algorithms and Applications
URL;VALUE=URI:https://calendar.college.harvard.edu/event/data-centric_netwo
 rking_theory_algorithms_and_applications
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260515T075835Z
UID:tag:localist.com\,2008:EventInstance_39763362658107
DTSTART:20220430T190000Z
DTEND:20220501T081500Z
DESCRIPTION:In the era of big data\, domain experts in various engineering 
 and science fields are facing unprecedented challenges in data access\, di
 stribution\, processing and analysis\, and in the coordinated use of limit
 ed computing\, storage and network resources. To meet this challenge\, dat
 a-centric network design approaches such as Named Data Networking (NDN) ha
 ve been proposed\, which focus on enabling end users to obtain the data th
 ey want\, rather than simply on communication between specific nodes.\n\nI
 n this talk\, we present new frameworks for the optimization of key functi
 onalities supported by data-centric networking\, which are broadly applica
 ble to content delivery networks\, peer-to-peer networks\, wireless hetero
 geneous networks\, and distributed computing networks. The frameworks enab
 le joint (in-network) caching\, request routing\, and congestion control\,
  for optimizing metrics including routing costs\, data retrieval delay\, a
 nd content-based fairness. We meet the challenge of the underlying NP-hard
  problems by exploiting submodularity\, matroid structure\, DR-submodulari
 ty\, and by leveraging tools including concave relaxation\, stochastic gra
 dient ascent\, continuous greedy and Lagrangian barrier algorithms. We dev
 elop polynomial-time approximation algorithms with proven optimality guara
 ntees\, with particular emphasis on adaptive and distributed implementatio
 ns. We further discuss the extension of these frameworks for jointly optim
 al wireless user association\, interference management and content caching
  in wireless heterogeneous networks\, and for jointly optimal computation 
 scheduling\, caching and request forwarding in distributed computing netwo
 rks.\n\nFinally\, we discuss an ongoing project which applies the optimiza
 tion frameworks and algorithms to facilitate data distribution and computa
 tion in the Large Hadron Collider (LHC) high-energy physics network\, one 
 of the largest data applications in the world.
LOCATION:Harvard John A. Paulson\, School of Engineering and Applied Scienc
 e\, SEC LL2.221
SUMMARY:Data-Centric Networking: Theory\, Algorithms and Applications
URL;VALUE=URI:https://calendar.college.harvard.edu/event/data-centric_netwo
 rking_theory_algorithms_and_applications
END:VEVENT
END:VCALENDAR