In the era of big data, domain experts in various engineering and science fields are facing unprecedented challenges in data access, distribution, processing and analysis, and in the coordinated use of limited computing, storage and network resources. To meet this challenge, data-centric network design approaches such as Named Data Networking (NDN) have been proposed, which focus on enabling end users to obtain the data they want, rather than simply on communication between specific nodes.

In this talk, we present new frameworks for the optimization of key functionalities supported by data-centric networking, which are broadly applicable to content delivery networks, peer-to-peer networks, wireless heterogeneous networks, and distributed computing networks. The frameworks enable joint (in-network) caching, request routing, and congestion control, for optimizing metrics including routing costs, data retrieval delay, and content-based fairness. We meet the challenge of the underlying NP-hard problems by exploiting submodularity, matroid structure, DR-submodularity, and by leveraging tools including concave relaxation, stochastic gradient ascent, continuous greedy and Lagrangian barrier algorithms. We develop polynomial-time approximation algorithms with proven optimality guarantees, with particular emphasis on adaptive and distributed implementations. We further discuss the extension of these frameworks for jointly optimal 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 networks.

Finally, we discuss an ongoing project which applies the optimization frameworks and algorithms to facilitate data distribution and computation in the Large Hadron Collider (LHC) high-energy physics network, one of the largest data applications in the world.