AF: Small: Network Algorithms Under Adversarial and Stochastic Uncertainty

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Modern information networks are composed of heterogeneous nodes and links, whose capacities and capabilities change unexpectedly due to mobility, failures, maintenance, and adversarial attacks.  User demands and critical infrastructure needs, however, require that basic primitives including access to information and services be always efficient and reliable.  This project studies the design of highly robust networked systems that are resilient to extreme failures and rapid dynamics, and provide optimal performance under a wide spectrum of scenarios with varying levels of predictability.The focus of this project will be on two problem domains, which together address adversarial network dynamics and stochastic network failures.  The first component is a comprehensive theory of information spreading in dynamic networks.  The PI will develop an algorithmic toolkit for dynamic networks, including local gossip-style protocols, network coding, random walks, and other diffusion processes.  The second component of the project concerns failure-aware network algorithms that provide high availability in the presence of unexpected and correlated failures.  The PI will study failure-aware placement of critical resources, and develop flow and cut algorithms under stochastic failures using techniques from chance-constrained optimization.  Algorithms tolerant to adversarial and stochastic uncertainty will play a critical role in large-scale heterogeneous information networks of the future. Broader impacts include student training and curriculum development.