Martin Suchara

Rethinking internet traffic management: from multiple decompositions to a practical protocol

In the Internet today, traffic management spans congestion control (at end hosts), routing protocols (on routers), and traffic engineering (by network operators). Historically, this division of functionality evolved organically. In this paper, we perform a top-down redesign of traffic management using recent innovations in optimization theory. First, we propose an objective function that captures the goals of end users and network operators. Using all known optimization decomposition techniques, we generate four distributed algorithms that divide traffic over multiple paths based on feedback from the network links. Combining the best features of the algorithms, we construct TRUMP: a traffic management protocol that is distributed, adaptive, robust, flexible and easy to manage. Further, TRUMP can operate based on implicit feedback about packet loss and delay. We show that using optimization decompositions as a foundation, simulations as a building block, and human intuition as a guide can be a principled approach to protocol design.

Multipath protocol for delay-sensitive traffic

Delay-sensitive Internet traffic, such as live streaming video, voice over IP, and multimedia teleconferencing, requires low end-to-end delay in order to maintain its interactive and streaming nature. In recent years, the popularity of delay-sensitive applications has been rapidly growing. This paper provides a protocol that minimizes the end-to-end delay experienced by inelastic traffic. We take a known convex optimization formulation of the problem and use an optimization decomposition to derive a simple distributed protocol that provably converges to the optimum. Through the use of multipath routing, our protocol can achieve optimal load balancing as well as increased robustness. By carrying out packet level simulations with realistic topologies, feedback delays, link capacities, and traffic loads, we show that our distributed protocol is adaptive and robust. Our results demonstrate that the protocol performs significantly better than other techniques such as shortest path routing or equal splitting among multiple paths.

Efficient algorithms for maximum likelihood decoding in the surface code

We describe two implementations of the optimal error correction algorithm known as the maximum likelihood decoder (MLD) for the 2D surface code with a noiseless syndrome extraction. First, we show how to implement MLD exactly in time

BGP safety with spurious updates

O(n^2)

Constructions and noise threshold of topological subsystem codes

, where

Securing BGP incrementally

n

Protecting the DNS from Routing Attacks: Two Alternative Anycast Implementations

is the number of code qubits. Our implementation uses a reduction from MLD to simulation of matchgate quantum circuits. This reduction however requires a special noise model with independent bit-flip and phase-flip errors. Secondly, we show how to implement MLD approximately for more general noise models using matrix product states (MPS). Our implementation has running time

Protecting DNS from Routing Attacks: A Comparison of Two Alternative Anycast Implementations

O(n\chi^3)

Greening backbone networks: reducing energy consumption by shutting off cables in bundled links

where

Network architecture for joint failure recovery and traffic engineering

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