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What and Who

PhD Defense Ben Wiederhake

Ben Wiederhake
Max-Planck-Institut für Informatik - D1
Promotionskolloquium
AG 1, INET, AG 5, RG1, SWS, AG 2, AG 4, D6  
AG Audience
English

Date, Time and Location

Tuesday, 14 June 2022
10:00
120 Minutes
E1 4
024
Saarbrücken

Abstract

In this talk you will learn about distributed systems, with a particular focus on graph problems and fault tolerance.


Fault-tolerance in a microprocessor or even System-on-Chip can be improved by using a fault-tolerant pulse propagation design. The existing design TRIX achieves this goal by being a distributed system consisting of very simple nodes. We show that even in the typical mode of operation without faults, TRIX performs significantly better than a regular wire or clock tree: Statistical evaluation of our simulated experiments show that we achieve a skew with standard deviation of O(log log H), where H is the height of the TRIX grid.

The distance-r generalization of classic graph problems can give us insights on how distance affects hardness of a problem. For the distance-r dominating set problem, we present both an algorithmic upper and unconditional lower bound for any graph class with certain high-girth and sparseness criteria. In particular, our algorithm achieves a O(r · f(r))-approximation in time O(r), where f is the expansion function, which correlates with density. For constant r, this implies a constant approximation factor, in constant time. We also show that no algorithm can achieve a (2r + 1 − δ)-approximation for any δ > 0 in time O(r), not even on the class of cycles of girth at least 5r. Furthermore, we extend the algorithm to related graph cover problems and even to a different execution model.

Furthermore, we investigate the problem of packet forwarding, which addresses the question of how and when best to forward packets in a distributed system. These packets are injected by an adversary. We build on the existing algorithm OED to handle more than a single destination. In particular, we show that buffers of size O(log n) are sufficient for this algorithm, in contrast to O(n) for the naive approach.

Contact

Ben Wiederhake
+49 681 9325 1129
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Ben Wiederhake, 05/09/2022 14:12 -- Created document.