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Distributed Systems

Almost every computing system nowadays is distributed, ranging from multi-core laptops to Internet-scale services; understanding the principles of distributed computing is hence important for the design and engineering of modern computing systems.  Fundamental issues that arise in reliable and efficient distributed systems include developing adequate methods for modeling failures and synchrony assumptions, determining precise performance bounds on implementations of concurrent data structures, capturing the trade-off between consistency and efficiency, and demarcating the frontier of feasibility in distributed computing.

For example, popular Internet services and applications such as CNN.com, YouTube, Facebook, Skype, BitTorrent attract millions of users every day, and only by the effective load-balancing and collaboration of many thousand machines, an acceptable Quality-of-Service/Quality-of-Experience can be guaranteed. While distributed systems promise a good scalability as well as a high robustness, they pose challenging research problems, such as: How to design robust and scalable distributed architectures and services? How to coordinate access to a shared resource, e.g., by electing a leader? Or how to provide incentives for cooperation in an open, collaborative distributed system?

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Selected Publications

A DoS-Resilient Information System for Dynamic Data Management
Zitatschlüssel BSS-DRISDDM-09
Autor Baumgart, Matthias and Scheideler, Christian and Schmid, Stefan
Buchtitel 21st ACM Symposium on Parallelism in Algorithms and Architectures (SPAA)
Seiten 300–309
Jahr 2009
ISBN 978-1-60558-606-9
DOI http://dx.doi.org/10.1145/1583991.1584064
Ort Calgary, Alberta, Canada
Monat August
Zusammenfassung Denial of service (DoS) attacks are arguably one of the most cumbersome problems in the Internet. This paper presents a distributed information system (over a set of completely connected servers) called Chameleon which is robust to DoS attacks on the nodes as well as the operations of the system. In particular, it allows nodes to efficiently look up and insert data items at any time, despite a powerful ``past-insider adversary'' which has complete knowledge of the system up to some time point t0 and can use that knowledge in order to block a constant fraction of the nodes and inject lookup and insert requests to selected data. This is achieved with a smart randomized replication policy requiring a polylogarithmic overhead only and the interplay of a permanent and a temporary distributed hash table. All requests in Chameleon can be processed in polylog-arithmic time and work at every node.
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