Algorithms and Complexity of Distributed Computation

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Distributed computing systems ranging from simple desktop PCs or even cell phones with multi-core CPUs to high-performance computer clusters and systems "in-the-cloud" have to deal with problems that arise from concurrency, asynchrony, and failures. In these settings, the design of time and space efficient algorithms, proof of their correctness, and analysis of their complexity are challenging tasks. Algorithms and data structures for fundamental tasks in distributed systems are critical for the future development of efficient and fault-tolerant software. Lower bounds and impossibility results can guide researchers and software developers in their efforts to build efficient software systems, and can help hardware designers to decide what hardware primitives are needed. In my research program I will devise new efficient algorithms and data structures for shared memory systems. I will try to find solutions for fundamental problems such as mutual exclusion, consensus, or the efficient and randomized implementation of strong primitives (e.g., compare-and-swap) from weaker ones. I will complement new algorithms with proofs of lower bounds in order to determine the time and space complexity of these problems. I will also investigate new methods for measuring the quality of randomized and deterministic shared memory algorithms and the complexity of problems. The classical worst-case analysis is often too pessimistic to predict the actual performance of programs in practice, and sometimes leads to complicated algorithms. Therefore, novel ways of theoretically predicting the performance of algorithms need to be developed and evaluated. Results of this research program will yield a better understanding of the system requirements of synchronization problems. Algorithms resulting from the research can be used as building blocks in software development, and lower bounds can help software and hardware developers making design decisions. Research on novel analytical performance measures for shared memory algorithms will directly and indirectly yield new solutions to synchronization problems, and improve the performance of systems relying on synchronization algorithms.