Over the last years, several gossip-based aggregation algorithms have been developed which focus on providing resilience in failure-prone distributed systems. The main objective of such algorithms is the efficient in-network computation of aggregates even in the case when system failures occur during runtime. In this paper, we evaluate performance and limitations in practical computations of those gossip-based aggregation algorithms with the most promising theoretical fault tolerance properties. Theoretical analyses of these algorithms usually address only the principal ability of handling or overcoming a certain kind of system failure. Most of the time, there are no formal results on the concrete impact of failure handling on the performance of the algorithms, e. g., in terms of convergence speed. This leaves a wide gap between theory and practice, as we illustrate in this paper. In order to bridge this gap, we first categorize common system failures of interest. Then, we experimentally investigate how well these common failure types are handled in practice by the considered algorithms and up to which extent these state-of-the-art methods provide a reasonable degree of fault tolerance in practice. Our experimental studies reveal (i) that certain failure handling approaches which work in theory exhibit unacceptable performance in practice and (ii) that in some cases the failure handling mechanisms used introduce new problems, e.g., numerical inaccuracy. Our investigations illustrate that for some failure types (such as permanent node failures) further algorithmic advances are required to achieve resilience with a reasonably small overhead and acceptable performance.

Solving Eigen's quasispecies model for the evolution of virus populations involves the computation of the dominant eigenvector of a matrix whose size N grows exponentially with the chain length of the virus to be modeled. Most biologically interesting chain lengths are so far well beyond the reach of existing algorithms and hardware.