Continuous Distributed Counting for Non-monotonic Streams
Specifically, we consider a setting where the input values are selected and assigned to sites by an adversary but the arrival order of the input items is random. We show a randomized protocol with a sub-linear total communication with respect to the number of updates and its matching lower bound. This result stands in between the previously known results that for monotonic partial sums (all value updates either positive or negative), the expected total communication is logarithmic in the number of updates, and that for general worst-case inputs, the total required communication is linear in the number of updates. Finally, we also discuss applications of the sum tracking module for tracking the second frequency moment and for solving a Bayesian linear regression.
Zhenmin Liu is a PhD candidate in the theory of computation group at Harvard, working with Michael Mitzenmacher. His research focuses on stochastic processes, optimization, and algorithm design, as well as their applications to distributed computation and big data analytics.