A common approach for dealing with large data sets is to stream over the input in one pass, and perform computations using sublinear resources. For truly massive data sets, howeve...
Jon Feldman, S. Muthukrishnan, Anastasios Sidiropo...
With the goal to generate more scalable algorithms with higher efficiency and fewer open parameters, reinforcement learning (RL) has recently moved towards combining classical tec...
Given a set of machines and a set of Web applications with dynamically changing demands, an online application placement controller decides how many instances to run for each appl...
Chunqiang Tang, Malgorzata Steinder, Mike Spreitze...
In this paper we propose a lightweight algorithm for constructing multi-resolution data representations for sensor networks. We compute, at each sensor node u, O(log n) aggregates...
Concurrent with recent theoretical interest in the problem of metric embedding, a growing body of research in the networking community has studied the distance matrix defined by n...