Distributed and parallel computing environments are becoming cheap and commonplace. The availability of large numbers of CPU?s makes it possible to process more data at higher speeds. Stream-processing systems are also becoming more important, as broad classes of applications require results in real-time.

Since load can vary in unpredictable ways, exploiting the abundant processor cycles requires effective dynamic load distribution techniques. Although load distribution has been extensively studied for the traditional pull-based systems, it has not yet been fully studied in the context of push-based continuous query processing.

In this paper, we present a correlation based load distribution algorithm that aims at avoiding overload and minimizing end-to-end latency by minimizing load variance and maximizing load correlation. While finding the optimal solution for such a problem is NP-hard, our greedy algorithm can find reasonable solutions in polynomial time. We present both a global algorithm for initial load distribution and a pair-wise algorithm for dynamic load migration.