Adaptive stream resource management using Kalman Filters

TLDR

Stream management must handle continuous, high‑volume, noisy, and time‑varying data streams, and resource allocation is a key research area, yet most existing solutions rely on ad hoc, hard‑coded heuristics. The study reframes stream resource management as a filtering problem that conserves resources while meeting precision standards, focusing on minimizing communication overhead for both synthetic and real‑world streams. We adopt the Kalman Filter as an adaptive filtering solution, leveraging its ability to adjust to stream characteristics, sensor noise, and time variance, and use it to cache dynamic procedures that predict data at the server. Empirical studies demonstrate that the Kalman‑Filter approach improves performance by reducing communication overhead and effectively balancing precision and resource usage in stream queries.

Abstract

To answer user queries efficiently, a stream management system must handle continuous, high-volume, possibly noisy, and time-varying data streams. One major research area in stream management seeks to allocate resources (such as network bandwidth and memory) to query plans, either to minimize resource usage under a precision requirement, or to maximize precision of results under resource constraints. To date, many solutions have been proposed; however, most solutions are ad hoc with hard-coded heuristics to generate query plans. In contrast, we perceive stream resource management as fundamentally a filtering problem, in which the objective is to filter out as much data as possible to conserve resources, provided that the precision standards can be met. We select the Kalman Filter as a general and adaptive filtering solution for conserving resources. The Kalman Filter has the ability to adapt to various stream characteristics, sensor noise, and time variance. Furthermore, we realize a significant performance boost by switching from traditional methods of caching static data (which can soon become stale) to our method of caching dynamic procedures that can predict data reliably at the server without the clients' involvement. In this work we focus on minimization of communication overhead for both synthetic and real-world streams. Through examples and empirical studies, we demonstrate the flexibility and effectiveness of using the Kalman Filter as a solution for managing trade-offs between precision of results and resources in satisfying stream queries.