Efficient Microservice Monitoring Via Kernel Transformation and FFT Forecasting

Marianna ojannen, Maryam Sabzevari, Sandor Szedmak

Abstract:

Abstract:
Change point detection (CPD) is crucial for identifying abrupt shifts in time series data across various domains. In microservice architectures, where components operate at high velocity and scale, monitoring efficiency becomes particularly critical for maintaining system reliability while minimizing over-head. Efficient change point detection allows for timely root cause analysis, dynamic resource allocation, and targeted instance restarts, interventions that can prevent cascading failures across interconnected services. This paper presents a novel approach called MMR-FFT CPD that transforms high-dimensional mi-croservice telemetry data into a one-dimensional kernel-based representation through Maximum Margin Regression (MMR). The resulting 1D time series is then processed using Fast Fourier Transform (FFT) for efficient temporal pattern forecasting, which enables prediction of expected future values. By computing the correlation between these FFT-based predictions and the actual observed values, our method can identify deviations that indicate potential change points in microservice behavior. By tracking only these compact representations rather than raw metrics, our method dramatically reduces computational overhead and network traffic in distributed monitoring scenarios. We evaluated MMR-FFT CPD against multiple state-of-the-art algorithms. Our experiments demonstrate that with a 1D data transfer to the monitoring entity, we can maintain competitive detection accuracy, which is advantageous, particularly for high-dimensional data streams typical in modern distributed systems. The results provide valuable insights for implementing efficient, low-overhead monitoring in microservice environments with varying computational and network constraints, enabling more resnonsive and resilient microservice architectures.