Anomaly Detection As-a-Service

TL;DR

This paper introduces ADaaS, a cloud-based service allowing operators to declaratively specify anomaly detection parameters, improving flexibility and control in monitoring complex cloud systems.

Contribution

The paper presents ADaaS, a novel as-a-service framework enabling dynamic, declarative control of anomaly detection logic in cloud environments.

Findings

Lightweight detectors show promising results

Enhanced control over anomaly detection logic

Potential for cost-effective cloud monitoring

Abstract

Cloud systems are complex, large, and dynamic systems whose behavior must be continuously analyzed to timely detect misbehaviors and failures. Although there are solutions to flexibly monitor cloud systems, cost-effectively controlling the anomaly detection logic is still a challenge. In particular, cloud operators may need to quickly change the types of detected anomalies and the scope of anomaly detection, for instance based on observations. This kind of intervention still consists of a largely manual and inefficient ad-hoc effort. In this paper, we present Anomaly Detection as-a-Service (ADaaS), which uses the same as-a-service paradigm often exploited in cloud systems to declarative control the anomaly detection logic. Operators can use ADaaS to specify the set of indicators that must be analyzed and the types of anomalies that must be detected, without having to address any operational aspect. Early results with lightweight detectors show that the presented approach is a promising solution to deliver better control of the anomaly detection logic.