Anomaly Detection As-a-Service
This addresses the problem for cloud operators needing flexible and efficient anomaly detection control, but it appears incremental as it adapts an existing as-a-service paradigm to this specific domain.
The paper tackles the challenge of cost-effectively controlling anomaly detection logic in complex cloud systems, presenting Anomaly Detection as-a-Service (ADaaS) as a declarative solution that allows operators to specify indicators and anomaly types without manual effort, with early results showing it is a promising approach.
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.