Name
Abstract | ||
|---|---|---|
Anomaly detection in Cloud service provisioning platforms is of significant importance, as the presence of anomalies indicates a deviation from normal behaviour, and in turn places the reliability of the distributed Cloud network into question. Existing solutions lack a multi-level approach to anomaly detection in Clouds. This paper presents a wavelet-inspired anomaly detection framework for detecting anomalous behaviours across Cloud layers. It records the evolution of multiple metrics and extracts a two-dimensional spectrogram representing a monitored systemâs behaviour. Over two weeks of historical monitoring data were used to train the system to identify healthy behaviour. Anomalies are then characterised as deviations from this expected behaviour. The training technique as well as the pre-processing techniques are highly configurable. Based on a Cloud service deployment use case scenario, the effectiveness of the framework was evaluated by randomly injecting anomalies into the recorded metric data and performing comparison using the resulting spectrograms. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.5220/0005913701060117 | CLOSER |
Keywords | Field | DocType |
Anomaly Detection, Wavelet Transformation, Cloud Monitoring, Data Analysis, Cloud Computing | Data mining,Anomaly detection,Use case,Software deployment,Spectrogram,Computer science,Normal behaviour,Real-time computing,Provisioning,Wavelet,Cloud computing | Conference |
Citations | PageRank | References |
2 | 0.39 | 15 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| David O'Shea | 1 | 2 | 0.73 |
| Vincent C. Emeakaroha | 2 | 325 | 20.40 |
| John Pendlebury | 3 | 2 | 0.39 |
| Neil Cafferkey | 4 | 7 | 2.36 |
| John P. Morrison | 5 | 262 | 45.28 |
| Theo Lynn | 6 | 116 | 22.40 |