Abstract：As an increasing number of online services have migrated into the cloud, anomaly detection has now become an important problem. Existing efforts detect anomalies by mining real-time workloads; however, the accuracy of such approaches cannot be assured in case of user spikes and application errors. This paper presents a wavelet-based online anomaly detection approach that uses discrete wavelet transforms to decompose real-time workload traces into multiple curves with different frequencies and then applies statistical analysis to the decomposed traces to detect the workload anomalies. Tests show that this approach is more accurate with a lower false-alarm rate than existing approaches.
Armbrust M, Fox A, Griffith R, et al. A view of cloud computing [J]. Communications of the ACM, 2010, 53(4): 50-58.
Jadeja Y, Modi K. Cloud computing-concepts, architecture and challenges [C]//Proc International Conference on Computing, Electronics and Electrical Technologies (ICCEET). Piscataway, NJ, USA: IEEE Press, 2012: 877-880.
Kandias M, Virvilis N, Gritzalis D. The insider threat in cloud computing [C]//Proc 6th International Workshop on Critical Information Infrastructure Security. Berlin, Germany: Springer-Verlag, 2013: 93-103.
Tan Y, Nguyen H, Shen Z, et al. PREPARE: Predictive performance anomaly prevention for virtualized cloud systems [C]//Proc 32nd International Conference on Distributed Computing Systems (ICDCS). Piscataway, NJ, USA: IEEE Press, 2012: 285-294.
WANG Chengwei, Viswanathan K, Choudur L, et al. Statistical techniques for online anomaly detection in data centers [C]//Proc 2011 IFIP/IEEE International Symposium on Integrated Network Management (IM). Piscataway, NJ, USA: IEEE Press, 2011: 385-392.
XIE Yi, YU Shunzheng. A large-scale hidden semi-Markov model for anomaly detection on user browsing behaviors [J]. IEEE/ACM Transactions on Networking, 2009, 17(1): 54-65.
WANG Tao, ZHANG Wenbo, WEI Jun. Workload-aware online anomaly detection in enterprise applications with local outlier factor [C]//Proc 36th Annual Conference on Computer Software and Applications (COMPSAC). Piscataway, NJ, USA: IEEE Press, 2012: 25-34.
GUAN Qiang, FU Song. Adaptive anomaly identification by exploring metric subspace in cloud computing infrastructures [C]//Proc 32nd International Symposium on Reliable Distributed Systems (SRDS). Piscataway, NJ, USA: IEEE Press, 2013: 205-214.
WANG Chengwei, Talwar V, Schwan K, et al. Online detection of utility cloud anomalies using metric distributions [C]//Proc Network Operations and Management Symposium (NOMS). Piscataway, NJ, USA: IEEE Press, 2010: 96-103.
Vasic N, Novakovicet D, Miucin S, et al. DejaVu: Accelerating resource allocation in virtualized environments [J]. ACM SIGARCH Computer Architecture News, 2012, 40(1): 423-436.
Reis A, Rocha J, Alexandre P. Feature extraction via multiresolution analysis for short-term load forecasting [J]. IEEE Transactions on Power Systems, 2005, 20(1): 189-198.
Pannu H, LIU Jianguo, FU Song. AAD: Adaptive anomaly detection system for cloud computing infrastructures [C]//Proc 31st Symposium on Reliable Distributed Systems (SRDS). Piscataway, NJ, USA: IEEE Press, 2012: 396-397.
Mallat S. A theory for multiresolution signal decomposition: The wavelet representation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(7): 674-693.