Spark-based anomaly detection over multi-source VMware performance data in real-time
Mohiuddin Solaimani, Mohammed Iftekhar, Latifur Khan, Bhavani M. Thuraisingham, Joey Burton Ingram · 2014
Anomaly detection refers to identifying the patterns in data that deviate from expected behavior. These non-conforming patterns are often termed as outliers, malwares, anomalies or exceptions in different application domains. This paper presents a novel, generic real-time distributed anomaly detection framework for multi-source stream data. As a case study, we have decided to detect anomaly for multi-source VMware-based cloud data center. The framework monitors VMware performance stream data (e.g., CPU load, memory usage, etc.) continuously. It collects these data simultaneously from all the VMwares connected to the network. It notifies the resource manager to reschedule its resources dynamically when it identifies any abnormal behavior of its collected data. We have used Apache Spark, a distributed framework for processing performance stream data and making prediction without any delay. Spark is chosen over a traditional distributed framework (e.g., Hadoop and MapReduce, Mahout, etc.) that is not ideal for stream data processing. We have implemented a flat incremental clustering algorithm to model the benign characteristics in our distributed Spark based framework. We have compared the average processing latency of a tuple during clustering and prediction in Spark with Storm, another distributed framework for stream data processing. We experimentally find that Spark processes a tuple much quicker than Storm on average.