Flux Research Group / School of Computing

ATOM: Automated Tracking, Orchestration and Monitoring of Resource Usage in Infrastructure as a Service Systems

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Min Du and Feifei Li

2015 IEEE International Conference on Big Data (IEEE BigData) 2015.

DOI: 10.1109/BigData.2015.7363764

Networking, Security, Virtualization, Cloud


We present ATOM, an efficient and effective framework to enable automated tracking, monitoring, and orchestration of resource usage in an Infrastructure as a Service (IaaS) system. We design a novel tracking method to continuously track important performance metrics with low overhead, and develop a principal component analysis (PCA) based approach with quality guarantees to continuously monitor and automatically find anomalies based on the approximate tracking results. Lastly, when potential anomalies are identified, we use introspection tools to perform memory forensics on virtual machines (VMs) to identify malicious behavior inside a VM. We deploy ATOM in an IaaS system to monitor VM resource usage, and to detect anomalies. Various attacks are used as examples to demonstrate how ATOM is both effective and efficient to track and monitor resource usage, detect anomalies, and orchestrate system resource usage.