An Artificial Immune Ecosystem Model for Hybrid Cloud Supervision
Résumé
In this paper, we propose a new approach to the performance supervision of complex and heterogeneous infrastructures found in hybrid cloud networks, which typically consist of hundreds or thousands of interconnected servers and networking devices. This hardware and the quality of the interconnections is monitored by sampling specific metrics (such as bandwidth usage, CPU time, packet loss...) using probes, and raising alarms in case of an anomaly. We study an Artificial Immune Ecosystem model derived from the Artificial Immune Systems (AIS) algorithms to perform distributed analysis of the data collected throughout the network by these probes. In particular, we use the low variability of the measured data to derive statistical approaches to outlier detection, instead of the traditional stochastic antibody generation and selection method. The failure modes and baseline behaviour of the metrics being monitored (such as bandwidth usage, CPU time, packet loss...) are recorded in a distributed learning process and increase the system's ability to react quickly to suspiscious events. By matching the data with only a small number of failure signatures, we reduce the overall computations required to operate the system with respect to traditional AIS, therefore allowing its deployment on low-end monitoring servers or virtual machines. We demonstrate that a very small computational overhead allows the supervision engine to react much faster than the monitoring solutions currently in use.
Domaines
Informatique [cs]
Origine : Fichiers produits par l'(les) auteur(s)
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