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Even well-designed software systems suffer from chronic performancedegradation, also named "software aging", due to internal (e.g. software bugs)and external (e.g. resource exhaustion) impairments. These chronic problemsoften fly under the radar of software monitoring systems before causing severeimpacts (e.g. system failure). Therefore it's a challenging issue how to timelydetect these problems to prevent system crash. Although a large quantity ofapproaches have been proposed to solve this issue, the accuracy andeffectiveness of these approaches are still far from satisfactory due to theinsufficiency of aging indicators adopted by them. In this paper, we present anovel entropy-based aging indicator, Multidimensional Multi-scale Entropy(MMSE). MMSE employs the complexity embedded in runtime performance metrics toindicate software aging and leverages multi-scale and multi-dimensionintegration to tolerate system fluctuations. Via theoretical proof andexperimental evaluation, we demonstrate that MMSE satisfies Stability,Monotonicity and Integration which we conjecture that an ideal aging indicatorshould have. Based upon MMSE, we develop three failure detection approachesencapsulated in a proof-of-concept named CHAOS. The experimental evaluations ina Video on Demand (VoD) system and in a real-world production system,AntVision, show that CHAOS can detect the failure-prone state in anextraordinarily high accuracy and a near 0 Ahead-Time-To-Failure (ATTF).Compared to previous approaches, CHAOS improves the detection accuracy by about5 times and reduces the ATTF even by 3 orders of magnitude. In addition, CHAOSis light-weight enough to satisfy the realtime requirement.

CHAOS: Accurate and Realtime Detection of Aging-Oriented Failure Using Entropy