Predicting Breakdowns in Cloud Services (with SPIKE)
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Maintaining web-services is a mission-critical task. Any downtime of web-based services means loss of revenue. Worse, such downtimes can damage the reputation of an organization as a reliable service provider (and in the current competitive web services market, such a loss of reputation causes extensive loss of future revenue). To address this issue, we developed SPIKE, a data mining tool which can predict upcoming service breakdowns, half an hour into the future. Such predictions let an organization alert and assemble the tiger team to address the problem (e.g. by reconfiguring cloud hardware in order to reduce the likelihood of that breakdown). SPIKE utilizes (a) regression tree learning (with CART); (b) synthetic minority over-sampling (to handle how rare spikes are in our data); (c) hyperparameter optimization (to learn best settings for our local data) and (d) a technique we called "topology sampling" where training vectors are built from extensive details of an individual node plus summary details on all their neighbors. In the experiments reported here, SPIKE predicted service spikes 30 minutes into future with recalls and precision of 75 than other widely-used learning methods (neural nets, random forests, logistic regression).
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