Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
The Vision of Autonomic Computing
Computer
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Scheduling time-bounded dynamic software adaptation
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Elastic controllers autonomically adjust the allocation of resources in cloud computing systems. Usually such controllers assume that control actions will take immediate effect. In clouds, however, actuation times may be long, and the controllers can hardly guarantee acceptable levels of service if they neglect these actuation delays. Therefore, the ability to correctly estimate the time that control actions take effect on the systems is crucial. However, detecting actuation delays in elastic computing systems is challenging because cloud systems provide only inaccurate and incomplete data about reconfigurations timing. In this paper, we tackle the problem of estimating the delay of control actions in elastic systems. We identify recurring types of changes in the monitored metrics and requirements to properly carry out the estimation. Based on that, we develop a novel framework for the actuation delays estimation that utilizes change point detection techniques. We conduct several experiments with real-world systems to illustrate the feasibility and applicability of our framework.