Runtime profiling of Web-based applications and services is an effective method to aid in the provisioning of required resources, for monitoring service-level objectives, and for detecting implementation defects. Unfortunately, it is difficult to obtain accurate profile data on live client workloads due to the high overhead of instrumentation. This paper describes a cloud-based profiling service for managing the tradeoffs between: (i) profiling accuracy, (ii) performance overhead, and (iii) costs incurred for cloud computing platform usage. We validate our cloud-based profiling service by applying it to an open-source e-commerce Web application. © 2011 Springer-Verlag.
CITATION STYLE
Kaviani, N., Wohlstadter, E., & Lea, R. (2011). Profiling-as-a-service: Adaptive scalable resource profiling for the cloud in the cloud. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7084 LNCS, pp. 157–171). https://doi.org/10.1007/978-3-642-25535-9_11
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