@article{PIETRIM201567,
title = {Adaptive, scalable and reliable monitoring of big data on clouds},
journal = {Journal of Parallel and Distributed Computing},
volume = {79-80},
pages = {67-79},
year = {2015},
note = {Special Issue on Scalable Systems for Big Data Management and Analytics},
issn = {0743-7315},
doi = {https://doi.org/10.1016/j.jpdc.2014.08.007},
url = {https://www.sciencedirect.com/science/article/pii/S074373151400149X},
author = {Mauro Andreolini and Michele Colajanni and Marcello Pietri and Stefania Tosi},
keywords = {Adaptivity, Monitoring, Cloud computing, Big data, Scalability},
abstract = {Real-time monitoring of cloud resources is crucial for a variety of tasks such as performance analysis, workload management, capacity planning and fault detection. Applications producing big data make the monitoring task very difficult at high sampling frequencies because of high computational and communication overheads in collecting, storing, and managing information. We present an adaptive algorithm for monitoring big data applications that adapts the intervals of sampling and frequency of updates to data characteristics and administrator needs. Adaptivity allows us to limit computational and communication costs and to guarantee high reliability in capturing relevant load changes. Experimental evaluations performed on a large testbed show the ability of the proposed adaptive algorithm to reduce resource utilization and communication overhead of big data monitoring without penalizing the quality of data, and demonstrate our improvements to the state of the art.}
}