Abstract:Cloud computing depends on the dynamic allocation and release of resources, on demand, to meet heterogeneous computing needs. This is challenging for cloud data centers, which process huge amounts of data characterised by its high volume, velocity, variety and veracity (4Vs model). Managing such a workload is increasingly difficult using state-of-the-art methods for monitoring and adaptation, which typically react to service failures after the fact. To address this, we seek to develop proactive methods for pre… Show more
“…Further to the evaluation of our model we compared its performance with other shallow learning machine algorithms. Specifically we compare our work with the approaches using the Bayesian information criterion (BIC) [17] and the Kalman filtering technique (KFT) [32]. BDT outperforms BIC in terms of average prediction accuracy.…”
Section: Comparison With the State-of-the-art Shallow Machine Learnin...mentioning
“…Further to the evaluation of our model we compared its performance with other shallow learning machine algorithms. Specifically we compare our work with the approaches using the Bayesian information criterion (BIC) [17] and the Kalman filtering technique (KFT) [32]. BDT outperforms BIC in terms of average prediction accuracy.…”
Section: Comparison With the State-of-the-art Shallow Machine Learnin...mentioning
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