Journal
PROCEEDINGS 2018 IEEE 11TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING (CLOUD)
Volume -, Issue -, Pages 41-48Publisher
IEEE
DOI: 10.1109/CLOUD.2018.00013
Keywords
Cloud Computing; Workload Prediction; Predictive Resource Management; Machine Learning; Ensemble Model
Funding
- National Science Foundation [CCF-1617390]
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1618310] Funding Source: National Science Foundation
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Many predictive approaches have been proposed to overcome the limitations of reactive autoscaling on clouds. These approaches leverage workload predictors that are usually targeted for a particular workload pattern and can fail to handle real-world cloud workloads whose patterns may be unknown a priori, may dynamically change over time, or may be irregular. The result is that resources are frequently under-and overprovisioned. To address this problem, we create a novel cloud workload prediction framework called CloudInsight, leveraging the combined power of multiple workload predictors that collectively provide a council of experts. The weights of the predictors in this ensemble model are determined in real-time based on their accuracy for current workload using multi-class regression. Under real workload traces, CloudInsight has 13% - 27% better accuracy than state-of-the-art predictors. It also has low overhead for predicting future workload changes (< 100 ms) and creating a new ensemble workload predictor (< 1.1 sec.).
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