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 predicting future resource exhaustion and
cloud service failures. Our work uses a realistic test bed in the cloud,
which is instrumented to monitor and analyze resource usage. In this
paper, we employed the optimal Kalman filtering technique to build a
predictive and analytic framework for cloud server KPIs, based on
historical data. Our k-step-ahead predictions on historical data yielded
a prediction accuracy of 95.59%. The information generated from the
framework can best be used for optimal resources provisioning, admission
control and cloud SLA management.