| dc.description.abstract | The use of virtualization technology has gradually changed the way a datacenter works in recent
years. Nowadays the end-users of a datacenter do not access physical resources directly. Instead,
they access virtualized resources, such as VMs and virtual clusters, on top of a pool of physical
resources. This new computing paradigm provides the datacenter administrators a more flexible,
scalable, manageable, and economical way for resource provisioning/sharing as prior study
indicated. When a service on a VM encounters a massive amount of workload, it can scale faster
than a non-virtualized datacenter, by dynamically turning on extra virtual/physical machines to
share the workload. For example, OpenStack, an open source project for building a virtualized
cloud platform, provides a reactive approach for auto-scaling. That is, it creates new VMs to
share workload when the workload of a monitored VM exceeds a given workload threshold. The
weakness of the mechanism is that, sometimes it is too late to handle unexpected workload surges
and thus can decrease the quality of the services running on the VM. To this end, we purpose a
new hybrid auto-scaling mechanism for auto-scaling. It relies on a predictive auto-scaling
approach that predicts the upcoming workload by historical workloads. To prevent the case that
the prediction result is not accurate enough, we also use the reactive auto-scaling mechanism
provided by OpenStack, and integrate the two mechanisms as one. We have verified the
performance of our approach via experiments, and the results show that, when a massive
workload arrives, the proposed approach outperforms other approaches. In addition, the proposed
approach does not incur much overhead as the experimental results show.
Keywords: Auto-scaling, reactive auto-scaling, predictive auto-scaling, load balancing, load
sharing, virtual machine | en_US |