OnlineElastMan: Self Trained Proactive Elasticity Manager for Cloud Based Storage Services
The pay as you go pricing model and the illusion of unlimited resources in the Cloud initiate the idea to provision services elastically. Elastic provisioning of services allocates/de allocates resources dynamically in response to the changes of the workload. It minimizes the service provisioning cost while maintaining the desired service level objectives (SLOs) Model predictive control is often used in building such elasticity controllers that dynamically provision resources. However, they need to be trained, either online or offline, before making accurate scaling decisions. The training process involves tedious and significant amount of work as well as some expertise, especially when the model has many dimensions and the training granularity is fine, which is proved to be essential in order to build an accurate elasticity controller. In this paper, we present OnlineElastMan, which is a self trained proactive elasticity manager for cloud based storage services. It automatically trains and evolves itself while serving the workload. Experiments using OnlineElastMan with Cassandra indicate that OnlineElastMan continuously improves its provision accuracy, i.e. minimizing provisioning cost and SLO violations, under various workload patterns.
2016 International Conference on Cloud and Autonomic Computing (ICCAC)