Seminar: Self-adaptive Cloud - Predictive Autoscaling
26 Feb 2019 11:00 AM - 12:00 PM
Presenter/Speaker: Vladimir Podolskiy, PhD student, Technical University of Munich, Germany
Location: G.1.15
Self-adaptive systems are all around us. Moreover, we, as human beings, are the most advanced self-adaptive systems ever. What does it mean to be self-adaptive? Essentially, self-adaptive systems have notion of environment that they constantly observe as well as raison d'etre, i.e. why do they exist, what are their goals. Self-adaptive system utilizes observations to derive intrinsic “adaptation algorithms” that compute system’s “settings” that allow it to meet its goal (to fulfill its raison d'etre) within the corresponding environment conditions. This general biological concept was borrowed by computer science to enable automation in complex multi-parameter systems that are highly stochastic like cloud.
An example of a self-adaptivity in the cloud is the predictive autoscaling technology. This technology aims to provide enough cloud resources and container replicas to serve the anticipated user demand according to some service-level objectives. Such technology uses advanced AI techniques and time series-related models to derive the capacity/performance model of application and to forecast the demand on this application. This AI-powered technology was recently introduced by AWS to EC2 service that offers various types of virtual machines.
In his talk, Vladimir Podolskiy, a Ph.D. student from the Technical University of Munich (Germany) will introduce an ongoing project in developing the predictive autoscaling engine SCALENDAR at TUM. Vladimir will give an overview of the SCALENDAR’s architecture, and will dive deeper into its components implementing various functions such as: workload forecasting, microservice capacity/performance modeling, application structural modeling, scaling actions scheduling, and scaling actions timely execution. The aim of the talk is to introduce the research project and to invite the colleagues from the University of Waikato to collaborate on corresponding research topics.