Continuous self‐adaptation of control policies in automatic cloud management

Abstract

Deep reinforcement learning has been recently a very active field of research. The policies generated with the use of this class of training algorithms are flexible and thus have many practical applications. In this article we present the results of our attempt to use the recent advancements in reinforcement learning to automate the management of resources in a compute cloud environment. We describe a new approach to self-adaptation of autonomous management, which uses a digital clone of the managed infrastructure to continuously update the control policy. We present the architecture of our system and discuss the results of evaluation which includes autonomous management of a sample application deployed to Amazon Web Services cloud. We also provide the details of the training of the management policy using the Proximal Policy Optimization algorithm. Finally, we discuss the feasibility to extend the presented approach to further scenarios.

Machine Learning based Reconstruction for the MUonE Experiment

“…Neural Networks are therefore widely used in High Energy Physics not only as a classification tool, but also for other tasks like intelligent data reduction and time series analysis [27] or reconstruction of a pulse shape from the front-end electronics [26]. They are used as well to optimize processes in various environments(Reinforcement Learning), for example automate the management of resources in a computing cloud [20].…”