CacHing Optimization In Cloud infrastructurE
In this project, we improve the performance of caching in a cloud infrastructure by using predictive and adaptive algorithms. The selection of cache contents is based on a stochastic optimization model. The method leverages information extracted from users’ requests and knowledge of the costs in a specific cloud environment. The aim is to produce an algorithm that optimizes execution time and execution cost simultaneously. The underlying cloud infrastructure is hepiaCloud, a private cloud platform built by our research group on top of OpenStack. Two applications are used to test and validate the designed solution: an image search engine called IFI (Image Finding Image) and a multimedia broadcast application.