Section: Application Domains

Capacity Planning in Cloud, Fog and Edge Computing

Cloud and more recently Fog and Edge computing platforms aim at delivering large capacities of computing power. These capacities can be used to improve performance (for scientific applications) or availability (e.g., for Internet services hosted by datacenters). These distributed infrastructures consist of a group of coupled computers that work together and may be spread across a LAN (cluster), across a the Internet (Fog/Edge). Due to their large scale, these architectures require permanent adaptation, from the application to the system level and call for automation of the corresponding adaptation processes. We focus on self-configuration and self-optimization functionalities across the whole software stack: from the lower levels (systems mechanisms such as distributed file systems for instance) to the higher ones (i.e. the applications themselves such as clustered servers or scientific applications).

In 2017, we have been consolidating our expertise around the OpenStack ecosystem. We proposed in particular EnOS, a dedicated framework to conduct performance analyses of OpenStack at large-scale ina reproducible manner. The framework enables researchers to conduct experiments in an automize manner on top of different testbeds such as Grid'5000 and Chameleon. see Sec. 7.1.

In the energy field, we have designed a set of techniques, named Optiplace, for cloud management with flexible power models through constraint programming. OptiPlace supports external models, named views. Specifically, we have developed a power view, based on generic server models, to define and reduce the power consumption of a datacenter's physical servers. We have shown that OptiPlace behaves at least as good as our previous system, Entropy, requiring as low as half the time to find a solution for the constrained-based placement of tasks for large datacenters.