From One to Many FemtoClouds
Hend K. Gedawy, Ali E. Elgazar, Khaled A. Harras · 2020
Many novel IoT-based applications now require large compute resources, high-privacy, and low-latency. This demand has triggered the rise of fog and edge computing to complement the high-latency and low-privacy cloud. Fog computing provides lower latency by bringing computational servers closer to the user, typically within the city's vicinity. However, due to the high cost of deploying such fog servers at scale, and poor network infrastructures in many countries and areas, edge computing has been introduced. Edge computing argues for leveraging compute resources, typically within a user's immediate environment, on distributed ensembles of devices called FemtoClouds. In this paper, we propose Maestro, a system that aids users by offloading computational jobs from them to multiple FemtoClouds in their immediate vicinity. We propose an integrated architecture for Maestro, which incorporates a new scheduling algorithm that assigns compute tasks to FemtoClouds. We implement a full prototype of Maestro, and evaluate its performance on our experimental testbed, as well as through emulation. Our results show that our system and scheduler outperforms state-of-the-art by up to 55%.