Local and Global Scheduling in Mobile Drop Computing

With the emergence of the Internet of Things (IoT), the Cloud Computing (CC) paradigm has started to exhibit several limitations caused by the fact that it is too far away from the tens of billions of devices. Thus, the edge computing paradigm has come to the fore, aiming to move the processing power to the edge of the network closer to the data sources. Furthermore, the fog computing paradigm comes as an addition, providing Cloud services decentralized at the geographic region level. This trend of migrating from a centralized to a decentralized approach gives way to a new paradigm, Drop Computing (DC), that involves creating opportunistic decentralized ad-hoc social networks consisting of edge and mobile devices. The main goal of this paper is to provide a scheduling model for the Drop Computing paradigm, both at the local end device level and at the global ad-hoc network level. We implement various scheduling solutions using the proposed model and, through simulations in the mobile network environment, we analyze their behavior in multiple scenarios to understand the requirements and outcomes of scheduling in DC.