Deadline-Aware Task Scheduling in a Tiered IoT Infrastructure

With the proliferation of the Internet of Things (IoT), the current "cloud-only" architectures cannot efficiently handle IoT's data processing and communications needs, while providing satisfactory service latency to support emerging mobile applications on the horizon that require almost real-time responses. fog computing is introduced as a new computing paradigm that distributes computation, communication, control, and storage closer to the end users along the "cloud- to-things" continuum. In this paper, we present a deadline-aware task scheduling mechanism for fog computing in a tiered IoT infrastructure, where service providers exploit the collaboration between their own fog nodes and the rented cloud resources to efficiently execute users' offloaded tasks, at large geographical scale. We first formulate the task-scheduling problem in such a cloud-fog environment as a multi-dimensional 0-1 knapsack problem that is NP-hard, and then propose an efficient algorithmic solution based on ant colony optimization heuristic. The main objective is to maximize the profits of fog service provider while meeting the tasks' deadline constraint. Extensive experimental results show that our proposed optimization and solution significantly improves the system performance compared with existing heuristics.