Awakening the Cloud Within: Energy-Aware Task Scheduling on Edge IoT Devices

Mobile and IoT devices are becoming increasingly capable computing platforms that are often underutilized. In this paper, we propose a system that leverages the idle compute cycles in a group of heterogeneous mobile and IoT devices that can be clustered to form an edge micro-cloud. At the heart of this system, we formulate a task assignment and scheduling problem that strives to maximize the computational throughput of the constructed micro-cloud while maintaining the energy consumption below an operator specified threshold. Due to the NP-Completeness of this scheduling problem, we design a set of heuristics to solve this problem. We implement a prototype of our system and use it to evaluate its performance and assess its efficiency. Our results demonstrate the system's ability to utilize the available compute capacity of a group of mobile and IoT devices while adhering to pre-specified energy constraints. Compared to other schedulers, our scheduler achieves 10% to 40% improvement in terms of latency minimization, and up to 30% improvement in terms of computational throughput.