Journal
IEEE TRANSACTIONS ON MOBILE COMPUTING
Volume 20, Issue 8, Pages 2654-2670Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TMC.2020.2984134
Keywords
Task analysis; Processor scheduling; Energy consumption; Schedules; Performance evaluation; Systems architecture; Batteries; Edge computing; FemtoCloud; internet of things; IoT cloud; mobile cloud; mobile computing
Funding
- NPRP Grant from the Qatar National Research Fund (Qatar Foundation) [8-1645-1-289]
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This paper introduces a system called RAMOS that leverages idle resources from mobile and IoT devices to form an edge FemtoCloud, operating under multi-objective schedulings for latency minimization and energy efficiency. Despite the NP-Completeness of the scheduling problem, heuristics are designed to solve it. Through prototype implementation, RAMOS demonstrates its performance and efficiency in meeting various scheduling objectives.
Mobile and IoT devices are becoming increasingly capable computing platforms that are often underutilized. In this paper, we propose RAMOS, 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 FemtoCloud. At the heart of this system, we formulate a multi-objective, resource-aware task assignment and scheduling problem. The scheduler runs in two main modes; latency-minimization and energy-efficiency. Under the latency-minimization mode, it strives to maximize the computational throughput of the constructed FemtoCloud while maintaining the energy consumption below an operator specified threshold. Under the energy-efficient mode, it minimizes the total energy consumed in the FemtoCloud while meeting defined tasks deadlines. Due to the NP-Completeness of this scheduling problem, we design a set of heuristics to solve it. We implement a prototype of our system and use it to evaluate its performance and efficiency. Our results demonstrate the system's ability to meet different scheduling objectives while adhering to pre-specified time and energy constraints. Compared to other schedulers, RAMOS achieves 10 to 40 percent completion time improvement under latency minimization mode and up to 30 percent more energy-efficiency under the energy-efficient mode.
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