Predictive Resource Management in Energy-constrained Embedded Systems

Abstract

The current trends in Internet of Things (IoT) lead to the deployment of low-power devices covering a wide range of application scenarios. These devices have the goal of executing simple tasks, automatically, usually with strict requirements in terms of space and cost. Typically, these devices have to rely on batteries or by harvesting energy devices (e.g., solar panels), in order to operate. On the other hand, IoT devices may be equipped with powerful multi-core CPUs to achieve performance goals, making the management of the energy budget a challenging task. This requires the development of an effective management system, that takes into account current and future energy budget availability, to dynamically bound the actual allocation of processing resources. Specifically, when exploiting solar panels for power supply, we can leverage on the weather forecast, to estimate the availability of energy in the near future. This paper introduces a predictive energy budget management system, targeting multi-core based embedded platforms. Thanks to both local and large-scale weather information, our solution aims at predicting the future incoming power and, accordingly, tuning the exploitable performance level to keep the system running under any environmental condition.