Optimization of Embedded System With Edge Computing and Sleep Modes for Balance Between Energy Consumption and Cache Occupancy

Abstract

The paper considers the embedded system that can either compute tasks locally by itself or offload tasks to the edge server for remote computing during the running period (RP) and switch to the sleep mode to save energy once the RP ends, i.e., the idle period (IP) arrives. The tasks are stored in the cache, and the more tasks are computed during the RP, the less cache space will be occupied at the end of RP, while also leading to more energy consumption. Meanwhile, the sleep mode that the embedded system enters during the IP also influences the energy consumption. Therefore, how to make the optimal tradeoff between energy consumption and cache occupancy arises as an interesting issue. To address this issue, this paper first establishes an optimization-theoretical framework to formulate the energy consumption under the constraint of cache occupancy, then discovers the most energy-saving RP, computing mode (i.e., local or edge computing), and low-power mode. An algorithm based on our discovered theoretical results is proposed for the embedded system to minimize the energy consumption within the acceptable level of cache occupancy. Theoretical analysis and field experiments jointly verify its good performance. Note to Practitioners—This paper addresses the interesting tradeoff between energy consumption and cache occupancy in the embedded system that operates in the environments with limited available energy as well as cache space. It facilitates to improve the operation efficiency of the embedded systems in the area of Internet of Things (IoT) or Cyber-Physical Systems (CPS) that employs edge computing technology to empower embedded systems with more computing capability and sleep modes to guarantee embedded systems with more energy savings, in order to minimize the accumulative energy consumption, while maintaining the cache occupancy in terms of task data bits stored within an acceptable range. Experimental investigations show that the solution proposed here outperforms existing ones.