Profiling Performance of Application Partitioning for Wearable Devices in Mobile Cloud and Fog Computing

• Published in: IEEE access Vol. 7; pp. 12156 - 12166
• Main Authors: Fiandrino, Claudio, Allio, Nicholas, Kliazovich, Dzmitry, Giaccone, Paolo, Bouvry, Pascal
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applications programs; Cloud computing; Edge computing; Electronic devices; energy efficiency; fog computing; Glass; Google; IoT; Mobile cloud computing; Mobile computing; Partitioning; Performance evaluation; Power management; Task analysis; Wearable computers; wearable devices; Wearable technology
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2892508

Wearable devices have become essential in our daily activities. Due to battery constraints, the use of computing, communication, and storage resources is limited. Mobile cloud computing (MCC) and the recently emerged fog computing (FC) paradigms unleash unprecedented opportunities to augment the capabilities of wearable devices. Partitioning mobile applications and offloading computationally heavy tasks for execution to the cloud or edge of the network is the key. Offloading prolongs the lifetime of the batteries and allows wearable devices to gain access to the rich and powerful set of computing and storage resources of the cloud/edge. In this paper, we experimentally evaluate and discuss the rationale of application partitioning for MCC and FC. To experiment, we develop an Android-based application and benchmark energy and execution time performance of multiple partitioning scenarios. The results unveil architectural tradeoffs that exist between the paradigms and devise guidelines for proper power management of the service-centric Internet-of-Things applications.