Mobile device power models for energy efficient dynamic offloading at runtime

• Power models of mobile device’s CPU, Wi-Fi, Display unit and memory access are presented.
• The Wi-Fi models clearly differentiate between Send, Receive, Idle and Tail States.
• AIOLOS dynamically makes offloading decision based on different parameters.
• Offloading is validated with computational and communication intensive use cases.
• Energy aware offloading reduces mobile device energy consumption up to 55%.

Spectacular advances in hardware and software technologies have resulted in powerful mobile devices, equipped with advanced processing, storage and network capabilities. Therefore, using resource-intensive applications has become a commodity in many contexts. However, the rapid evolution in hardware and software capabilities has not been paralleled by a similar advance in battery technology. A potential avenue to cope with the device energy resource limitation is to offload computational tasks to cloud infrastructure in the network. In order to offload tasks in an energy-aware manner, we present a detailed model of mobile device energy consumption, addressing the main power consuming subsystems, including CPU, display unit, wireless network interface and memory. Applying this model allows to estimate the power consumed by the application when executed locally, remotely or hybridly (i.e. partly on the device and partly in the cloud infrastructure). Offloading parts of the application can subsequently be decided at runtime based on these energy consumption estimates, also taking into account the power consumed by the device-to-cloud communication over the wireless network. The dynamic offloading has been validated with computational and communication intensive applications. Results show that 18–55% energy gains on the mobile device can be achieved, depending on different conditions.