Analysis of the Energy-Performance Tradeoff for Delayed Mobile Offloading

Mobile cloud offloading that migrates heavy computation from mobile devices to powerful cloud servers through communication networks can alleviate the hardware limitations of mobile devices for higher performance and energy saving. Different applications usually give different relative importance to the factors of response time and energy consumption. If a delay- tolerant job is deferred up to a given deadline, or until a fast and energy-efficient network becomes available, the transmission time will be reduced, which could lead to energy savings. However, if the reduced service time fails to cover the extra waiting time, this policy may not be suitable for the delay- sensitive applications. In this paper, we investigate two types of delayed offloading policies, the partial model where jobs can leave from the slow phase of the offloading process and then executed locally on the mobile device, and the full offloading model, where jobs can abandon the WiFi Queue and then offloaded via the Cellular Queue. In both models we minimise the Energy-Response time Weighted Product (ERWP) metric. We find that jobs abandon the queue very often especially when the availability ratio (AR) of the WiFi network is relatively small. We can optimally choose the reneging deadline to achieve different energy-performance tradeoff by optimizing the ERWP metric. The amount of delay a job can tolerate closely de- pends on the application type and the potential energy saving for the mobile device. In general one can say that for delay- sensitive applications, the partial offloading model is preferred when having a suitable reneging rate, while for delay-tolerant applications, the full offloading model shows very good results and outperforms the other offloading models when setting the deadline a large value.