Stochastic Analysis of Delayed Mobile Offloading in Heterogeneous Networks - 2018


Mobile cloud offloading that migrates significant computation from mobile devices to powerful cloud servers through Communication networks can alleviate the hardware limitations of mobile devices therefore providing higher performance and saving energy. Totally different applications typically provide different relative importance to response time and energy consumption. If a delay-tolerant job is deferred up to a given deadline, or until a quick and energy-economical network becomes obtainable, the transmission time can be extended, that will save energy because a a lot of energy-economical Communication channel and a less energy-restricted computation platform could become obtainable. But, if the reduced service time fails to cover the extra waiting time, this policy might not be competitive. During this Project, we investigate 2 varieties of delayed offloading policies, the partial offloading model where jobs can leave from the slow phase of the offloading process and be executed locally on the mobile device, and the complete offloading model, where jobs will abandon the WiFi Queue and be offloaded via the Cellular Queue. In each models, we minimize the Energy-Response time Weighted Product (ERWP) metric. Not surprisingly, we have a tendency to realize that jobs abandon the queue usually when the availability of the WiFi network is low. Normally, for delay-sensitive applications the partial offloading model is most well-liked beneath a suitable reneging rate, while for delay-tolerant applications the total offloading model shows very good results and outperforms the other offloading model when selecting a large deadline. From the perspective of energy consumption, the complete offloading model can continually be best, even if the deadline should be extraordinarily long. Solely if job response time is of high importance an optimal deadline to abort offloading within the partial offloading model or the WiFi transmission in the complete offloading model will be found. For reduction of the energy consumption it will invariably be higher to attend longer instead of compute locally or use the cellular network.

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