Smartphones are becoming increasingly popular with a wide range of capabilities for the purpose of handling heavy applications like gaming, video editing, and face recognition etc. These kinds of applications continuously require intensive computational power, memory, and battery. Many of the early techniques solve this problem by offloading these applications to run on the Cloud due to its famous resources availability. Later, enhanced techniques choosed to offload part of the applications while leaving the rest to be processed on the smartphone based on one or two metrics like power and CPU consumption without any consideration to the communication and network overhead. With the notable development of the smartphone’s hardware, it becomes crucial to develop a smarter offloading framework that is able to efficiently utilize the available smartphone’s resources and only offload when necessary based on real-time decision metrics. This paper proposed such framework, which we called Mobile Capabilities Augmentation using Cloud Computing (MCACC). In this framework, any mobile application is divided into a group of services, and then each of them is either executed locally on the mobile or remotely on the Cloud based a novel dynamic offloading decision model. Here, the decision is based on five real-time metrics: total execution time, energy consumption, remaining battery, memory and security. The extensive simulation studies show that both heavy and light applications can benefit from our proposed model while saving energy and improving performance compare to previous techniques. The proposed MCACC turns the smartphones to be more smarter as the offloading decision is taken without any user interaction. |
