Recently as smartphones have a wide range of capabilities a lot of heavy applications like gaming, video editing, and face recognition are now available. However, this kind of applications need intensive computational power, memory, and battery. A lot of researches solve this problem by offloading applications to run on the Cloud due to its intensive storage and computation resources. Later, some techniques chooses 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 only without any consideration to other important metrics. Our previously proposed MCACC framework has introduced a new generation of offloading frameworks that handle this problem by smartly emerging a group of real-time metrics like total execution time, energy consumption, remaining battery, memory, and security into the offloading decision. In this paper, we introduce an enhanced version of the MCACC framework that can now smartly operate under low bandwidth network scenario in addition to its existing capabilities. 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 through a dynamic offloading decision model. The extensive simulation studies show that both heavy and light applications can benefit from the proposed framework while saving energy and improving performance compare to previous counterparts. The enhanced MCACC turns the smartphones to be smarter as the offloading decision is taken without any user interference. |
