A BROADCAST RECEIVE MODEL FOR COMPUTATIONAL OFFLOADING IN MOBILE CLOUD COMPUTING

Abstract

Computational offloading is a vital part of mobile cloud computing which has attracted so much attention in recent times. It is a way of saving energy in mobile devices by sending an intensive task to a remote server for execution. However, in existing offloading systems, the opportunistic moments to offload a task are often short-lived. The social aware hybrid computational offloading framework involves outsourcing a task to any available surrogate either remote cloud, cloudlet or device to device, this comes with a drawback of super peer having to constantly supervise the network to discover peers. This takes a considerable amount of energy and time. This research aims to develop an improved model for peer discovery in computational offloading which relieves the use of the super peer and transfers the discovery process between network peers. We used a device profiler that serves as an information collector in peers on the network. We evaluated our model by developing an application for our client nodes in order to get information from our nodes. We evaluated our model by using ten peers with different processing power and RAM. On an average, discovery time for all peers in the existing model was 2040 milliseconds, while we have 1,213 milliseconds for our new model. Energy level for the existing model was 72.5% while we have 82.3% for our new model, evaluating our model with the existing one, it was discovered that we saved more energy and time.