Computation Offloading for Mobile-Edge Computing with Multi-user

New age smartphones are equipped with high processing power and internet connectivity. Hence, smartphones are capable of executing applications, which were only possible by desktops or laptops until recently. Some examples of such applications are email, banking, flight booking etc. People prefer to use mobile devices for these applications due to the usability and portability of mobile devices. However, because of hardware limitations, mobiles have limited resources such as battery life, power and capacity. Researchers are constantly looking for ways to maximize the usage of these resources. The execution of any application on mobile, needs storage capacity of mobile to store, battery life of mobile to keep running and processing capacity of mobile to process. Thus, more resources are needed to run more applications on these devices. To reduce the load of applications on mobile devices and use the resources efficiently, it is necessary to move some load of applications to remote cloud server in such a way that the applications will run seamlessly. Computation offloading for mobile-edge computing (MEC) is a mechanism to utilize mobile resources well by moving resource-intensive applications to cloud server at network edge. In the case of multiple users, the total computing capacity of the server needs to be taken into consideration for allocating resources to multiple users. The key to efficient computation offloading is allocating applications to mobile and remote server in such a way that minimizes transmission energy. In this paper, we formulate the computation offloading problem as graph cut problem and propose a solution based on spectral clustering computation. First, for the applications on mobile a corresponding network flow graph model is defined. Then, label propagation theory is applied on the network graph and the network graph is simplified by compressing and combining. Finally, the optimal solution is obtained by computation using spectral clustering algorithm. Experiments show that the algorithm is effective in handling programs with loosely coupled as well as highly coupled functions.