Energy-optimized bandwidth allocation strategy for mobile cloud computing in LTE networks

This paper presents a mobile cloud computing application model and addresses how to minimize the energy consumption for uploading L size of data load within the T delay constraint. We propose a bandwidth allocation strategy for a LTE network with homogeneous sub-channel condition. Our objective is to allocate more bandwidth to each UE when its relative channel condition becomes better. We formulate the UE´s objective function as the sum of two penalty functions: channel condition penalty function which incentivizes base stations to minimize the energy consumption for every UE and Service Level Agreement (SLA) demand penalty function which guarantees L size of data load that can be uploaded in time. In the network scenario, we formulate the EnerGy Optimized (EGO) bandwidth allocation strategy as a linear programming model and solve it by the Simplex Method. Simulation results show that EGO can save energy of up to 60% for each UE and decrease the SLA violation rate in the network of up to 30% in comparison with the existing bandwidth allocation strategy in the uplink of the LTE network.