Mobile Cloud Computing (MCC) augments capabilities of mobile devices by Offloading applications to the cloud. Task allocation is one of the most challenging issues in MCC considering neighboring mobile devices as the service providers. Given an application Offloading request, the objective of the task allocation is to select service providers minimizing the completion time of the application Offloading as well as consumed energy of all participating mobile devices while satisfying some QoS constraints. This paper models the task allocation problem in MCC as a constrained multi-objective optimization and proposes a two stages approach to solve the problem. In the first stage, a Multi-objective Resource Allocation based on Branch and Bound algorithm (MRABB) is designed to obtain Pareto solution set. In the subsequent stage, using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method and given user’s preferences, the best compromise solution is determined. Furthermore, a context-aware software for Offloading in Mobile Cloud (OMC) is designed and implemented to collect contextual used in task allocation, and manage the Offloading process. The results show the ability of the proposed multi-objective task allocation method to manage the trade-off between time and energy comparing to traditional algorithms.

In order to increase throughput and spectral efficiency in a heterogeneous network including a macro and a femtocell, we propose a combined Offloading with In-Band Full Duplex (IBFD) scheme in this paper. Traffic Offloading to the femtocell is deployed to transmit network users traffic to a macrocell base station in the uplink. In other words, all or a part of the traffic is offloaded to the femtocell and then transmitted to the macrocell, while the rest of traffic is transmitted to the macrocell directly. In the femtocell, we deploy and investigate IBFD technology, i.e., simultaneous transmit and receive traffic in one frequency band. Furthermore, in order to improve throughput of the network, we propose several scheduling schemes to transmit traffic. Finally, optimal number and position of users who use IBFD or do not use it, are determined. We propose a heuristic solution to find optimal position of IBFD users. Simulation results verify the network throughput improvement and power consumption reduction.