Energy-Aware Collaborative Computation Offloading Over Mobile Edge Computation Empowered Fiber-Wireless Access Networks

With the emergence of smart mobile devices (SMDs) and mobile applications, the cloud-mobile edge computing (MEC) collaborative computation offloading (CMCCO) scheme, i.e., offloading the computation-intensive task from the local SMD to either the MEC server, or the remote mobile cloud computing server (MCC), is widely identified as a promising candidate under the conflict between SMDs' limited computing ability and computing-intensive application requiring higher energy consumption. Meanwhile, the existing CMCCO scenario over integrated cloud-MEC Fiber Wireless broadband access networks (CM-FiWi) architecture, by generally fixing computing ability and transmitting power, still achieves higher computation offloading overhead in terms of task's aggregate response time and SMD's energy consumption. In light of this, the energy-aware collaborative computation offloading (EA-CCO) paradigm with very diverse types of computation tasks over CM-FiWi broadband access network is provided in this paper. An iterative searching algorithm for collaborative computation offloading scheme (ISA-CCO) is proposed as a solution to obtain minimized task offloading overhead, which jointly takes scaling computing ability, variable transmit power, and residual battery rate into considerations. Extensive numerical results demonstrate that the proposed solution outperforms the traditional paradigms, e.g., optimal enumeration collaborative computation offloading scheme (OECCO), approximation collaborative computation offloading algorithm (ACCO), and game theoretic collaborative computation offloading scheme (GT-CCO). More specially, the proposed ISA-CCO scheme obviously achieves lower overall task offloading overhead than those fixed transmit power and computational frequency scaling.