Abstract
Due to the exploding traffic demands and the diversity of novel applications requiring extensive computation and radio resources, research has been active to devise mechanisms for responding to these challenges. Mobile edge computing (MEC) and device-to-device (D2D) computation task offloading are expected to play a major role in serving devices with limited capabilities, and thus enhance system performance. In this work, we propose a joint computing, communication and cost-aware task offloading optimization problem aiming at maximizing the number of completed tasks, while minimizing energy consumption and monetary cost in D2D-enabled heterogeneous MEC networks. Our proposed scheme allows partial offloading where a requester mobile terminal offloads different parts of its data task simultaneously to multiple peer mobile terminals (MTs), edge servers and cloud. We formulate and solve the optimal allocation strategy then decompose the problem into two sub-problems in an attempt to reduce its complexity. Furthermore, we propose a low-complexity algorithm that generates high performance results and can be applied for large-scale networks. Compared to conventional and state-of-the-art system models, results show the effectiveness of the proposed schemes and provide useful insights into the tradeoffs between the number of completed tasks, energy consumption and monetary cost.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.