Online Cloud Offloading Using Heterogeneous Enhanced Remote Radio Heads
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
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AbstractThis paper studies the cloud offloading gains of using heterogeneous enhanced remote radio heads (eRRHs) and dual-interface clients in fog radio access networks (F-RANs). First, the cloud offloading problem is formulated as a collection of independent sets selection problem over a network coding graph, and its NP-hardness is shown. Therefore, a computationally simple online heuristic algorithm is proposed, that maximizes cloud offloading by finding an efficient schedule of coded file transmissions from the eRRHs and the cloud base station (CBS). Furthermore, a lower bound on the average number of required CBS channels to serve all clients is derived. Simulation results show that our proposed framework that uses both network coding and a heterogeneous F-RAN setting enhances cloud offloading as compared to conventional homogeneous F-RANs with network coding.
CitationShnaiwer YN, Sorour S, Sadeghi P, Al-Naffouri TY (2017) Online Cloud Offloading Using Heterogeneous Enhanced Remote Radio Heads. 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall). Available: http://dx.doi.org/10.1109/VTCFall.2017.8288205.
Conference/Event name86th IEEE Vehicular Technology Conference, VTC Fall 2017