Abstract
Edge offloading, including offloading to edge base stations (BS) via cellular links and to idle mobile users (MUs) via device-to-device (D2D) links, has played a vital role in achieving ultra-low latency characteristics in 5G wireless networks. This paper studies an offloading method of parallel communication and computation to minimize the delay in multi-user systems. Three different scenarios are explored, i.e., full offloading, partial offloading, and D2D-enabled partial offloading. In the full offloading scenario, we find a serving order for the MUs. Then, we jointly optimize the serving order and task segment in the partial offloading scenario. For the D2D-enabled partial offloading scenario, we decompose the problem into two subproblems and then find the sub-optimal solution based on the results of the two subproblems. Finally, the simulation results demonstrate that the offloading method of parallel communication and computing can significantly reduce the system delay, and the D2D-enabled partial offloading can further reduce the latency.
Highlights
1 Introduction With the proliferation of Internet of Things (IoT) devices and limited by computation resource of mobile users (MUs) as well as communication resources, traditional cloud computing cannot meet the requirements of ultra-low latency in 5G networks [1]
This paper considers three different scenarios based on the location where the data are processed, i.e., the full offloading scenario where the raw data are calculated only at the edge server, the partial offloading where raw data are computed on both the local equipment and the edge server, and the D2D-enabled partial offloading with one part for local computing, and the rest two parts are offloaded to the neighbor MU and the edge server, respectively
6 Results and discussion we provide numerical results to verify the superiority of system delay of three different scenarios based on parallel communication and computational offload strategies
Summary
With the proliferation of Internet of Things (IoT) devices and limited by computation resource of mobile users (MUs) as well as communication resources, traditional cloud computing cannot meet the requirements of ultra-low latency in 5G networks [1]. Mobile edge computing (MEC) has been regarded as a promising means to cope with the challenge [2, 3], as it can effectively relieve network congestion and decrease service latency. The MUs offload intensive computation tasks to the proximate edge servers with more computing resource for execution. Low transmission delay can be achieved because of the short distances between the MUs and edge servers. Ji and Guo [4] proposed an energy-effective resource allocation strategy based on wireless transmission energy in a two-user MEC system. In [5], aiming at controlling local resources and selecting computing modes, an energy-saving
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