A Multi-User Effective Computation Offloading Mechanism for MEC System: Batched Multi-Armed Bandits Approach

With the development of the fifth generation (5G) technology, Mobile Edge Computing (MEC) is becoming a useful architecture, which is envisioned as a cloud extension version. Users within the MEC system could save more time on data transmission, in which the tasks can be executed on the edge side. Multi-armed bandits (MAB) are powerful tools that help users offload tasks to their best servers in the MEC system. However, as the system scale grows, the traditional MAB algorithms are weak and the channel condition deteriorates rapidly, which will lead to an increasing time cost of edge tasks offloading and computing. Therefore, in this paper, we propose a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</u>atch-based <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</u>ulti-user <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u>erver <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u>limination (BMSE) to solve such a problem. BMSE contains two sub-algorithms, BMSE in <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">U</u>ser <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u>evel (BMSE-UL) and BMSE in <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u>ystem <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u>evel (BMSE-SL). BMSE-UL is applied among users for the initial stage which can help them discard servers with obvious bad performance. BMSE-SL is applied in the whole MEC system which can guide users offloading tasks collectively. Furthermore, we establish the optimality of the proposed algorithms by proving the sub-linearity convergence of their regrets and demonstrate the effectiveness through extensive experiments.