  With the development of 5G technology, mobile edge computing (MEC) is
becoming a useful architecture, which is envisioned as a cloud computing
extension version. Users within MEC system could deal with data processing at
edge terminals, which can reduce time for communication or data transmission.
Multi-armed bandits (MAB) algorithms are powerful tools helping users
offloading tasks to their best servers in MEC. However, as the number of users
and tasks growing, the frequency of selecting servers and the cost of making
decision is growing rapidly under traditional MAB algorithms. Inspired by this,
in this paper, we propose a Batch-based Multi-user Server Elimination (BMSE)
algorithm to solve such problem, which includes two sub-algorithms. We firstly
propose a sub-algorithm in user level (BMSE-UL) to reduce the time cost. In
BMSE-UL, users can simplify its own available server groups and offload tasks
collectively. Then another sub-algorithm in system level (BMSE-SL) is proposed
to reduce the frequency of making decision. In BMSE-SL, the system can cut down
all the suboptimal task offloading actions and make the choosing option unique.
Furthermore, we establish the optimality of the proposed algorithms by proving
the sub-linearity convergence of their regrets and demonstrate the
effectiveness of BMSE by extensive experiments.
