In the edge computing environment, the uneven geographical distribution of tasks may lead to unbalanced load on the edge server. In addition, some larger tasks are difficult to completely offload to edge servers, which cannot fully utilize edge server resources. To solve the above problems, we propose a task scheduling method to minimize the completion time by combining the horizontal edge collaboration and fine-grained task partial offloading technology. First, combining horizontal edge collaboration and fine-grained task partial offloading technology, considering the location relationship between users and edge servers in multiuser multiedge server scenario, a task partial offloading optimization problem is established to minimize task completion time. Second, due to the nonconvex and variables coupling, we decompose the original problem into resource allocation, user-server association, and offloading strategy subproblems. A task scheduling algorithm based on improved teaching-learning-based optimization (ITLBO) is proposed to obtain the best task scheduling decision which includes task offloading location and offloading ratio. Simulation results show that the proposed method can effectively reduce the task completion time in edge collaboration environment.
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