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Multi-access edge computing (MEC) as an emerging and promising paradigm can alleviate the physical resource bottlenecks for smart mobile devices, involving storage and processing capacities. In the MEC system, the traffic load and the quality of service (QoS) can be improved through service caching. However, due to the highly coupled relationship between service caching and offloading decisions, it is extremely challenging to flexibly configure edge service cache within limited edge storage capacity to improve system performance. In this paper, we aim to minimize the average task execution time in the edge system by considering the heterogeneity of task requests, the pre-storage of the application data, and the cooperation of the base stations. Firstly, we formulate the problem of joint computation offloading, service caching, and resource allocation as a Mixed Integer Non-Linear Programming (MINLP) problem, which is difficult to solve because of the coupling relationship between optimization variables. Then we solve the MINLP problem by the decomposition theory based on Generalized Benders Decomposition. Moreover, we develop an efficient algorithm of cooperative service caching and computation offloading, called GenCOSCO, to improve QoS while reducing computation complexity. In particular, for special cases when the service cache configuration is fixed, the FixSC algorithm is proposed to derive the offloading strategy by cache replacement. Finally, numerous simulation experiments indicate that our proposed scheme can significantly reduce the average time consumption of task execution.