A Combined Prediction Scheme (CPS) and a concept of Prediction Accuracy Assurance (PAA) were put forward for the runtime of local and remote tasks, on the issue of inapplicability of the singleness policy to all the heterogeneous tasks. The toolkit of GridSim was used to implement the CPS, and PAA was a quantitative evaluation standard of the prediction runtime provided by a specific strategy. The simulation experiments showed that, compared with the local task prediction strategy such as Last and Sliding Median (SM), the average relative residual error of CPS respectively reduced by 1.58% and 1.62%; and compared with the remote task prediction strategy such as Running Mean (RM) and Exponential Smoothing (ES), the average relative residual error of CPS respectively reduced by 1.02% and 2.9%. The results indicate that PAA can select the near-optimal value from the results of comprehensive prediction strategy, and CPS enhances the PAA of the runtime of local and remote tasks in the computing environments.