Multi-heuristic scheduling methods for workflow in credit cloud

In the credit cloud, credit services are sold to applications for credit computing, credit fusion and credit risk estimates. Plenty of services with different performance for the same task may have different execution time and charged by various ways. The users have specific requirements for the workflow completion time or cost. Hence, to meet the user’s satisfaction is an important challenge. In this paper, we propose heuristic scheduling methods for credit workflow with total cost minimization, and the deadline should be satisfied. The problem can be divided into two sub-problems, task-mode mapping and task tabling on renting service instances. For the task-mode mapping problem, a recursive heuristic method is constructed to select appropriate service for each task of the workflow. Then another heuristic algorithm based is established to get a final schema with deadline constraint. We discussed the service instance rented in shareable manner and compared with un-shareable manner. Three renting strategies are discussed in detail. Experimental results show the effectiveness and efficiency of the proposed algorithm.

Model Hub Median Problem Dengan Batasan Travel Time

This article describes the development of an uncapacitated p-hub median problem model. The established model will be applied in determining the location of the hub. Generally in the median p-hub model problem, it is known that the number of hubs built with the total cost minimization function. In this developed model, there is no limit to the number of hubs and the function of the intended destination is how many hubs are built. The model also looks for where the hub location will be built and which nodes are allocated to each hub. By eliminating the limitation of the number of hubs built, the model adds a total total timeout limit. The numerical experiments are dealing with the model. The solution to solve the model using excel solver. So that it will be designed spread sheet excel appropriate model. In numerical experiments, dummy data representing real systems will be used with the aim of shortening computational time. Numerical experiments are performed in several conditions scenarios. Computational results generate the location of hubs and nodes allocated to each hub with various experimental scenarios.