Abstract
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.
Highlights
Credit information is everywhere, such as finance, medical care, e-purchase platforms and governments
Consider the workflow under credit cloud, an efficient scheduling method is necessary for higher resource utilization and cheaper renting cost based on shareable service instances
2.4 The considered scheduling problem Given the deadline of the workflow δ, the service set of the system S = (S1, S2, · · ·, Sw) and the their pricing model, the objective of the workflow scheduling algorithm is to select appropriate number and type of service instances making a balance between execution time and renting cost
Summary
Credit information is everywhere, such as finance, medical care, e-purchase platforms and governments. The cloud services are usually priced in intervals, such as Amazon EC2, IBM cloud In such cases, the free slots of rented service instances can be shared among the tasks of the same workflow to decrease the total renting cost. In the cloud service environment, such as vehicle network cloud or Internet of things cloud, some scholars have studied the optimization of resource services such as network transmission performance [20], network rate [21], transmission efficiency [22, 23], optimization of joint pricing and power allocation [24] and achieved some results These studies provide a technical reference for the transmission of credit cloud services on distributed heterogeneous platforms. Consider the workflow under credit cloud, an efficient scheduling method is necessary for higher resource utilization and cheaper renting cost based on shareable service instances. The results show the effectiveness and efficiency of the proposed algorithm
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