Elastic Resource Provisioning for Cloud Workflow Applications

Many workflow applications are moved to clouds for elastic capacities. Elastic resource provisioning is one of the most important problems. Realistic factors are involved, including an interval-based charging model, data transfer time, VM loading time, software setup time, resource utilization, and the workflow deadline. A multirule-based heuristic is proposed for the problem under study which contains two components: a deadline division and task scheduling. Taking into account the gaps between tasks, the impact of different critical paths and the precedence constraints, the workflow deadline is properly divided into task deadlines based on the solution of a relaxed problem. The relaxed problem is modeled by integer programming and solved by CPLEX. All tasks are sorted in terms of the developed depth-based rule. For different realistic factors, three priority rules are developed to allocate tasks to appropriate available time slots, from which a weighted rule is constructed for task scheduling. The weights are calibrated by random instances. Experiments are conducted using a benchmark realistic workflow. Experimental results show that the proposal is effective and efficient for realistic workflows. Note to Practitioners-This paper is motivated by the elastic resource provisioning problem of virtual data centers in clouds which are managed by scientific research institutes, or small or middle-sized enterprises, to minimize the total resource renting cost of cloud workflow applications. For example, when we rent virtual machines from Amazon EC2 for big-data analysis applications, the number and the type of rented virtual machines change in terms of saving on renting costs. Because virtual machines are priced in intervals in most commercial clouds, tasks must be properly scheduled on rented virtual machines to improve the utilization of rented intervals. Existing methods do not factor in software setup times, yet these have an impact on scheduling effectiveness (especially for the cases when tasks have shorter execution times than software setup times). In this paper, a heuristic called MRH is developed for elastic virtual machine provisioning. Similarly, practical factors (utilization of rented intervals, VM loading time, software setup, data transfer, execution efficiency, the match between the length of time slots and that of task executions) are considered in MRH. Experimental results on realistic applications show that MRH could decrease virtual machine renting costs by up to 78.57%. Furthermore, MRH is fast which could meet the quick reaction times re-quired in modern IT applications in rented virtual data centers (such as data centers built on Amazon EC2).