Scaling and Scheduling to Maximize Application Performance within Budget Constraints in Cloud Workflows

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Abstract

It remains a challenge to provision resources in the cloud such that performance is maximized and financial cost is minimized. A fixed budget can be used to rent a wide variety of resource configurations for varying durations. The two steps - resource acquisition and scheduling/allocation - are dependent on each other and are particularly difficult when considering complex resource usage such as workflows, where task precedence need to be preserved and the budget constraint is assigned for the whole cloud application instead of every single job. The ability to acquire resources dynamically and trivially in the cloud - while being incredibly powerful and useful - exacerbates this particular resource acquisition and scheduling problem. In this paper, we design, implement and evaluate two auto-scaling solutions to minimize job turnaround time within budget constraints for cloud workflows. The scheduling-first algorithm distributes the application-wide budget to each individual job, determines the fastest execution plan and then acquires the cloud resources, while the scaling-first algorithm determines the size and the type of the cloud resources first and then schedules the workflow jobs on the acquired instances. The scaling-first algorithm shows better performance when the budget is low while the scheduling-first algorithm performs better when the budget is high. The two algorithms can reduce the job turnaround time by 9.6% - 45.2% compared to choosing a fixed general machine type. Moreover, they show good tolerance (between -10.2% and 16.7%) to inaccurate parameters (±20% estimation error).