解决云边混合环境中任务资源与时长不确定下的容量规划与调度问题
Capacity Planning and Scheduling for Jobs with Uncertainty in Resource Usage and Duration
- 采用基于配对采样的约束规划方法处理资源与持续时间双重不确定性
- 相比人工调度，峰值资源使用量显著降低且服务质量不下降
- 适合金融等行业中对时效性与资源效率要求高的场景
全球各地组织定期调度任务以完成用户指定的各种工作。随着向云计算基础设施的转型，本组织采用云与本地服务器相结合的混合模式。本文旨在针对本地网格计算环境进行容量规划，即估算资源需求并安排任务调度。核心贡献在于同时处理任务资源消耗和持续时间的不确定性，这在金融行业尤为重要，因随机市场条件会显著影响任务特征。为实现容量规划与调度，我们需平衡两个相互冲突的目标：(a) 最小化资源使用量；(b) 通过在用户请求截止时间前完成任务，保障高质量服务。本文提出基于确定性估计器和配对采样约束规划的近似方法。最优方法（配对采样）相比人工调度显著降低了峰值资源使用量，且未牺牲服务质量。
原文摘要 · Abstract (English)
Organizations around the world schedule jobs (programs) regularly to perform various tasks dictated by their end users. With the major movement towards using a cloud computing infrastructure, our organization follows a hybrid approach with both cloud and on-prem servers. The objective of this work is to perform capacity planning, i.e., estimate resource requirements, and job scheduling for on-prem grid computing environments. A key contribution of our approach is handling uncertainty in both resource usage and duration of the jobs, a critical aspect in the finance industry where stochastic market conditions significantly influence job characteristics. For capacity planning and scheduling, we simultaneously balance two conflicting objectives: (a) minimize resource usage, and (b) provide high quality-of-service to the end users by completing jobs by their requested deadlines. We propose approximate approaches using deterministic estimators and pair sampling-based constraint programming. Our best approach (pair sampling-based) achieves much lower peak resource usage compared to manual scheduling without compromising on the quality-of-service.
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