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Towards Cost-Optimal Policies for DAGs to Utilize IaaS Clouds with Online Learning
arXiv - CS - Performance Pub Date : 2021-06-03 , DOI: arxiv-2106.01847
Xiaohu Wu, Han Yu, Giuliano Casale, Guanyu Gao

Premier cloud service providers (CSPs) offer two types of purchase options, namely on-demand and spot instances, with time-varying features in availability and price. Users like startups have to operate on a limited budget and similarly others hope to reduce their costs. While interacting with a CSP, central to their concerns is the process of cost-effectively utilizing different purchase options possibly in addition to self-owned instances. A job in data-intensive applications is typically represented by a directed acyclic graph which can further be transformed into a chain of tasks. The key to achieving cost efficiency is determining the allocation of a specific deadline to each task, as well as the allocation of different types of instances to the task. In this paper, we propose a framework that determines the optimal allocation of deadlines to tasks. The framework also features an optimal policy to determine the allocation of spot and on-demand instances in a predefined time window, and a near-optimal policy for allocating self-owned instances. The policies are designed to be parametric to support the usage of online learning to infer the optimal values against the dynamics of cloud markets. Finally, several intuitive heuristics are used as baselines to validate the cost improvement brought by the proposed solutions. We show that the cost improvement over the state-of-the-art is up to 24.87% when spot and on-demand instances are considered and up to 59.05% when self-owned instances are considered.

中文翻译:

为 DAG 制定成本最优政策以利用 IaaS 云进行在线学习

顶级云服务提供商 (CSP) 提供两种类型的购买选项,即按需实例和现货实例,在可用性和价格方面具有随时间变化的特性。像初创公司这样的用户必须在有限的预算下运营,同样其他人希望降低成本。在与 CSP 交互时,他们关注的核心是经济高效地利用不同购买选项的过程,可能除了自有实例。数据密集型应用程序中的作业通常由有向无环图表示,该图可以进一步转换为任务链。实现成本效率的关键是确定为每个任务分配特定的期限,以及为任务分配不同类型的实例。在本文中,我们提出了一个框架来确定任务的最后期限的最佳分配。该框架还具有在预定义的时间窗口内确定现货和按需实例分配的最佳策略,以及用于分配自有实例的近乎最佳策略。这些策略被设计为参数化的,以支持使用在线学习来推断云市场动态的最佳值。最后,使用几个直观的启发式方法作为基线来验证所提出的解决方案带来的成本改进。我们表明,在考虑现货和按需实例时,与最先进的成本相比,成本改进高达 24.87%,而在考虑自有实例时,成本改进高达 59.05%。该框架还具有在预定义的时间窗口内确定现货和按需实例分配的最佳策略,以及用于分配自有实例的近乎最佳策略。这些策略被设计为参数化的,以支持使用在线学习来推断云市场动态的最佳值。最后,使用几个直观的启发式方法作为基线来验证所提出的解决方案带来的成本改进。我们表明,在考虑现货和按需实例时,与最先进的成本相比,成本改进高达 24.87%,而在考虑自有实例时,成本改进高达 59.05%。该框架还具有在预定义的时间窗口内确定现货和按需实例分配的最佳策略,以及用于分配自有实例的近乎最佳策略。这些策略被设计为参数化的,以支持使用在线学习来推断云市场动态的最佳值。最后,使用几个直观的启发式方法作为基线来验证所提出的解决方案带来的成本改进。我们表明,在考虑现货和按需实例时,与最先进的成本相比,成本改进高达 24.87%,而在考虑自有实例时,成本改进高达 59.05%。这些策略被设计为参数化的,以支持使用在线学习来推断云市场动态的最佳值。最后,使用几个直观的启发式方法作为基线来验证所提出的解决方案带来的成本改进。我们表明,在考虑现货和按需实例时,与最先进的成本相比,成本改进高达 24.87%,而在考虑自有实例时,成本改进高达 59.05%。这些策略被设计为参数化的,以支持使用在线学习来推断云市场动态的最佳值。最后,使用几个直观的启发式方法作为基线来验证所提出的解决方案带来的成本改进。我们表明,在考虑现货和按需实例时,与最先进的成本相比,成本改进高达 24.87%,而在考虑自有实例时,成本改进高达 59.05%。
更新日期:2021-06-04
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