MIRAS: Model-based Reinforcement Learning for Microservice Resource Allocation over Scientific Workflows
MIRAS: Model-based Reinforcement Learning for Microservice Resource Allocation over Scientific Workflows
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DOI：
10.1109/icdcs.2019.00021
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
Zhe Yang;Phuong Nguyen;Haiming Jin;K. Nahrstedt
中科院分区：
文献类型：
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作者：
Zhe Yang;Phuong Nguyen;Haiming Jin;K. Nahrstedt
微服务是一种将应用程序分解为松散耦合的服务的架构设计，被现代软件设计所采用，包括基于云的科学工作流处理。微服务设计使科学工作流系统更加模块化，更加灵活，更容易开发。然而，微服务工作流执行系统的云部署不是免费的，必须做出适当的资源管理决策以实现某些性能目标（例如，响应时间）内的约束操作成本。然而，由于动态工作负载和每个工作流中微服务的复杂交互，很难实现有效的在线资源分配决策。在本文中，我们提出了一个自适应资源分配方法的微服务工作流系统的基础上，强化学习的最新进展。我们的方法（1）假设微服务工作流系统的先验知识很少，不需要任何精心设计的模型或底层系统的代表性模拟器，（2）避免了高样本复杂性，这是无模型强化学习应用于现实世界场景时的常见缺点。我们表明，我们提出的方法自动实现了有效的资源分配策略，与微服务工作流系统进行了有限数量的耗时交互。我们进行了广泛的评估，以验证我们的方法的有效性，并证明它优于现有的资源分配方法与读取世界模拟工作流。
Microservice, an architectural design that decomposes applications into loosely coupled services, is adopted in modern software design, including cloud-based scientific workflow processing. The microservice design makes scientific workflow systems more modular, more flexible, and easier to develop. However, cloud deployment of microservice workflow execution systems doesn't come for free, and proper resource management decisions have to be made in order to achieve certain performance objective (e.g., response time) within constraint operation cost. Nevertheless, effective online resource allocation decisions are hard to achieve due to dynamic workloads and the complicated interactions of microservices in each workflow. In this paper, we propose an adaptive resource allocation approach for microservice workflow system based on recent advances in reinforcement learning. Our approach (1) assumes little prior knowledge of the microservice workflow system and does not require any elaborately designed model or crafted representative simulator of the underlying system, and (2) avoids high sample complexity which is a common drawback of model-free reinforcement learning when applied to real-world scenarios. We show that our proposed approach automatically achieves effective policy for resource allocation with limited number of time-consuming interactions with the microservice workflow system. We perform extensive evaluations to validate the effectiveness of our approach and demonstrate that it outperforms existing resource allocation approaches with read-world emulated workflows.