面向动态工作负载的集群资源管理在线优化方法
Cluster Resource Management for Dynamic Workloads by Online Optimization
AI总结：
针对动态多样化工作负载的集群资源管理问题，提出基于模拟退火的在线优化框架，可在性能与成本间动态权衡，经多场景实验验证有效，且可扩展适配更多资源管理场景。
摘要：
过去十年间，针对长期运行、动态且多样化的工作负载（如查询流处理或分布式深度学习）的资源管理问题，学界已从不同维度提出了诸多方案。尤其对于由容器化微服务构成的应用，研究人员已尝试解决多类动态选择问题，例如：虚拟化服务（如IaaS/VM）的类型与数量、不同微服务的垂直与水平扩缩容、微服务到VM的分配、任务调度，或是上述问题的组合。在此背景下，我们认为模拟退火（simulated annealing）这类框架非常适合在线权衡性能（SLO）与成本，尤其适用于复杂工作负载和云服务供给随时间变化的场景。基于结合性能与成本项的宏观目标，退火算法能够实现轻量且一致的探索与利用策略。本文首先介绍模拟退火的相关背景，随后通过实验验证其在不同案例中的有效性，包括单一类型工作负载（如分布式深度学习）的服务选择、混合类型工作负载的服务选择（探索部分为分类类型的选项集），以及微服务基准测试的容器规格配置。最后我们讨论了基础退火平台如何应用于其他资源管理问题、与其他方法混合，以及适配用户指定的经验法则。
英文摘要：
Over the past ten years, many different approaches have been proposed for different aspects of the problem of resources management for long running, dynamic and diverse workloads such as processing query streams or distributed deep learning. Particularly for applications consisting of containerized microservices, researchers have attempted to address problems of dynamic selection of, for example: types and quantities of virtualized services (e.g., IaaS/VMs), vertical and horizontal scaling of different microservices, assigning microservices to VMs, task scheduling, or some combination thereof. In this context, we argue that frameworks like simulated annealing are highly suitable for online navigation of trade-offs between performance (SLO) and cost, particularly when the complex workloads and cloud-service offerings vary over time. Based on a macroscopic objective that combines both performance and cost terms, annealing facilitates light-weight and coherent policies of exploration and exploitation. In this paper, we first give some background on simulated annealing and then experimentally demonstrate its usefulness for different case studies, including service selection for both a single type of workload (e.g., distributed deep learning) and a mixture of workload types (exploring a partially categorical set of options), and container sizing for microservice benchmarks. We conclude with a discussion of how the basic annealing platform can be applied to other resource-management problems, hybridized with other methods, and accommodate user-specified rules of thumb.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
