Run Wild: Resource Management System with Generalized Modeling for Microservices on Cloud

Q1 Computer Science

IEEE Cloud Computing Pub Date : 2021-09-01 DOI:10.1109/CLOUD53861.2021.00079

Sunyanan Choochotkaew, Tatsuhiro Chiba, Scott Trent, Marcelo Amaral

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引用次数: 1

Abstract

Microservice architecture competes with the traditional monolithic design by offering benefits of agility, flexibility, reusability resilience, and ease of use. Nevertheless, due to the increase in internal communication complexity, care must be taken for resource-usage scaling in harmony with placement scheduling, and request balancing to prevent cascading performance degradation across microservices. We prototype Run Wild, a resource management system that controls all mechanisms in the microservice-deployment process covering scaling, scheduling, and balancing to optimize for desirable performance on the dynamic cloud driven by an automatic, united, and consistent deployment plan. In this paper, we also highlight the significance of co-location aware metrics on predicting the resource usage and computing the deployment plan. We conducted experiments with an actual cluster on the IBM Cloud platform. RunWild reduced the 90th percentile response time by 11% and increased average throughput by 10% with more than 30% lower resource usage for widely used autoscaling benchmarks on Kubernetes clusters.

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Run Wild:面向云上微服务的资源管理系统

微服务架构通过提供敏捷性、灵活性、可重用性、弹性和易用性来与传统的单片设计竞争。然而，由于内部通信复杂性的增加，必须注意与放置调度协调的资源使用扩展，以及请求平衡，以防止跨微服务的级联性能下降。我们对Run Wild进行了原型设计，这是一个资源管理系统，它控制微服务部署过程中的所有机制，包括扩展、调度和平衡，以在自动、统一和一致的部署计划驱动下，在动态云上优化理想的性能。在本文中，我们还强调了协同位置感知度量在预测资源使用和计算部署计划方面的重要性。我们对IBM Cloud平台上的一个实际集群进行了实验。对于Kubernetes集群上广泛使用的自动缩放基准测试，RunWild将第90个百分位数的响应时间减少了11%，平均吞吐量提高了10%，资源使用量降低了30%以上。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Cloud Computing Computer Science-Computer Networks and Communications

CiteScore

11.20

自引率

0.00%

发文量

期刊介绍： Cessation. IEEE Cloud Computing is committed to the timely publication of peer-reviewed articles that provide innovative research ideas, applications results, and case studies in all areas of cloud computing. Topics relating to novel theory, algorithms, performance analyses and applications of techniques are covered. More specifically: Cloud software, Cloud security, Trade-offs between privacy and utility of cloud, Cloud in the business environment, Cloud economics, Cloud governance, Migrating to the cloud, Cloud standards, Development tools, Backup and recovery, Interoperability, Applications management, Data analytics, Communications protocols, Mobile cloud, Private clouds, Liability issues for data loss on clouds, Data integration, Big data, Cloud education, Cloud skill sets, Cloud energy consumption, The architecture of cloud computing, Applications in commerce, education, and industry, Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), Business Process as a Service (BPaaS)