Practical Efficient Microservice Autoscaling with QoS Assurance
Practical Efficient Microservice Autoscaling with QoS Assurance
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DOI：
10.1145/3502181.3531460
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发表时间：
2022-01
期刊：
影响因子：
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通讯作者：
Md Rajib Hossen;M. A. Islam;Kishwar Ahmed
中科院分区：
文献类型：
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作者：
Md Rajib Hossen;M. A. Islam;Kishwar Ahmed
云应用程序正逐渐从单片服务转向敏捷的基于微服务的部署。然而，由于大量的松散耦合和交互组件，微服务的有效资源管理带来了巨大的障碍。各种微服务之间的相互依赖使得现有的云资源自动伸缩技术无效。同时，试图捕捉微服务中复杂关系的基于机器学习（ML）的方法需要大量的训练数据，并导致故意违反SLO。此外，这些重机器学习的方法在适应动态变化的微服务操作环境方面很慢。在本文中，我们提出了PEMA (Practical Efficient Microservice Autoscaling)，这是一个轻量级的微服务资源管理器，通过机会性的资源减少来找到有效的资源分配。PEMA的轻量级设计支持新颖的工作负载感知和自适应资源管理。通过使用三个原型微服务实现，我们表明，与基于规则的商业资源分配相比，PEMA可以找到有效的资源分配，并节省高达33%的资源。
Cloud applications are increasingly moving away from monolithic services to agile microservices-based deployments. However, efficient resource management for microservices poses a significant hurdle due to the sheer number of loosely coupled and interacting components. The interdependencies between various microservices make existing cloud resource autoscaling techniques ineffective. Meanwhile, machine learning (ML) based approaches that try to capture the complex relationships in microservices require extensive training data and cause intentional SLO violations. Moreover, these ML-heavy approaches are slow in adapting to dynamically changing microservice operating environments. In this paper, we propose PEMA (Practical Efficient Microservice Autoscaling), a lightweight microservice resource manager that finds efficient resource allocation through opportunistic resource reduction. PEMA's lightweight design enables novel workload-aware and adaptive resource management. Using three prototype microservice implementations, we show that PEMA can find efficient resource allocation and save up to 33% resources compared to the commercial rule-based resource allocations.