SHOWAR: Right-Sizing And Efficient Scheduling of Microservices
SHOWAR: Right-Sizing And Efficient Scheduling of Microservices
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DOI：
10.1145/3472883.3486999
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
A. F. Baarzi;G. Kesidis
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文献类型：
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作者：
A. F. Baarzi;G. Kesidis
在设计分布式云应用程序时，微服务架构已被广泛采用，其中应用程序被解耦为多个小组件(例如：“microservices”)。部署微服务的挑战之一是为每个微服务找到最佳的资源量（即大小）和实例数量（即副本），以保持良好的性能，并防止资源浪费和利用不足，这是不划算的。本文介绍了SHOWAR，一个通过确定每个微服务的副本数量（水平扩展）和CPU和内存数量（垂直扩展）来配置资源的框架。对于垂直扩展，SHOWAR使用历史资源使用的经验方差来找到最优大小并减少资源浪费。对于水平扩展，SHOWAR使用控制理论的基本思想以及内核级性能指标。此外，一旦找到每个微服务的大小，SHOWAR通过为调度器生成亲和规则（即提示）来弥合最优资源分配和调度之间的差距，从而进一步提高性能。我们使用各种微服务应用程序和实际工作负载进行的实验表明，与最先进的自动伸缩和调度系统相比，SHOWAR平均可将资源分配提高22%，同时将第99百分位的端到端用户请求延迟提高20%。
Microservices architecture have been widely adopted in designing distributed cloud applications where the application is decoupled into multiple small components (i.e. "microservices"). One of the challenges in deploying microservices is finding the optimal amount of resources (i.e. size) and the number of instances (i.e. replicas) for each microservice in order to maintain a good performance as well as prevent resource wastage and under-utilization which is not cost-effective. This paper presents SHOWAR, a framework that configures the resources by determining the number of replicas (horizontal scaling) and the amount of CPU and Memory for each microservice (vertical scaling). For vertical scaling, SHOWAR uses empirical variance in the historical resource usage to find the optimal size and mitigate resource wastage. For horizontal scaling, SHOWAR uses basic ideas from control theory along with kernel level performance metrics. Additionally, once the size for each microservice is found, SHOWAR bridges the gap between optimal resource allocation and scheduling by generating affinity rules (i.e. hints) for the scheduler to further improve the performance. Our experiments, using a variety of microservice applications and real-world workloads, show that, compared to the state-of-the-art autoscaling and scheduling systems, SHOWAR on average improves the resource allocation by up to 22% while improving the 99th percentile end-to-end user request latency by 20%.