Ananta:云规模负载均衡

Parveen Patel, D. Bansal, Lihua Yuan, Ashwin Murthy, A. Greenberg, D. Maltz, Randy Kern, Hemant Kumar, M. Zikos, Hongyu Wu, Changhoon Kim, N. Karri
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引用次数: 289

摘要

第4层负载平衡是创建横向扩展web服务的基础。我们设计并实现了Ananta,这是一个在商用硬件上运行的横向扩展第4层负载均衡器,满足多租户云计算环境的性能、可靠性和操作要求。Ananta以独特的方式结合了路由和分布式系统中的现有技术,并将负载平衡器的组件拆分为基于共识的可靠控制平面和分散的横向扩展数据平面。Ananta的一个关键组件是每个主机中的代理,它可以从负载均衡器接管数据包修改功能,从而使负载均衡器能够自然地随数据中心的大小进行扩展。由于其分布式架构,Ananta提供了跨第二层边界的直接服务器返回(DSR)和网络地址转换(NAT)功能。Ananta的多个实例已经部署在Windows Azure公共云中,总带宽容量超过1Tbps。它满足各种租户的流量需求,包括blob、表和关系存储服务。凭借其横向扩展数据平面,我们可以轻松实现单个公共IP地址超过100Gbps的吞吐量。在本文中,我们描述了云规模负载均衡器的需求,Ananta的设计以及在Windows Azure公共云上实现和运行的经验教训。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ananta: cloud scale load balancing
Layer-4 load balancing is fundamental to creating scale-out web services. We designed and implemented Ananta, a scale-out layer-4 load balancer that runs on commodity hardware and meets the performance, reliability and operational requirements of multi-tenant cloud computing environments. Ananta combines existing techniques in routing and distributed systems in a unique way and splits the components of a load balancer into a consensus-based reliable control plane and a decentralized scale-out data plane. A key component of Ananta is an agent in every host that can take over the packet modification function from the load balancer, thereby enabling the load balancer to naturally scale with the size of the data center. Due to its distributed architecture, Ananta provides direct server return (DSR) and network address translation (NAT) capabilities across layer-2 boundaries. Multiple instances of Ananta have been deployed in the Windows Azure public cloud with combined bandwidth capacity exceeding 1Tbps. It is serving traffic needs of a diverse set of tenants, including the blob, table and relational storage services. With its scale-out data plane we can easily achieve more than 100Gbps throughput for a single public IP address. In this paper, we describe the requirements of a cloud-scale load balancer, the design of Ananta and lessons learnt from its implementation and operation in the Windows Azure public cloud.
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