Large-scale services are generally hosted on multiple application servers to scale out in today’s data centers. Load balancers distribute users’ requests across these servers. Software load balancer and switch-based load balancer are two typical classes of load balancers. However, most of the existing mechanisms either exhibit high processing latency at load balancers or likely lead to unbalanced requests distribution without considering the disparity of the application servers. In this paper, we study how the disparity of application servers significantly impacts the response time of requests. A fast switch-based Load Balancer considering Application Server states ( LBAS ) then is proposed to minimize the processing latency at both load balancers and application servers. The data plane of LBAS is well designed to store millions of connections in limited storage capacity without violating per-connection consistency. Besides, a partial dynamic weighting algorithm based on the Ridge Regression theory is designed and implemented to decrease the processing latency at application servers. We implement LBAS using the P4 programming language and conduct a series of extensive experiments to evaluate the performance. The results demonstrate that the proposed LBAS mechanism significantly reduces the response time of requests compared with Uniform random, Static weight, and Spotlight in various scenarios.

中文翻译：

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考虑应用服务器状态的基于快速交换机的负载均衡器

大型服务通常托管在多个应用程序服务器上，以在当今的数据中心中进行扩展。负载平衡器在这些服务器之间分发用户的请求。软件负载平衡器和基于交换机的负载平衡器是两类典型的负载平衡器。但是，大多数现有机制要么在负载均衡器处表现出高处理延迟，要么很可能导致请求分配不均衡，而没有考虑应用程序服务器之间的差异。在本文中，我们研究了应用服务器的差异如何显着影响请求的响应时间。考虑应用服务器状态的基于交换机的快速负载均衡器（ LBAS ）然后建议 最小化负载平衡器和应用程序服务器上的处理延迟。LBAS的数据平面经过精心设计，可以在有限的存储容量中存储数百万个连接，而不会破坏每个连接的一致性。此外，设计并实现了基于岭回归理论的局部动态加权算法，以减少应用服务器的处理延迟。我们使用P4编程语言实现LBAS，并进行了一系列广泛的实验以评估性能。结果表明，与各种情况下的均匀随机，静态权重和Spotlight相比，所提出的LBAS机制显着减少了请求的响应时间。