基于任意播播的cdn分布式负载管理

Abhishek Sinha, Pradeepkumar Mani, Jie Liu, A. Flavel, D. Maltz
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引用次数: 5

摘要

任播是一种互联网寻址协议,其中多个主机共享相同的ip地址。用于地理复制http服务的现代内容分发网络(cdn)的流行架构包括用于服务的多层代理节点和位于同一位置的dns服务器,用于在不同代理上进行负载平衡。代理和dns服务器都使用任意播寻址,这提供了设计的简单性和服务的高可用性,但代价是部分丢失路由控制。由于任播的本质,位于同一位置的dns服务器的负载管理操作也会影响网络中附近代理的负载。这使得分布式负载管理问题极具挑战性。在本文中,我们提出了一个分析框架来制定和解决这种情况下的负载管理问题。我们考虑两种不同的算法。在本文的前半部分,我们将负载管理问题视为一个凸优化问题。在双重分解技术的基础上,我们提出了一种完全分布式的负载管理算法。该算法利用底层的任播机制本身实现了节点之间的有效协调,从而避免了任何外部控制通道的需要。在本文的后半部分,我们考虑了一种替代的贪婪负载管理启发式算法,目前在一个主要的商业CDN中生产。研究了它的动力学特性,分析了它的运行特性和稳定性。最后,我们批判性地评估了这两种算法,并使用跟踪驱动的模拟探索了它们的最优性与复杂性之间的权衡。
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Distributed load management in anycast-based CDNs
Anycast is an internet addressing protocol where multiple hosts share the same IP-address. A popular architecture for modern Content Distribution Networks (CDNs) for geo-replicated HTTP-services consists of multiple layers of proxy nodes for service and co-located DNS-servers for load-balancing on different proxies. Both the proxies and the DNS-servers use anycast addressing, which offers simplicity of design and high availability of service at the cost of partial loss of routing control. Due to the very nature of anycast, load-management actions by a co-located DNS-server also affects loads at nearby proxies in the network. This makes the problem of distributed load management highly challenging. In this paper, we propose an analytical framework to formulate and solve the load-management problem in this context. We consider two distinct algorithms. In the first half of the paper, we pose the load-management problem as a convex optimization problem. Following a dual decomposition technique, we propose a fully-distributed load-management algorithm by introducing FastControl packets. This algorithm utilizes the underlying anycast mechanism itself to enable effective coordination among the nodes, thus obviating the need for any external control channel. In the second half of the paper, we consider an alternative greedy load-management heuristic, currently in production in a major commercial CDN. We study its dynamical characteristics and analytically identify its operational and stability properties. Finally, we critically evaluate both the algorithms and explore their optimality-vs-complexity trade-off using trace-driven simulations.
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