Risk based planning of network changes in evolving data centers
Risk based planning of network changes in evolving data centers
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不断发展的数据中心中基于风险的网络变化规划
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Minlan Yu
中科院分区：
文献类型：
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作者：
Omid Alipourfard;Jiaqi Gao;Jérémie Koenig;Chris Harshaw;Amin Vahdat;Minlan Yu
数据中心网络随着服务客户流量而发展。当应用网络变化时，运营商面临影响客户流量的风险，因为网络以降低的容量运行，并且更容易受到故障和流量变化的影响。对客户流量的影响最终转化为运营商成本（例如，退款给客户）。然而，在最小化风险的同时规划网络变化是一项挑战，因为我们需要适应各种流量动态和成本函数，同时扩展到大型网络和大变化。如今，运营商经常使用最大化剩余容量（MRC）的计划，这通常在不同的业务动态下产生高成本。相反，我们提出了Janus，它通过利用数据中心网络的高度对称性来搜索大型规划空间。我们对大型Clos网络和Facebook流量跟踪的评估表明，Janus实时生成计划仅需要MRC规划器成本的33~71%，同时适应各种设置。
Data center networks evolve as they serve customer traffic. When applying network changes, operators risk impacting customer traffic because the network operates at reduced capacity and is more vulnerable to failures and traffic variations. The impact on customer traffic ultimately translates to operator cost (e.g., refunds to customers). However, planning a network change while minimizing the risks is challenging as we need to adapt to a variety of traffic dynamics and cost functions while scaling to large networks and large changes. Today, operators often use plans that maximize the residual capacity (MRC), which often incurs a high cost under different traffic dynamics. Instead, we propose Janus, which searches the large planning space by leveraging the high degree of symmetry in data center networks. Our evaluation on large Clos networks and Facebook traffic traces shows that Janus generates plans in real-time only needing 33~71% of the cost of MRC planners while adapting to a variety of settings.