Risk based planning of network changes in evolving data centers

Abstract

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.