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.

Network function virtualization promises a path to rapid innovation in networks. However, due to the complexity of developing these functions, innovations have been slow. Designing a network function is a daunting task that requires combining packet processing optimizations with the network function logic. It is not possible to ignore packet processing optimizations either: an optimized pipeline can have 3 times better performance than an unoptimized pipeline. In this paper, we introduce NFMorph, a framework wherein the network function logic is decoupled from the packet processing optimizations. Developers would specify the packet processing algorithm in a high level language. The runtime then identifies the best set of optimizations on the packet processing algorithm based on the domain knowledge specified by operators and optimization templates for common NF primitives. NFMorph can also justin-time reoptimize based on the workload and environment constraints.

With the advancement of multicore servers, there is a new trend of moving network functions to software servers. Measurement is critical to most network functions as it not only helps the operators understand the network usage and detect anomalies, but also produces feedback to the control loop in management tasks such as load balancing and traffic engineering. Traditional researches on measurement algorithms mainly focus on reducing the memory usage leveraging the fact that measurement can sustain bounded inaccuracy. In this study, we re-evaluate these algorithms on software servers in order to understand their tradeoffs of accuracy and performance. We observe that simple hash tables work better than more advanced measurement algorithms for a variety of measurement scenarios. This is because with better cache design in modern servers and the skewness in the access patterns of measurement tasks, the memory usage of measurement tasks is largely irrelevant to the packet processing performance.