MMLU in Network Flow Optimization
In a previous post, I discussed how Maximum Flow problems can be used for network optimization. We focused on a scenario where demands were already routed in the network, and our objective was to determine the maximum demand that could be handled between a given source and a destination metro. We solved this problem by calculating the residual bandwidth for the graph, creating fake demand nodes for each metro with high-capacity edges to avoid them being bottlenecks, and applying Dinic’s algorithm between the source and the destination metro. This is also called a Single Commodity Flow Problem.
Keeping the Pipe Just Full
In this post, I will be discussing a paper published by the Internet pioneer Leonard Kleinrock, titled “Keep the Pipe Just Full, But No Fuller”. The paper’s conclusion is that it is best to keep the internet “pipe” full, without overloading it. This idea is a take on Einstein’s famous quote, “Make everything as simple as possible, but not simpler.”
Flow Distribution Across ECMP Paths
ECMP is crucial for scaling and performance in modern data centers and wide-area networks, which rely on hash-based path selection. It leverages path diversity and keeps a flow’s packets on the same path, preventing reordering with useful properties like stateless operation and no reordering.
Striking a Balance: Exploring Fairness in Buffer Allocation and Packet Scheduling
Recently, I’ve been contemplating the concept of fairness, and I see interesting parallels between being a parent and being a network professional. As human beings, we have an inherent, intuitive sense of fairness that manifests itself in various everyday situations. Let me illustrate this idea with a couple of hypothetical scenarios: