MLEFlow: Learning from History to Improve Load Balancing in Tor

Tor has millions of daily users seeking privacy while browsing the Internet. It has thousands of relays to route users’ packets while anonymizing their sources and destinations. Users choose relays to forward their traffic according to probability distributions published by the Tor authorities. The authorities generate these probability distributions based on estimates of the capacities of the relays. They compute these estimates based on the bandwidths of probes sent to the relays. These estimates are necessary for better load balancing. Unfortunately, current methods fall short of providing accurate estimates leaving the network underutilized and its capacities unfairly distributed between the users’ paths. We present MLEFlow, a maximum likelihood approach for estimating relay capacities for optimal load balancing in Tor. We show that MLEFlow generalizes a version of Tor capacity estimation, TorFlow-P, by making better use of measurement history. We prove that the mean of our estimate converges to a small interval around the actual capacities, while the variance converges to zero. We present two versions of MLEFlow: MLEFlow-CF, a closed-form approximation of the MLE and MLEFlow-Q, a discretization and iterative approximation of the MLE which can account for noisy observations. We demonstrate the practical benefits of MLEFlow by simulating it using a flow-based Python simulator of a full Tor network and packet-based Shadow simulation of a scaled down version. In our simulations MLEFlow provides significantly more accurate estimates, which result in improved user performance, with median download speeds increasing by 30%.

Explaining Regeneration: Cells and Limbs as Complex Living Systems, Learning From History

Regeneration has been investigated since Aristotle, giving rise to many ways of explaining what this process is and how it works. Current research focuses on gene expression and cell signaling of regeneration within individual model organisms. We tend to look to model organisms on the reasoning that because of evolution, information gained from other species must in some respect be generalizable. However, for all that we have uncovered about how regeneration works within individual organisms, we have yet to translate what we have gleaned into achieving the goal of regenerative medicine: to harness and enhance our own regenerative abilities. Turning to history may provide a crucial perspective in advancing us toward this goal. History gives perspective, allowing us to reflect on how our predecessors did their work and what assumptions they made, thus also revealing limitations. History, then, may show us how we can move from our current reductionist thinking focused on particular selected model organisms toward generalizations about this crucial process that operates across complex living systems and move closer to repairing our own damaged bodies.

The US 2020 Election and Learning from History

The US 2020 presidential election constitutes an anomaly for the general paradigm of learning from history that organizes cross-national research in politics. Was it a unique event that can be ignored or must we consider that history is no longer a reliable guide?