Eashan Adhikarla is pursuing a Ph.D. at Lehigh University and joined my group this summer to work on our “DTN as a Service” project. He contributed a lot of energy and novel insights into our work over the summer and I hope we have the opportunity to collaborate again in the near future. Here are some thoughts from Eashan on the summer student experience at ESnet.
This is my second internship at Berkeley Lab and my first at the Scientific Networking Division (SND). It has been full of excitement, thrills, challenges, and surprises, and it is a dream place to be.
This summer, I have been working on the intersection of machine learning and high-performance computing in data transfer nodes (DTNs). ESnet connects 40 DOE sites to 140 other networks and therefore has a high demand for data transfers ranging from megabytes to petabytes. The team is designing DTN-as-a-Service (DTNaaS), where the goal is to deploy and optimize the performance of the data movement across various sites. Managing the transmission control protocol (TCP) flows is a key factor in achieving good performance of transfers over a wide range of network infrastructure. My research helps automate DTN performance via machine learning – thus improving the overall DTNaaS framework.
At present, most DTN software is deployed on bare metal servers, limiting the flexibility for operational configuration changes and automation of transfer configurations. Manually inferring best tuning parameters for a dynamic network is a challenge. To optimize the throughput over TCP flow, we currently often use a pacing rate-function to control packet inter-arrival time. A part of my work proposes two different alternative approaches (supervised or sparse regression-based models) to better predict pacing rate, as well as automate change of related DTN settings based on the nature of the transfers.
Overall, my summer research involved getting experience with a wide set of networking areas of interest:
- Improving the DTN-as-a-Service agent traffic control API with profiles and setting pacing
- Creating a method for statistics retrieval for the harness toolkit for dynamic data visualization and analysis, and preparing these statistics to train the pacing model
- Developing a pacing prediction approach that reduces much of the effort for manual pacing rate configuration.
I was also able to contribute to a separate team’s project on exploring the use of network congestion control algorithms for DTNs; the resulting paper will be submitted to an SC21 workshop.
For me, one of the best things at ESnet is that the summer interns get to work directly with quintessential research scientists and research engineers in the lab and learn a variety of skills to tackle the most challenging problems on a real-world scale. It’s a place from which I always come out as a better version of myself.
If you are interested in learning more about future summer opportunities with ESnet, please see this link (https://cs.lbl.gov/careers/summer-student-and-faculty-program/). We typically post notices and application information starting in January or February.