2021 ESnet Student Assistant
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- SN-Scientific Networking
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- 91532 Requisition #
Do you have a passion for understanding and developing network systems? Consider Berkeley Lab’s Energy Sciences Network (ESnet) Research and Development team. ESnet’s mission is to accelerate science by delivering unparalleled networking capabilities, tools, and innovations. ESnet interconnects the US national laboratory system, is widely regarded as a technical pioneer, and is currently the fastest science network in the world. We are a dynamic organization, highly motivated and focused on results. We are working at the leading edge of software-defined networking, network knowledge plane, dynamic network infrastructure, network visualization, network knowledge plane, multi-domain and multi-layer architectures, deep learning etc. Successful Student Assistants will bring strong and diverse coding skills and are very self motivated.
Project One
Title: SENSE Telemetry and Monitoring System
Term: Spring / Summer 2021
Project Mentor: Dr. Xi Yang
Required skills: Knowledge of computer system and computer network concepts. Familiar with Linux; Proficient in Python programming and shell scripting.
Abstract:
The SENSE project (http://sense.es.net) is building smart network services to accelerate scientific discovery in the era of ‘big data’ driven by Exascale, cloud computing, machine learning and AI. The project’s architecture, models, and demonstrated prototype define the mechanisms needed to dynamically build end-to-end virtual guaranteed networks across administrative domains, with no manual intervention. The successful candidate will work with the SENSE project team to develop network monitoring agents and web portal for network device telemetry, data collection, metrics publication and query. If interested in research work, network telemetry data modeling, automated performance analysis and multi-domain troubleshooting can be additional tasks. Preference will be given to students who can start part-time in the Spring of 2021.
Project Two
Title: Data Transfer Node as-a-Service
Term: Spring / Summer 2021
Project Mentor: Dr. Ezra Kissel
Required skills: Knowledge of Linux-based systems and networking concepts. Familiarity with open source software development, Python programming and scripting. Experience with container technologies (e.g. Docker, LXC, Singularity, Shifter), virtual machines, and storage systems.
Abstract:
The DTN-as-a-Service (DTNaaS) concept aims to provide an optimized and managed Data Transfer Node (DTN) capability that moves data between remote end site storage resources and across wide-area networks. In contrast to the prevalent model of self-administered DTNs that are subject to the varying levels of local expertise needed to maintain and tune such systems, DTNaaS is instead being designed around a model of a pre-configured and managed pool of software images that can be provisioned to serve as a primary data movement service. Such images are designed to support a consistent set of data transfer software with the ability to appropriately tune the host systems, underlying transfer protocols, and service endpoints based on the particulars of available network connectivity and the set of destination node characteristics. Current DTNaaS frameworks are investigating the use of container technologies to instantiate bundled software images across managed DTN node deployments. Containers provide encapsulation of vetted software configurations with the added benefit of reliable, repeatable provisioning and orchestration across any number of DTNaaS nodes being managed in a similar manner.
The successful candidate will work on the development of an on-demand data transfer service capability being developed and prototyped within ESnet. The project involves investigating containerization platforms and associated networking support, integrating storage systems, host system tuning, network path provisioning and protocol evaluation, transfer tool integration, and measurement collection. The scope of work will be defined within a subset of the above topics depending on interest and need. The candidate will have the opportunity and freedom to explore new technologies and techniques in a configurable, high-performance testbed environment.
Project Three
Title: Multi-agent reinforcement learning for optimizing in wireless networks
Term: Spring/Summer 2021
Project Mentor: Dr. Mariam Kiran
Required skills: Multi-agent RL algorithm development
Abstract:
In the Deep Learning and AI for High Performance Networking (DAPHNE) team, we are exploring how traffic engineering will be impacted from wired to wireless network connectivity for our science use case. The research challenges and the variables to optimize on both networks are significantly different. In this project, the successful candidate will work on a new traffic engineering routing problem to be developed for sensor networks. The project involves investigating extensions to MAMRL algorithm, writing training gyms, automation and configuring of traffic movement across the network setup. The scope of work will include building models of a wireless network in ns3 and provide proof-of-concept testing of shortest possible routing with RL informed routing to learn optimal connectivity patterns for reliable wireless networks.
Project Four
Title: Q-Factor - Tuning TCP transfers using real-time data-plane telemetry
Term: Summer 2021
Project Mentor: Dr. Richard Cziva
Required skills: Networking and Linux fundamentals, TCP/IP tuning. *PhD student in networked systems is preferred*
Abstract:
Communication networks are critical components of today’s scientific workflows. Researchers require long-distance, ultra high-speed networks to transfer huge data from acquisition sites to processing sites, and to share measurements with scientists worldwide. While network bandwidth is continuously increasing, the majority of data transfers are unable to efficiently utilize the added capacity due to inherent limitations of parameter settings of the network transport protocols and the lack of network state information at the end hosts. Q-Factor plans to use temporal network state data to dynamically configure current transport protocol parameters to reach higher network utilization and to improve scientific workflows as a result.
Q-Factor leverages programmable network devices with the In-band Network Telemetry (INT) application and delivers a software solution to process in-band measurements at the end hosts. The selected candidate will investigate algorithms and techniques that help in Q-Factor with Data Transfer Nodes (DTN)s, TCP/IP parameters to be configured according to temporal network characteristics, such as round-trip time, network utilization, and network congestion. This tuning is expected to result in increased network utilization, shorter flow completion times, and significantly fewer packet drops caused by network buffers overflow. The successful candidate will work with our partners at Florida International University and learn programmable data planes and use the telemetry information to identify bottlenecks in the network as well as to tune end hosts for better network performance.
Project Five
Title: Network Telemetry Processing at 100Gbit/s and Beyond
Term: Summer 2021
Project Mentor: Dr. Richard Cziva
Required skills: Networking and Linux fundamentals, experience of one or more of today’s packet processing technologies, such as: DPDK, XDP, eBPF, P4 (preferred). Programming experience in Go is a plus. *PhD student in networked systems is preferred*
Abstract:
The next generation of ESnet’s network (ESnet6) will introduce `High-Touch Services`, an internal hardware and software solution to deliver enhanced, high-speed network services for network operators and users in real-time. These high-touch services will enhance user experience, collect service quality metrics (e.g., TCP performance monitoring), monitor network security (e.g., delay and flow monitoring) and enable real-time network debugging and packet analysis in the world’s fastest science network.
You will be working with ESnet’s high-touch team, designing, implementing and further enhancing already implemented high-touch services and associated management software. You will have the option to explore state of the art programmable hardware platforms for high-speed packet processing, such as Barefoot Tofino 6.5TB switches and Netronome cards. This project gives an opportunity to learn and use P4, a programming language designed to allow programming of packet forwarding planes. If you are someone who is excited about high-speed packet processing and network programmability on a multi-100Gbps fiber optic backbone network that stretches across the country and beyond - this internship is for you!
Project Six
Title: Failure Modeling of International Science Networks
Project Mentor: Dr. Richard Cziva
Term: Summer 2021
Required skills: Strong networking knowledge, experience in data analysis and scripting. *PhD student in networked systems is preferred*
Abstract:
International science networks, such as ESnet, move hundreds of Petabytes of data a month between continents, interconnecting instruments with users, storage and processing facilities. While the best quality of service is targeted, sometimes things do go as planned and network failures occur.
This project will be looking at understanding where these failures could occur in similar networks and what impact they have. We will investigate years of operational data collected at ESnet’s various systems and apply natural language processing on unstructured data such as operational emails. With the information derived, we will create a ESnet specific taxonomy of network failures and investigate if we can see correlations between events logged in different layers of the network. Furthermore, we will try to build a prediction for some of the failures.
For full consideration, please submit a statement explaining which project you prefer to apply to.
Notes:
The Spring 2021 Term is 16 weeks (1/11/2021 - 4/30/2021). The Summer 2021 Term is 12 weeks (6/2/2021 - 8/25/2021). Student participation requires 20 hours per week commitment for Spring appointment, and 40 hours per week for Summer appointment. A "late start" date can be considered for academic reasons.
The student assistant appointment can be renewed based upon satisfactory job performance, continuing availability of funds and ongoing operational needs.
Students who participate in their school's Co-Op programs can also apply.
Salary will be predetermined based on student step rates.
This position may be subject to a background check. Any convictions will be evaluated to determine if they directly relate to the responsibilities and requirements of the position. Having a conviction history will not automatically disqualify an applicant from being considered for employment.
Work will be primarily performed at Lawrence Berkeley National Lab in Berkeley, CA, the Champaign, Illinois office, or remotely but limited to individuals residing in the United States.
Equal Employment Opportunity: Berkeley Lab is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, or protected veteran status. Berkeley Lab is in compliance with the Pay Transparency Nondiscrimination Provision under 41 CFR 60-1.4. Click here to view the poster and supplement: "Equal Employment Opportunity is the Law."
Lawrence Berkeley National Laboratory encourages applications from women, minorities, veterans, and other underrepresented groups presently considering scientific research careers.