Home Greater New England Research Interest Group Meeting [May 2021]

Greater New England Research Interest Group Meeting [May 2021]

Upcoming Event Announcement: DevConf.US 2021 Call For Proposals – Urvashi Mohnani  

Project Update #1Creating the new “How Do you Fedora” video series (20 min) – Gabbie Chang

Abstract: The talk will detail the road to creating a new video series profiling some of Fedora’s various contributors and how they use Fedora.

View the .pdf presentation

Research Paper Reading Group – Part 2 (20 min) – Ahmed Sanaullah

Abstract:
This time, we were exploring one of the most fundamental questions in research paper reading: how can I tell if I should read a paper without actually reading it? What makes this a challenging task is that this is not always a question of good vs bad. Even when one has two excellent papers in front of them, the effort it takes to properly read a paper could mean there isn’t sufficient time for both. As a result, a paper would need to be prioritized over another. 

Similar to session 1, this session was also split into two parts. The first part of the session (5 min) outlined a heuristic approach to evaluating papers which is based on some common, high level indicators of quality. By spending 5 minutes evaluating a paper using these indicators, we can estimate if we should be spending the next 5 hours reading through the paper in detail. Then, as a group exercise, in the second part (15 min) we looked at three research papers and attempt to prioritize them on the basis of the indicators discussed in the first part.

View the .pdf presentation

Project Update #2
Robust LSM-Trees Tuning for Workload and Resources Uncertainty (20 min) – Andy Huynh

Abstract:
Modern data systems frequently employ tuning strategies that rely on a priori assumptions on the workload and hardware platform. However, data systems are consistently exposed to changing environments. Workloads may shift as application demands are not consistent, and with the prevalence of the cloud deploying applications on a constant hardware platform is not always guaranteed. Tuning data systems in such uncertain environments may lead to degradation in overall performance.

We introduce a new robust tuning paradigm to aid in the design of data systems with uncertain assumptions by modeling the behavior of the system and then utilizing these models in conjunction with techniques in robust optimization. Our approach is demonstrated through tuning a popular log-structured merge-tree based storage engine, RocksDB. We create a detailed cost model for the standard write and read queries, and frame the design decision as a robust optimization problem that chooses the physical layout of the tree by changing size ratio and memory allocated to the buffer versus bloom filter based on the available resources and expected workload.

In this talk Andy spoke about the process of developing the model, creating the system, and verifying the model against the physical system. Additionally, he touched upon the robust optimization framework and the potential benefits this type of design paradigm has for other systems. As this work is ongoing, he focuses mainly on the design and current implementation.

View the .pdf presentation

Date

May 04 2021
Expired!

Time

EDT
3:00 pm - 4:00 pm

Local Time

  • Timezone: America/New_York
  • Date: May 04 2021
  • Time: 3:00 pm - 4:00 pm

Labels

RIG meeting

Location

Virtual

Speaker

Submit a Comment

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.