Inspiration
We were inspired by the data sets provided by ExxonMobil. With such large amounts of information and different factors, it was a challenge to figure out a way to represent the data in a manner that displays useful trends. After analyzing the data we concluded that we should try to optimize the overall wait time.
What it does
After testing out some methods of optimization, we decided on prioritizing smaller tasks. Our challenge was then to write an algorithm that took tasks and sorted them into a queue if there were no idle cores.
Challenges we ran into
A short way into our analysis, we realized that there were noise values in our data sets. We had to figure out a way to minimize the impact of this uncertainty. Another problem to solve was to find the actual amount of cores that were available.
Accomplishments that we're proud of
Despite being inexperienced in data analysis, we were able to discover trends that could use optimizing, and built a simulator to run our improved algorithm.
Log in or sign up for Devpost to join the conversation.