From 3ca3e6088f19197a3f894e4a2a35703639aecffb Mon Sep 17 00:00:00 2001 From: Emir Catir <78615181+empazi@users.noreply.github.com> Date: Thu, 12 Sep 2024 21:14:03 +0200 Subject: [PATCH] Week 4: Scientific Proposal (#2472) week 4 scientific proposal Co-authored-by: empazi --- .../week4/catir-robcla/README.md | 26 +++++++++++++++++++ 1 file changed, 26 insertions(+) create mode 100644 contributions/scientific-paper/week4/catir-robcla/README.md diff --git a/contributions/scientific-paper/week4/catir-robcla/README.md b/contributions/scientific-paper/week4/catir-robcla/README.md new file mode 100644 index 0000000000..5bcdad927c --- /dev/null +++ b/contributions/scientific-paper/week4/catir-robcla/README.md @@ -0,0 +1,26 @@ +# Assignment Proposal + +## Title + +Predicting Node Failures in an Ultra-Large-Scale Cloud Computing Platform: An AIOps Solution + +## Names and KTH ID + +- Emir Catir (catir@kth.se) +- Robin Claesson (robcla@kth.se) + +## Deadline + +- Week 4 + +## Category + +- Scientific Paper + +## Description + +The paper "[Predicting Node Failures in an Ultra-Large-Scale Cloud Computing Platform: An AIOps Solution](https://dl.acm.org/doi/abs/10.1145/3385187)" describes the process of building an ultralarge-scale AIOps solution to combat the challenges cloud computing environments face. The process is thoroughly described and reviewed by the authors. + +**Relevance** + +This research is relevant to DevOps because it brings a perspective on how DevOps can leverage AI to improve resilience by reducing downtime with the help of automed node failure detection.