PhD Proposal by Joshua Kimball

Event Details
  • Date/Time:
    • Friday November 20, 2020
      10:00 am - 12:00 pm
  • Location: Remote: Blue Jeans
  • Phone:
  • URL: Bluejeans
  • Email:
  • Fee(s):
    N/A
  • Extras:
Contact
No contact information submitted.
Summaries

Summary Sentence: PerfDB + PerfML: Supporting Big Data-Driven Research on Fine-Grained Performance Phenomena

Full Summary: No summary paragraph submitted.

Title

PerfDB + PerfML: Supporting Big Data-Driven Research on Fine-Grained Performance Phenomena

 

Joshua Kimball

Ph.D. Student in Computer Science

School of Computer Science

Georgia Institute of Technology

 

Date: November 20, 2020

Time: 10:00 AM to 12:00PM EST

Location: https://bluejeans.com/287403017

**Note: this proposal is remote-only. complete meeting info. below.**

 

Committee

Dr. Calton Pu (Advisor) - School of Computer Science, Georgia Institute of Technology

Dr. Arulaj Joy - School of Computer Science, Georgia Institute of Technology

Dr. Ling Liu - School of Computer Science, Georgia Institute of Technology

Dr. Sham Navathe - School of Computer Science, Georgia Institute of Technology

Dr. Qingyang Wang - School of Computer Science, Louisiana State University

 

Abstract

The long-tail latency problem is a well-documented problem in large-scale system topologies like cloud platforms. Long-tail latency can lead to degraded quality of experience, potential economic loss and less predictable system performance overall. In the past, platforms have over-provisioned to mitigate tail latency and its effects. Instead, we propose studying these performance bugs systematically and addressing their underlying root cause. In this proposal, we present the first-generation population study of VLRT requests using PerfDB, our data management system. Specifically, we present the first study of VLRT periods on integrated data from 500 experiments. We find evidence that many VLRT coincide with Cross-Tier Queue Overflow (CTQO) induced by millibottlenecks. In our second analysis, we conduct the first study of a phenomenon called Localized Latency Requests (LLR)—requests with latency between 100-500ms. Finally, we propose a machine learning system, PerfML, which employs a teamed-classifier method to automatically isolate and diagnose fine-grained performance anomalies found in a collection of fine-grained (sampling at 50ms intervals) resource monitoring data covering a wide variety of hardware and software configurations spanning hundreds of experiments and terabytes of experimental data. 

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Meeting URL

https://bluejeans.com/287403017

 

Meeting ID

287 403 017

 

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Additional Information

In Campus Calendar
No
Groups

Graduate Studies

Invited Audience
Faculty/Staff, Public, Graduate students, Undergraduate students
Categories
Other/Miscellaneous
Keywords
Phd proposal
Status
  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Nov 16, 2020 - 1:05pm
  • Last Updated: Nov 16, 2020 - 1:05pm