Toward Predictable Cloud Systems

Event Sponsor: 
Mathematics and Computer Science Seminar
Start Date: 
Feb 16 2018 - 10:30am
Building 240/Room 1404
Argonne National Laboratory
In Kee Kim
Speaker(s) Title: 
University of Virginia
Raj Kettimuthu

Clouds have become an attractive infrastructure for high performance and scientific computing because the clouds offer cost efficiency, scalability, and elasticity of on-demand resources. Predictive resource management is developed to efficiently leverage cloud resources with two interrelated goals: ensuring application performance and minimizing execution cost. However, existing approaches are not sufficient to meet these two goals due to uncertainties in the clouds -- workload and performance uncertainties -- resulting in poor performance and adaptability in the cloud resource management.

My presentation introduces two techniques that mitigate such uncertainties for predictive resource management. I will first present a novel workload prediction framework called "CloudInsight" that leverages a combined power of multiple workload predictors. Next, I will focus on "Orchestra" framework, which ensures the performance goals of multiple cloud applications with dynamic allocation of shared resources in the user space. Then, I will conclude this talk with my vision for future research.

Miscellaneous Information: 

BlueJeans Meeting URL: / Meeting ID: 161 227 608 / Participant Passcode: 7465

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