MemGuard: Memory bandwidth reservation system for efficient performance isolation in multi-core platforms

2013 ◽  
Author(s):  
Heechul Yun ◽  
Gang Yao ◽  
R. Pellizzoni ◽  
M. Caccamo ◽  
Lui Sha
Keyword(s):  
Memory Bandwidth ◽  
Bandwidth Reservation ◽  
Performance Isolation ◽  
Efficient Performance ◽  
Reservation System

Memory Bandwidth Management for Efficient Performance Isolation in Multi-Core Platforms

2016 ◽  
Vol 65 (2) ◽  
pp. 562-576 ◽  
Author(s):  
Heechul Yun ◽  
Gang Yao ◽  
Rodolfo Pellizzoni ◽  
Marco Caccamo ◽  
Lui Sha
Keyword(s):  
Memory Bandwidth ◽  
Bandwidth Management ◽  
Performance Isolation ◽  
Efficient Performance

scholarly journals Mitigating Interference between Scientific Applications in OS-Level Virtualized Environments

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Theodora Adufu ◽  
Yoonhee Kim
Keyword(s):  
Scientific Workflows ◽  
Scientific Applications ◽  
Weighted Mean ◽  
Scientific Application ◽  
Isolation Layer ◽  
Performance Isolation ◽  
Efficient Performance ◽  
Production Environments ◽  
Scheduling Method ◽  
Computing Environments

Recent research and production environments are deploying more container technologies for the execution of HPC applications and for reproducing scientific workflows or computing environments. Research works, however, have not accounted for performance interference when executing corunning applications in containers though the absence of an efficient performance isolation layer cannot guarantee the absence of performance interference among multiple corunning applications which share resources. In this research, we propose an interference-aware scheduling method that mitigates the problem of performance interference based on applications’ I/O and CPU usage profiles. The proposed method estimates the amount of interference between various pairs of applications and coschedules them based on estimated interference. We evaluate the proposed method for both Bag-of-Tasks (BOT) scientific applications and scientific workflows and compare our method to the Weighted Mean Method. Our method improves the performance of the target scientific application by coscheduling applications with the least estimated interference ratios.

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