Effective hotspot removal system using neural network predictor

Authors:
Sangyoon Oh;Mun-Young Kang;Sanggil Kang
Affiliations:
Department of Computer Engineering, Ajou University, Korea;Department of Computer Engineering, Ajou University, Korea;Department of Computer and Information Engineering, Inha University, Korea
Venue:
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
Year:
2013

Citing 9
Cited 0

Time Series Analysis: Forecasting and Control

Time Series Analysis: Forecasting and Control
Condor-G: A Computation Management Agent for Multi-Institutional Grids

Cluster Computing
Enterprise IT Trends and Implications for Architecture Research

HPCA '05 Proceedings of the 11th International Symposium on High-Performance Computer Architecture
Power-aware dynamic placement of HPC applications

Proceedings of the 22nd annual international conference on Supercomputing
A regression-based analytic model for capacity planning of multi-tier applications

Cluster Computing
Sandpiper: Black-box and gray-box resource management for virtual machines

Computer Networks: The International Journal of Computer and Telecommunications Networking
The impact of virtualization on network performance of amazon EC2 data center

INFOCOM'10 Proceedings of the 29th conference on Information communications
Cloud Computing Principles and Paradigms

Cloud Computing Principles and Paradigms
Distributed and Cloud Computing: From Parallel Processing to the Internet of Things

Distributed and Cloud Computing: From Parallel Processing to the Internet of Things

Quantified Score

Hi-index	0.00

Visualization

Abstract

Monitoring and prediction of resource usage are two major methods to manage distributed computing environments such as cluster, grid computing, and most recent cloud computing. In this paper, we propose a novel hotspot removal system using a neural network predictor. The proposed system detects and removes hotspots with resource specific removal algorithm. The system also improves neural network predictor by introducing prediction confidence. The effectiveness of our proposed system is verified with empirical examples, and evaluation results show that our system outperforms a popular hotspot removal system in hotspot predication and hotspot removal.