Earticle

초록

영어

To have high performance scheduling mechanisms in grid computing, we need accurate methods for estimating parameters like jobs' wait time and run time. In this paper, we consider wait time prediction problem. Different regression techniques are examined on AuverGrid data set to predict wait time. To improve the quality of prediction, some extra features are proposed. Simulation results show that adding these features reduces prediction error between 13% and 60% in different methods. Results also show that K-nearest neighbor outperforms other regression techniques. We have compared the k-nearest neighbor method in both original and enriched data set with Last-M. K-Nearest neighbor in enriched data set outperforms both Last-M and K-nearest neighbor in original data set in accuracy and perfect prediction percentage.

목차

Abstract 
 1. Introduction 
 2. AuverGrid 
 3. Wait Time Prediction 
  3.1. An Overview of Used Learning Methods
  3.2. Proposed Features 
 4. Results 
  4.1. Examining The Effect of Proposed Features
  4.2. Nearest Neighbor Versus Time Series Predictor 
 5. Conclusion 
 References

키워드

저자

• Somayeh Kianpisheh [ Faculty of Electrical and Computer Engineering, arbiat Modares University, Tehran, Iran ]
• Saeed Jalili [ Faculty of Electrical and Computer Engineering, arbiat Modares University, Tehran, Iran ]
• Nasrolah Moghadam Charkari [ Faculty of Electrical and Computer Engineering, arbiat Modares University, Tehran, Iran ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605