Earticle

초록

영어

Regarding to the theories and techniques of cloud computing having been developed and applied in scientific computing field, tasks can be conveniently managed by the cloud platform on the basis of standardized scheduling system with cost (resources consumed) recorded. However, there are two issues which drag the customers’ attention: 1) When will the tasks expect for termination (response time) under a specific resource scheduling; 2) What is the best scheduling solution by considering cost. In order to reply these two questions, a Kriging based forecasting and scheduling system has been proposed in this paper. With the cooperation between the scientific designer and the cloud designer, the design variables for evaluating the cloud applications can be achieved; Kriging surrogate model is then introduced to simulate the approximate functional relationship between the design variables and the response time of the tasks; Sequential quadratic programming optimization algorithm then provides the best scheduling solution for the tasks if cost constraints are to be met. Two real scientific computing cloud applications have been testified on an OpenStack cloud platform, with consequences described in details. The work in this paper has put forward a novel way for the designers and the customers on predictable and reasonable scheduling strategies for the various resource-intensive scientific computing cloud applications with surrogate models and optimization algorithms.

목차

Abstract
 1. Introduction
 2. Related work
  2.1. Cloud Computing and Task Scheduling
  2.2. Application-Level and Resource-Intensive Scheduling Methods
 3. Research Background
  3.1. Sequential Quadratic Programming
  3.2. Kriging Surrogate Model
  3.3. Openstack Open-Source IaaS
 4. System Design
  4.1. Design Variables
  4.2. Application-Based “Sampling”
  4.3. Kriging Surrogate Model Creation
  4.5. Virtual Computing Machine Image and Virtual Computing Application
  4.6. Platform Mechanism
 5. Testing Cases
  5.1. Testing Case One
  5.2. Testing Case Two
  5.3. Results Analysis and Discussion
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Zhaojun Li [ School of Computer Science and Technology, Dalian University of Technology, Dalian City, Liaoning Province, P.R.China, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian City, Liaoning Province, P.R.China ]
• Xinyu Wang [ State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian City, Liaoning Province, P.R.China ]
• Zheng Li [ State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian City, Liaoning Province, P.R.China ]
• Xicheng Wang [ State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian City, Liaoning Province, P.R.China ]
• Keqiu Li [ School of Computer Science and Technology, Dalian University of Technology, Dalian City, Liaoning Province, P.R.China ]

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