Welcome to the InfoSci Platform
Reference Hub1
Self-Management of Operational Issues for Grid Computing: The Case of the Virtual Imaging Platform

Self-Management of Operational Issues for Grid Computing: The Case of the Virtual Imaging Platform

Rafael Ferreira da Silva, Tristan Glatard, Frédéric Desprez
Copyright: © 2015 |Pages: 35
ISBN13: 9781466682139|ISBN10: 1466682132|EISBN13: 9781466682146
DOI: 10.4018/978-1-4666-8213-9.ch006
Cite Chapter Cite Chapter

MLA

Ferreira da Silva, Rafael, et al. "Self-Management of Operational Issues for Grid Computing: The Case of the Virtual Imaging Platform." Emerging Research in Cloud Distributed Computing Systems, edited by Susmit Bagchi, IGI Global, 2015, pp. 187-221. https://doi.org/10.4018/978-1-4666-8213-9.ch006

APA

Ferreira da Silva, R., Glatard, T., & Desprez, F. (2015). Self-Management of Operational Issues for Grid Computing: The Case of the Virtual Imaging Platform. In S. Bagchi (Ed.), Emerging Research in Cloud Distributed Computing Systems (pp. 187-221). IGI Global. https://doi.org/10.4018/978-1-4666-8213-9.ch006

Chicago

Ferreira da Silva, Rafael, Tristan Glatard, and Frédéric Desprez. "Self-Management of Operational Issues for Grid Computing: The Case of the Virtual Imaging Platform." In Emerging Research in Cloud Distributed Computing Systems, edited by Susmit Bagchi, 187-221. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-8213-9.ch006

Export Reference

Mendeley
Favorite

Abstract

Science gateways, such as the Virtual Imaging Platform (VIP), enable transparent access to distributed computing and storage resources for scientific computations. However, their large scale and the number of middleware systems involved in these gateways lead to many errors and faults. This chapter addresses the autonomic management of workflow executions on science gateways in an online and non-clairvoyant environment, where the platform workload, task costs, and resource characteristics are unknown and not stationary. The chapter describes a general self-management process based on the MAPE-K loop (Monitoring, Analysis, Planning, Execution, and Knowledge) to cope with operational incidents of workflow executions. Then, this process is applied to handle late task executions, task granularities, and unfairness among workflow executions. Experimental results show how the approach achieves a fair quality of service by using control loops that constantly perform online monitoring, analysis, and execution of a set of curative actions.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.