Task rescheduling optimization to minimize network resource consumption

详细信息    查看全文

• 作者：Ao Zhou ; Shangguang Wang ; Ching-Hsien Hsu ; Qibo Sun…
• 关键词：Cloud computing ; Reliability ; Big data analysis ; Resource consumption ; Task rescheduling
• 刊名：Multimedia Tools and Applications
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：75
• 期：20
• 页码：12901-12917
• 全文大小：1,505 KB
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems
  Computer Communication Networks
  Data Structures, Cryptology and Information Theory
  Special Purpose and Application-Based Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-7721
• 卷排序：75

文摘

An increasing number of big-data services are being deployed in a cloud computing environment, attracted by the on-demand service, rapid elasticity, and low maintenance costs. As a result, ensuring the quality of service has become an important research problem. Traditionally, task rescheduling is used to ensure a consistent quality of service in the event of failure of a virtual machine. However, the network resource consumption of different rescheduling methods varies. To address this problem, we propose a task rescheduling method that minimizes network resource consumption.The method includes three algorithms. The first obtains a set of good virtual machines from the large quantity of service-providing virtual machines using the skyline operation. A ranking algorithm then fuses the data size and the task emergency to identify significant tasks. Finally, we present an algorithm that automatically determines the optimal insertion point for each task. To verify the effectiveness of the proposed method, we extend the renowned simulator CloudSim and conduct a series of experiments. The results show that our method is more efficient than other methods in terms of network resource consumption.