Autonomic fine-grained replication and migration at component level on multicloud
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- 作者：Linh Manh Pham ; Tuan-Minh Pham
- 关键词：Autonomic computing ; Cloud computing ; Application migration ; Application replication ; Network virtualization
- 刊名：Vietnam Journal of Computer Science
- 出版年：2017
- 出版时间：February 2017
- 年：2017
- 卷：4
- 期：1
- 页码：39-49
- 全文大小：852KB
- 刊物类别：Information Systems and Communication Service; Artificial Intelligence (incl. Robotics); Computer Ap
- 刊物主题：Information Systems and Communication Service; Artificial Intelligence (incl. Robotics); Computer Applications; e-Commerce/e-business; Computer Systems Organization and Communication Networks; Computa
- 出版者：Springer Berlin Heidelberg
- ISSN：2196-8896
- 卷排序：4
文摘
Although migration and replication of applications in a distributed environment have been discussed by many researchers, the implementations of these features are rarely focused when deployed in the cloud. The cloud enterprises usually have to migrate or replicate partly or fully their services because of economical or disaster preventing reasons. Because the cost of copying the whole virtual machines is too high due to their big size, the replication at application level is a possible approach. This work proposes an autonomic replication and migration mechanism integrated in an implementation of a fine-grained deployment framework which enables ability to migrate and replicate-service components on the clouds. We formulate the deployment problem of replicated components to optimize the system performance as a quadratic program. Our proposed framework ensures the high availability and scalability of services, and complies with the service-oriented architecture. Our experiments conducted in real scenarios of elastic demands demonstrate that the proposed fine-grained migration and replication is more efficient than the coarse-grained ones when an autonomic system responds to fluctuation of webapp’s workload. We also show the influence of adding servers and upgrading server connections on the system performance.