Name
Title | ||
|---|---|---|
AutoMigrate: A Framework for Developing Intelligent, Self-Managing Cloud Services with Maximum Availability. |
Abstract | ||
|---|---|---|
Cloud services are on-demand services provided to end-users over the Internet and hosted by cloud service providers. A cloud service consists of a set of interacting applications/processes running on one or more interconnected VMs. Organizations are increasingly using cloud services as a cost-effective means for outsourcing their IT departments. However, cloud service availability is not guaranteed by cloud service providers, especially in the event of anomalous circumstances that spontaneously disrupt availability including natural disasters, power failure, and cybersecurity attacks. In this paper, we propose a framework for developing intelligent systems that can monitor and migrate cloud services to maximize their availability in case of cloud disruption. The framework connects an autonomic computing agent to the cloud to automatically migrate cloud services based on anticipated cloud disruption. The autonomic agent employs a modular design to facilitate the incorporation of different techniques for deciding when to migrate cloud services, what cloud services to migrate, and where to migrate the selected cloud services. We incorporated a virtual machine selection algorithm for deciding what cloud services to migrate that maximizes the availability of high priority services during migration under time and network bandwidth constraints. We implemented the framework and conducted experiments to evaluate the performance of the underlying techniques. Based on the experiments, the use of this framework results in less down-time due to migration, thereby leading to reduced cloud service disruption. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s10586-017-0900-x | Cluster Computing |
Keywords | DocType | Volume |
Cloud computing,Anomaly detection,Self-managing cloud services,Intelligent systems,Cloud service availability,Live migration of virtual machines | Journal | 20 |
Issue | ISSN | Citations |
3 | 1386-7857 | 4 |
PageRank | References | Authors |
0.39 | 34 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mamadou H. Diallo | 1 | 49 | 6.97 |
| Michael August | 2 | 9 | 1.40 |
| Roger Hallman | 3 | 14 | 7.73 |
| Megan Kline | 4 | 11 | 3.13 |
| Scott M. Slayback | 5 | 5 | 1.42 |