CAVMS: Application-Aware Cloudlet Adaption and VM Selection Framework for Multicloudlet Environment

The mobile users offload the application to nearby cloudlet servers instead of the remote cloud for better end-user experience. Each cloudlet is able to process real-time applications with the help of virtual machines (VM). While multiple applications running on the cloudlet, the possibility of over...

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Bibliographic Details
Published in:IEEE systems journal 2021-12, Vol.15 (4), p.5098-5106
Main Authors: Ramasubbareddy, Somula, Ramasamy, Sasikala, Sahoo, Kshira Sagar, Kumar, R. Lakshmana, Pham, Quoc-Viet, Dao, Nhu-Ngoc
Format: Article
Language:English
Subjects:
Base stations
Cloud computing
Computation offloading
Delays
Electronic devices
Load balancing
Mobile applications
mobile cloud computing (MCC)
multicloudlet
Response time
Servers
Time factors
User experience
Virtual environments
virtual machine (VM) migration
Virtual machining
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Summary:The mobile users offload the application to nearby cloudlet servers instead of the remote cloud for better end-user experience. Each cloudlet is able to process real-time applications with the help of virtual machines (VM). While multiple applications running on the cloudlet, the possibility of overprovisioning issue is unavoidable due to massive task-offloading requests from mobile devices. In this regard, balancing the load, among the cloudlets in a high-interactive applications scenario, is a promising issue. In order to balance the cloudlet load, migration of VMs from an overloaded cloudlet to an underloaded cloudlet is a favored solution. During this process, a well-designed migration mechanism must be outlined that can perform two steps such as VM selection and cloudlet adaption. In this article, an application-aware cloudlet adaption and VM selection framework has been devised for balancing the load in a multicloudlet environment. The candidate-cloudlet adaption is based on a migration efficiency indicator that reduces the response time and enhances load-balancing rate. Furthermore, the effectiveness of the framework has been evaluated by comparing with other state-of-the-art cloudlet-selection strategies.
ISSN:1932-8184
1937-9234
DOI:10.1109/JSYST.2020.3029807