Title
Edgify: Resource Allocation Optimization for Edge Clouds Using Stable Matching
Publication Date
4-2020
Document Type
Conference Proceeding
Abstract
As more Internet of Things (IoT) devices become increasingly ubiquitous, dynamic resource allocation for edge computing environments becomes extremely time consuming and challenging task.To overcome these challenges, edge computing environments need to dynamically scale based on the availability of accessible edge clouds within existing IoT infrastructure. In this paper, we introduce Edgify: a dynamic resource provisioning model that can effciently allocate resources across a distributed edge computing environment. We evaluate Edgify through a number of experiments that demonstrate usefulness and effectiveness of the proposed approach.
Publication Title
Companion Proceedings of the Web Conference 2020 (WWW ’20 Companion),
DOI
10.1145/3366424.3382732
Publisher Policy
No SHERPA/RoMEO policy available
Open Access Status
Licensed
Recommended Citation
Al-Masri, E., Wang, J., & Lu, Z. (2020, April). Edgify: Resource Allocation Optimization for Edge Clouds Using Stable Matching. Companion Proceedings of the Web Conference 2020 (WWW ’20 Companion),. Companion Proceedings of the Web Conference 2020 (WWW ’20 Companion), Taipei, Taiwan. https://doi.org/10.1145/3366424.3382732
