A cost-efficient auto-scaling mechanism for IoT applications in fog computing environment: a deep learning-based approach

Autor:	Masoumeh Etemadi, Ali Shahidinejad, Mostafa Ghobaei-Arani
Rok vydání:	2021
Předmět:	Cost efficiency Computer Networks and Communications Mechanism (biology) business.industry Computer science Distributed computing Deep learning Volume (computing) CPU time Resource (project management) Fog computing Artificial intelligence business Scaling Software
Zdroj:	Cluster Computing. 24:3277-3292
ISSN:	1573-7543 1386-7857
Popis:	The fog computing model has emerged as a viable infrastructure for serving IoT applications in recent years. In the fog ecosystem, it is essential to manage resources for different workloads due to the high volume and rapid growth of requests. Therefore, a challenge faced in this area is dynamic and efficient resource auto-scaling because fog resources must be allocated to requests efficiently. More fog resources than needed leads to “Over-Provisioning”, and fewer fog resources leads to the “Under-provisioning” issue. To this end, an effective deep learning-based resource auto-scaling mechanism has been proposed to manage the number of resources needed to handle dynamic workloads in a fog environment. The simulation results indicated that the proposed solution reduces cost, network usage, and delay violation and increases CPU utilization compared with existing resource auto-scaling mechanisms.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::cf5f13b8badcc1c2033b8e774d17bace https://doi.org/10.1007/s10586-021-03307-2 Zobrazit plný text záznamu Full text from SpringerLink