.png){kind=icon}
.png){kind=icon}
Return
Fog-Integrated Cloud Architecture enabled multi-attribute combinatorial reverse auctioning framework
.png){kind=icon}
PRE
AI
DOI：10.1016/j.simpat.2021.102307.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Fog computing is an emerging service-oriented market in conjunction with Cloud computing to fulfill the resource demand of mobile users as well as IoT users for real-time applications. Auctioning in Fog computing is highly challenging due to mobility, dynamic pricing, real-time demand in comparison to Cloud based auctioning models. Further, due to users? mobility and limited Fog resources, existing reverse auction techniques developed for Cloud computing model cannot directly be applied for the resource procurement in Fog-Integrated Cloud Architecture (FICA). Therefore, a reverse auction-based model which includes customer, auctioneer, Fog provider, Cloud provider, and Fog & Cloud provider together as auction participants, is proposed in this work. The proposed model, for resource provisioning using a multi-attribute combinatorial reverse auction, is named as Fog-Integrated Cloud Auctioning Model (FICAM). FICAM pricing scheme includes three types of resources depending on their requirement i.e., local Fog, remote Fog, and Cloud. A truthful, robust, and fair algorithm for resource allocation is proposed considering response time, data source mobility requirements, and Fog resource limitations. To encourage providers to bid truthfully, the Vickrey model is extended. FICAM also introduces a new algorithm for resource procurement in which instead of giving all resources of the bundle, only the required resources at a time are given to the customer with the bundle discount. The discount is based on a certain threshold in the ratio of the availed amount of resources to the offered amount of resources. Rigorous experimentation exhibits that the proposed model offers a low resource procurement cost in polynomial time as compared to other state of the art algorithms.
Keywords:
Fog computing
Resource procurement
Reverse auction
Fog service provider
Internet of Things (IoT)
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.6
Papers:
2.6K
Citations:
4.8K
View more organizations