An Effective Multi-Criteria Decision-Making Approach for Allocation of Resources in the Fog Computing Environment

Recent advances in Internet technology have shifted the focus of end-users from the usage of traditional mobile applications to the Internet of Things (IoT)-based service-oriented smart applications (SAs). These SAs use edge devices to obtain different types of Fog services and provide their real-time response to the end-users. The Fog computing environment extends its services to the edge network layer and hosts SAs that require low latency. Further, a growing number of latency-aware SAs imposes the issue of effective allocation of resources in the Fog environment. In this paper, we have proposed an effective multi-criteria decision-making (MCDM) based solution for resource ranking and resource allocation in the Fog environment. The Proposed algorithms implement the modified edition of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Analytical Hierarchical Process (AHP) and consider Quality of Experience parameters (QoE), i.e., network bandwidth, average latency, and cores for ranking and mapping of resources. The obtained results reveal that the proposed approach utilizes 70% resources, and reduces the response time by an average of 7.5s as compared to the Cloud model and the Fog model, respectively. Similarly, the completion time of the proposed framework is minimum in comparison with the cloud and Fog models with a difference of 9s and 16s.

Automated summary

This paper proposes an effective solution for resource ranking and allocation in the Fog environment based on multi-criteria decision-making. The proposed algorithms utilize the modified edition of TOPSIS and AHP and consider QoE parameters for resource mapping. The experimental results show that the proposed approach reduces the response time by an average of 7.5s compared to the Cloud model and the Fog model.