An adaptive model for resource selection and allocation in fog computing environment. (July 2019)

• Record Type: Journal Article
• Authors: Mishra, Manoj Kumar; Ray, Niranjan Kumar; Swain, Amulya Ratna; Mund, Ganga Bishnu; Mishra, Bhabani Sankar Prasad
• Is Part Of: Computers & electrical engineering. Volume 77(2019)
• Page Start: 217
• Page End: 229
• Publication Date: 2019-07
• Subjects: Fog computing -- Distributed computing -- Multi-criteria decision-making (MCDM) -- PROMETHEE-II -- Recommender systems -- Adaptive-MCDM; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2019.05.010
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Fog computing is a computing infrastructure that supports data distribution between cloud and source in an efficient manner. Building an effective and generalized model for efficient resource selection and allocation mechanism seems to be complex in fog computing. Allocation and selection mechanisms in such environment fall into a class of multi-criteria decision-making (MCDM) problems. The existing MCDM methods may not be suitable for fog computing environment as these problems are distributed, scalable and dynamic. We propose an adaptive multi-criteria decision-making (A-MCDM) model to obtain an optimal ranking of alternatives in dynamic and scalable environments. The time complexity of the proposed A-MCDM is O(nm) in general, and it takes only O(m) time to assign a rank to an alternative where n is the number of criteria and m is the number of alternatives. The performance of the proposed model has been analyzed and found to be better than other MCDM methods.