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Preference based multi-issue negotiation algorithm (PMINA) for fog resource allocation

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Abstract

Fog computing has emerged as a decentralized computing paradigm that extends cloud services to the network edge, enabling faster data processing and real-time applications. The increasing popularity of fog computing has led to the emergence of a potential market involving users and providers of fog resources. However, both parties are driven by self-interest and seek to maximize their utility, giving rise to multiple conflicts extending beyond mere price considerations. Negotiations can play a crucial role in resolving conflicts and establishing mutually beneficial service level agreements. Moreover, in the heterogeneous fog environment, quality of service attributes, such as throughput, delay, trust, power dissipation, etc., vary significantly among different user-fog associations. These attributes, although non-negotiable, hold great importance for entities and directly influence partner selection. Entities may exhibit a preference for one another based on these non-negotiable attributes. To the best of our knowledge, no existing literature specifically addresses the issue of associating with a preferred trading partner at a negotiated value for multiple issues in the fog environment. This research aims to address this gap and provide insights into this unexplored area. This work presents a novel Preference-based Muti-Issue Negotiation Algorithm, PMINA, for many to many, bilateral and concurrent negotiations in the fog environment. The results confirm the significance of PMINA, demonstrating a substantial enhancement in user and fog utilities.

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This research received no specific grant from any funding agency.

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SM conducted a literature review, designed and implemented the algorithm, and performed simulations. CKB supervised the process and contributed to manuscript review.

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Correspondence to Shaifali Malukani.

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Malukani, S., Bhensdadia, C.K. Preference based multi-issue negotiation algorithm (PMINA) for fog resource allocation. Computing (2024). https://doi.org/10.1007/s00607-024-01271-4

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