Original scientific paper

https://doi.org/10.17559/TV-20230404000503

Block Chain Technology Assisted Privacy Preserving Resource Allocation Scheme for Internet of Things Based Cloud Computing

Raja Ramamoorthi ; Department of Computer Applications, Anjalai Ammal Mahalingam Engineering College, Kovilvenni, Chennai
Arunprakash Ramasamy ; Department of Computer Science and Engineering, University College of Engineering - Ariyalur, Tamilnadu, India *

* Corresponding author.

Abstract

Resource scheduling in cloud environments is a complex task, as it involves allocating suitable resources based on Quality of Service (QoS) requirements. Existing resource allocation policies face challenges due to resource dispersion, heterogeneity, and uncertainty. In this research, the authors propose a novel approach called Quasi-Oppositional Artificial Jellyfish Optimization Algorithm (QO-AJFOA) for resource scheduling in cloud computing (CC) environments. The QO-AJFOA model aims to optimize the allocation of computing power and bandwidth resources in servers, with the goal of maximizing long-term utility. The technique combines quasi-oppositional based learning (QOBL) with traditional AJFOA. Additionally, a blockchain-assisted Smart Contract protocol is used to distribute resource allocation, ensuring agreement on wireless channel utilization. Experimental validation of the QO-AJFOA technique demonstrates its promising performance compared to recent models, as tested with varying numbers of tasks and iterations. The proposed approach addresses the challenges of resource scheduling in cloud environments and contributes to the existing literature on resource allocation policies.

Keywords

block chain; cloud computing; internet of things (IoT); resource scheduling; metaheuristics; MEC server

Hrčak ID:

309244

URI

https://hrcak.srce.hr/309244

Publication date:

25.10.2023.

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