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A Mixed-integer programming model using particle swarm optimization algorithm for resource discovery in the cloudiot
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DOI：10.1007/s12083-022-01349-w.png)
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Abstract
En 中文
Cloud computing and the Internet of Things (IoT) are new platforms in the information and communication technology revolution. Selecting Cloud of Things (CloudIoT) in applications with fixed and mobile resources can provide many opportunities in different technologies, such as healthcare and transportation. Discovering fixed and mobile resources are one of the main concerns of the CloudIoT paradigm that requires a proper discovery mechanism. This paper proposes a mathematical optimization model to minimize response time, cost, and bandwidth of CloudIoT platforms by considering fixed and mobile resources in resource discovery. Moreover, a heuristic Single Resource Discovery algorithm is presented based on a Mathematical optimization model (SRDM). Furthermore, a heuristic Multi Resource Discovery algorithm is introduced based on a Mathematical optimization model (MRDM). In addition, this paper employs Particle Swarm Optimization (PSO) and Multi-Objective Particle Swarm Optimization (MOPSO) to solve the optimization problem. Finally, according to the simulation results, the proposed MOPSO-based algorithm significantly reduces the latency and improves the success ratio and availability compared to other algorithms.
Keywords:
CloudIoT
Resource discovery
Mobility
PSO algorithm
Goal programming
Linear programming
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2.6
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2.2K
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2.9K
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