QoS-aware service composition in Fog-IoT computing using multi-population genetic algorithm

Aoudia, Idir; Kahloul, Laïd; Benharzallah, Saber; Kazar, Okba

doi:10.1109/acit50332.2020.9300090

Cited by 7 publications

(6 citation statements)

References 22 publications

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“…The authors in study [14] suggested a novel approach for QoS-aware service composition in the context of Fog-IoT computing, leveraging a multi-population genetic algorithm. In order to address the challenges associated with the architecture of IoT-cloud systems, they have adopted a five-layered architecture, with a particular emphasis on the transport layer within a Fog computing environment.…”

Section: Literature Reviewmentioning

confidence: 99%

Toward Optimal Service Composition in the Internet of Things via Cloud-Fog Integration and Improved Artificial Bee Colony Algorithm

Xiao

2024

ijacsa

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In the quest to delve deeper into the burgeoning realm of the service-oriented Internet of Things (IoT), the pressing challenge of smoothly integrating functionalities within smart objects emerges prominently. IoT devices, notorious for their resource constraints, often lean heavily on cloud infrastructures to function effectively. However, the emergence of fog computing offers a promising alternative, allowing the processing of IoT applications closer to the sensors and thereby slashing delays. This research develops a novel method for IoT service composition that leverages both fog and cloud computing, utilizing an enhanced version of the Artificial Bee Colony (ABC) algorithm to refine its convergence rate. The approach introduces a Dynamic Reduction (DR) mechanism designed to perturb dimensions innovatively. Traditionally, the ABC algorithm generates new solutions that closely mimic their parent solutions, which unfortunately slows down convergence. By initiating the process with significant dimension disparities among solutions and gradually reducing these disparities over successive iterations, this method strikes an optimal balance between exploration and exploitation through dynamic adjustment of dimension perturbation counts. Comparative analyses against contemporary methodologies reveal significant improvements: a 17% decrease in average energy consumption, a 10% boost in availability, an 8% enhancement in reliability, and a remarkable 23% reduction in average cost. Combining the strengths of fog and cloud computing with the refined ABC algorithm through the Dynamic Reduction mechanism significantly advances the efficiency and effectiveness of IoT service compositions.

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Section: Literature Reviewmentioning

confidence: 99%

Toward Optimal Service Composition in the Internet of Things via Cloud-Fog Integration and Improved Artificial Bee Colony Algorithm

Xiao

2024

ijacsa

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“…Aoudia et al [58] also address the problem of service composition to improve seven metrics concerning the quality of service. To do this, they propose to use a multi-population GA.…”

Section: Service Orchestrationmentioning

confidence: 99%

“…Likewise, they facilitate the possibility of running the search process in parallel. The work of Aoudia et al [58] proposes to use this type of approach to the problem of orchestration and composition of services.…”

Section: Extensions To Standard Gamentioning

confidence: 99%

Genetic-based optimization in Fog Computing: current trends and research opportunities

Guerrero,

Lera,

Juiz

2021

Preprint

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Fog Computing is a new computational paradigm that emerges from the need of reducing the network usage and latency in Internet of Things (IoT). Fog can be understood as a continuum between the Cloud layer and the IoT users that allows to execute applications or store/process data in the networks devices of the infrastructure. This paper aims to review current trends in the use of Genetic Algorithm for the optimization of resource management in Fog architecture. Related papers are classified concerning the optimization scope and the genetic. Finally, future research lines are also presented.

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“…Moreover, an increasing number of applications have been created by the composition of services in cloud environments. Most of the current clouds that are built on top of modern data centers incorporate Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), where endusers are billed for using the consumed service [5]. In this model, an enormous number of service requests are mapped to available services according to user requirements which, due to the proliferation of services and diversification of vendors, is quite a challenging task.…”

Section: Introductionmentioning

confidence: 99%

“…Fog is an intermediate layer between the cloud service and IoT local devices. In this architecture, service composition techniques are employed for fog dynamism in an adaptive system that fulfills user requirements by selecting available services [5].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Metaheuristics Using Rough Sets for QoS-Aware Service Composition

et al. 2022

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Quality of Service (QoS)-aware service composition plays an increasingly important role in various computational paradigms and delivery models, predominantly cloud computing. The proliferation of services with expanding quality attributes navigates this problem towards big service compositions, which fall under the umbrella of NP-hard. Within the realm of big services, performing composition also became a computationally expensive and challenging task. Since service composition is an NP-hard problem, numerous research aimed to determine optimal or near-optimal solutions within a reasonable budget. A large body of evidence suggests that metaheuristics could realize this goal to some extent. However, the proliferation of services with expanding quality attributes (search dimensions) may fail the most efficient techniques. In order to deal with the problem of big service composition, one trending approach has been devising hybrid metaheuristics methods by incorporating clustering techniques to minimize search space. This paper proposes a hybrid metaheuristic incorporated with a maximal discernibility heuristic based on rough set theory to perform composition in the subset of search space. Moreover, it introduces a parallel architecture and monitoring mechanism to provide immunity against premature convergence when the search space is minimized. The experiment was conducted for 25 datasets generated incrementally from a real-world QWS dataset, where the proposed hybrid solution effectively improves solution quality and reduces execution time with a statistical significance of 99 % confidence interval across diverse metaheuristics and datasets.

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QoS-aware service composition in Fog-IoT computing using multi-population genetic algorithm

Cited by 7 publications

References 22 publications

Toward Optimal Service Composition in the Internet of Things via Cloud-Fog Integration and Improved Artificial Bee Colony Algorithm

Toward Optimal Service Composition in the Internet of Things via Cloud-Fog Integration and Improved Artificial Bee Colony Algorithm

Genetic-based optimization in Fog Computing: current trends and research opportunities

Hybrid Metaheuristics Using Rough Sets for QoS-Aware Service Composition

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