Energy aware fuzzy approach for placement and consolidation in cloud data centers

Khemili, Wided; Hajlaoui, Jalel Eddine; Omri, Mohamed Nazih

doi:10.1016/j.jpdc.2021.12.001

Cited by 17 publications

(5 citation statements)

References 45 publications

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“…To save energy and time, fuzzy logic systems have chosen the best computing system for a variety of real-world scenarios and factors. A new Fuzzy-FCA (fuzzy formal concept analysis) strategy for VNF placement was proposed by [109] depending on the FCA and the fuzzy logic in a mixed environment facilitated by CC and multiple access edge computing (MEC) architecture. Fuzzy-FCA was used in this study to group and classify the VNF obtained by FCA based on their support value; this must be set at the relevant VM and must be larger than or equal to the confidence value specified.…”

Section: Figure 8 Fuzzy Load Balancing Classificationmentioning

confidence: 99%

Fuzzy Theory in Fog Computing: Review, Taxonomy, and Open Issues

et al. 2022

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Geographically dispersed Fog Computing architecture ubiquitously connected to a range of heterogeneous nodes at the edge of the network can provide cooperative flexible, and variable computations, communications, and storage services. Several fog computing methods, models, and techniques have been used to solve cloud issues. The fuzzy theory has also been used in many aspects of fog computing. Objectives: This work presents a systematic literature review of the use of fuzzy theory in Fog Computing, highlighting the main practical motivations, classification types in research approaches, fuzzy methods used, popular evaluation tools, open issues, and future trends. Methods: The investigations were systematically performed using fuzzy theory in fog computing, and four databases which are ScienceDirect, Web of Science (WoS), Scopus, and IEEE Xplore Digital Library from 2015 to 2022, were used to analyse their performance evaluation, architecture, and applications. Results: 94 articles were selected based on fuzzy theory in fog computing using different methods, models, and techniques, based on the proposed exclusion and inclusion criteria. The results of the taxonomy were divided into five major classes: task and resource management, intrusion detection systems, trust management, and healthcare services. Discussion: Applications requiring real-time, low latency, and quick responses are well suited for fog computing. These studies show that resource sharing improves the fog computing architecture by delivering reduced latency, distributed processing, improved scalability, better security, fault tolerance, and privacy. Conclusion: The majority of the time, research areas on fuzzy theory in fog computing are crucially significant. We conclude that this review will enhance research capacity, thereby expanding and creating new research domains.

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Section: Figure 8 Fuzzy Load Balancing Classificationmentioning

confidence: 99%

Fuzzy Theory in Fog Computing: Review, Taxonomy, and Open Issues

et al. 2022

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“…As a method in knowledge discovery, application of formal concept analysis and formal context covers many research domains including computer science and other domains. In computer science, some studies were successful to apply formal concept analysis for solving some problems in many sub-domains, e.g ., datamining ( Aragón, Medina & Ramírez-Poussa, 2022 ; Hao et al, 2023 ), machine learning ( Janostik, Konecny & Krajča, 2022 ), data science ( Bazin et al, 2022 ), intelligent system ( Shao et al, 2023 ), information retrieval ( Ojeda-Hernández, López-Rodríguez & Mora, 2023 ; Khattak et al, 2021 ), natural language processing ( Marín et al, 2021 ; Jain, Seeja & Jindal, 2020 ), decision support system ( Wei et al, 2020 ), recommendation system ( Liu et al, 2022 ), semantic web ( Jindal, Seeja & Jain, 2020 ), cloud computing ( Khemili, Hajlaoui & Omri, 2022 ), data structure ( Ferré & Cellier, 2020 ), mobile application ( Kwon et al, 2021 ), software engineering ( Carbonnel et al, 2020 ), and robotic ( Zhang et al, 2023 ). In addition, some successful studies to apply formal concept analysis were in other domains, e.g ., engineering ( Rocco, Hernandez-Perdomo & Mun, 2020 ), mathematics ( Jäkel & Schmidt, 2022 ; Rocco, Hernandez-Perdomo & Mun, 2020 ), biology ( Gély et al, 2022 ), psychology ( Belohlavek & Mikula, 2022 ), medicine ( Md Saleh, Ab Ghani & Jilani, 2022 ), business ( Wajnberg et al, 2018 ; Ravi, Ravi & Prasad, 2017 ; Acharjya & Das, 2017 ), and social science ( Lang & Yao, 2023 ; Hao et al, 2021 ; Gao et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Non-redundant implicational base of formal context with constraints using SAT

Hidayat,

Ahmad,

Ngo

2024

PeerJ Computer Science

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An implicational base is knowledge extracted from a formal context. The implicational base of a formal context consists of attribute implications which are sound, complete, and non-redundant regarding to the formal context. Non-redundant means that each attribute implication in the implication base cannot be inferred from the others. However, sometimes some attribute implications in the implication base can be inferred from the others together with a prior knowledge. Regarding knowledge discovery, such attribute implications should be not considered as new knowledge and ignored from the implicational base. In other words, such attribute implications are redundant based on prior knowledge. One sort of prior knowledge is a set of constraints that restricts some attributes in data. In formal context, constraints restrict some attributes of objects in the formal context. This article proposes a method to generate non-redundant implication base of a formal context with some constraints which restricting the formal context. In this case, non-redundant implicational base means that the implicational base does not contain all attribute implications which can be inferred from the others together with information of the constraints. This article also proposes a formulation to check the redundant attribute implications and encoding the problem into satisfiability (SAT) problem such that the problem can be solved by SAT Solver, a software which can solve a SAT problem. After implementation, an experiment shows that the proposed method is able to check the redundant attribute implication and generates a non-redundant implicational base of formal context with constraints.

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“…Fog computing supports the Internet of Things and provides sensors in cloud environments. It also provides users with services from virtual machines and scalable storage spaces [18][19][20]. Cloud computing infrastructure is one model that manages cloud resources efficiently and effectively.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid arithmetic optimization algorithm and salp swarm algorithm for data placement in cloud computing

et al. 2023

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In recent years, the Internet of Things (IoT) has led to the spread of cloud computing devices in all commercial, industrial and agricultural sectors. The use of cloud computing environment services is increasing exponentially with all technology applications based on IoT. Fog computing has led to addressing issues in cloud computing environments. Fog computing reduces load balancing, processing, bandwidth, and storage as data file replication from the cloud to the network closest to sensors in different geographic locations. Traditional cloud computing leads to an increase in response time and processing time, and processing in the performance of data replication. We need replication strategies to meet users' requirements across different geographic locations while effectively harnessing fog computing capabilities to optimally select and place data replication of IoT services on cloud resources. In this strategy, the identification and mode of the data replication problem are designed as a multi-objective optimization problem that considers the heterogeneity of resources, least cost path, distance, and applications based on replication requirements. Firstly, a new hybrid metaheuristic method, using the Arithmetic Optimization Algorithm (AOA) and the salp swarm algorithm (SSA), is proposed to handle the problem of selection and placement data replication in fog computing. This strategy, called AOASSA, depends on using fog computing to optimally select and place data replication of IoT services on cloud resources. Secondly, the Floyd algorithm is used to strategy the least cost path, distance, and data transmission in different geographic locations. To validate the AOASSA strategy a set of experiments was carried out to validate the proposed strategy AOASSA. The performance of AOASSA is tested and compared with other swarm intelligence. Experiment results show the superiority of AOASSA over its competitors in terms of performance measures, such as least cost path, distance, and bandwidth.

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Energy aware fuzzy approach for placement and consolidation in cloud data centers

Cited by 17 publications

References 45 publications

Fuzzy Theory in Fog Computing: Review, Taxonomy, and Open Issues

Fuzzy Theory in Fog Computing: Review, Taxonomy, and Open Issues

Non-redundant implicational base of formal context with constraints using SAT

A novel hybrid arithmetic optimization algorithm and salp swarm algorithm for data placement in cloud computing

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