Resource Management in Cloud and Cloud-influenced Technologies for Internet of Things Applications

Jeyaraj, Rathinaraja; Balasubramaniam, Anandkumar; Kumara, Ajay; Guizani, Nadra; Paul, Anand

doi:10.1145/3571729

Cited by 32 publications

(9 citation statements)

References 128 publications

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“…The research community is gradually beginning to pay greater attention to task scheduling in fog computing, despite the fact that there is not a great deal of research on the subject. In distributed systems, the metaheuristic method is often used to schedule tasks and handle resources (Jeyaraj et al, 2023). But not much study has been done on how scheduling methods can be used in fog computing architecture.…”

Section: Literature Surveymentioning

confidence: 99%

Machine learning-driven task scheduling with dynamic K-means based clustering algorithm using fuzzy logic in FOG environment

Sheikh,

Enam,

Qureshi

2023

Front. Comput. Sci.

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Fog Computing has emerged as a pivotal technology for enabling low-latency, context-aware, and efficient computing at the edge of the network. Effective task scheduling plays a vital role in optimizing the performance of fog computing systems. Traditional task scheduling algorithms, primarily designed for centralized cloud environments, often fail to cater to the dynamic, heterogeneous, and resource-constrained nature of Fog nodes. To overcome these limitations, we introduce a sophisticated machine learning-driven methodology that adapts task allocation to the ever-changing Fog environment's conditions. Our approach amalgamates K-Means clustering algorithm enhanced with fuzzy logic, a robust unsupervised learning technique, to efficiently group Fog nodes based on their resource characteristics and workload patterns. The proposed method combines the clustering capabilities of K-means with the adaptability of fuzzy logic to dynamically allocate tasks to fog nodes. By leveraging machine learning techniques, we demonstrate how tasks can be intelligently allocated to fog nodes, resulting in reducing execution time, response time and network usage. Through extensive experiments, we showcase the effectiveness and adaptability of our proposed approach in dynamic fog environments. Clustering proves to be a time-effective method for identifying groups of jobs per virtual machine (VM) efficiently. To model and evaluate our proposed approach, we have utilized iFogSim. The simulation results affirm the effectiveness of our scheduling technique, showcasing significant enhancements in execution time reduction, minimized network utilization, and improved response time when compared to existing machine learning and non-machine learning based scheduling methods within the iFogSim framework.

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Section: Literature Surveymentioning

confidence: 99%

Machine learning-driven task scheduling with dynamic K-means based clustering algorithm using fuzzy logic in FOG environment

Sheikh,

Enam,

Qureshi

2023

Front. Comput. Sci.

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“…Work [12] further divides cloud application security engineering and implementation into two main categories: systems and software development security and software acquisition security author [13] suggests their cloud storage-specific security model. The only difference between the two is that the proposal from [14] goes into greater detail and describes the theories as well as users associated with their proof-of-concept. However, neither of the proposals [15] has any experiments, simulations, or empirical data to support its fine-grained security model's efficacy or robustness.…”

Section: Data Protection In Cloud Based Social Networkmentioning

confidence: 99%

Evaluation of soft computing in methodology for calculating information protection from parameters of its distribution in social networks

Sapra

Paikaray

Gusain

et al. 2023

Preprint

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Security of data has always been a big problem in information technology. Because the data are stored in a variety of locations, including all over the world, this problem becomes even more pressing in the context of cloud computing. Concerns about cloud technology stem primarily from users' concerns regarding data security and privacy. The heterogeneity of cloud resources and the numerous shared applications they serve can benefit from effective scheduling. Considering the quality of the service that is provided to users, this will cut costs and energy use for them. Goal of this study is to improve cloud soft computing's resource allocation and data protection using a secure channel model and machine learning architecture combined with distributed social networks. The cloud architecture data protection in the proposed network model is accomplished by developing the channel model using hierarchical lightweight cryptography analysis. Then, Q-bayes propagation quantum networks are used to allocate resources. Memory capacity, data protection analysis, throughput, end-end delay, and processing time are all used in experimental analysis.Proposed technique attained memory capacity of 73%, data protection analysis of 69%, throughput of 95%, end-end delay of 69%, processing time of 49%.

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“…With the rapid development of wireless sensor networks [ 1 ], microelectromechanical systems [ 2 ], and the Internet of Things [ 3 ], more and more demands are being placed on portable power supplies. Traditional chemical batteries cannot provide a long-term and stable power supply [ 4 ] due to their low energy density, inability to be recycled, and environmental hazards [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Improvement of the Airflow Energy Harvester Based on the New Diamagnetic Levitation Structure

et al. 2023

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This paper presents an improved solution for the airflow energy harvester based on the push–pull diamagnetic levitation structure. A four-notch rotor is adopted to eliminate the offset of the floating rotor and substantially increase the energy conversion rate. The new rotor is a centrally symmetrical-shaped magnet, which ensures that it is not subjected to cyclically varying unbalanced radial forces, thus avoiding the rotor’s offset. Considering the output voltage and power of several types of rotors, the four-notch rotor was found to be optimal. Furthermore, with the four-notch rotor, the overall average increase in axial magnetic spring stiffness is 9.666% and the average increase in maximum monostable levitation space is 1.67%, but the horizontal recovery force is reduced by 3.97%. The experimental results show that at an airflow rate of 3000 sccm, the peak voltage and rotation speed of the four-notch rotor are 2.709 V and 21,367 rpm, respectively, which are 40.80% and 5.99% higher compared to the three-notch rotor. The experimental results were consistent with the analytical simulation. Based on the improvement, the energy conversion factor of the airflow energy harvester increased to 0.127 mV/rpm, the output power increased to 138.47 mW and the energy conversion rate increased to 58.14%, while the trend of the levitation characteristics also matched the simulation results. In summary, the solution proposed in this paper significantly improves the performance of the airflow energy harvester.

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Resource Management in Cloud and Cloud-influenced Technologies for Internet of Things Applications

Cited by 32 publications

References 128 publications

Machine learning-driven task scheduling with dynamic K-means based clustering algorithm using fuzzy logic in FOG environment

Machine learning-driven task scheduling with dynamic K-means based clustering algorithm using fuzzy logic in FOG environment

Evaluation of soft computing in methodology for calculating information protection from parameters of its distribution in social networks

Improvement of the Airflow Energy Harvester Based on the New Diamagnetic Levitation Structure

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