Constrained k-means algorithm for resource allocation in mobile cloudlets

Alguliyev, Rasim; Alıguliyev, Ramiz M.; Alakbarov, Rashid

doi:10.14736/kyb-2023-1-0088

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…The research cited in reference Alexandrescu (2023), Alguliyev et al (2023), Aqib et al (2023), andJamshed et al (2023), upon which I have anchored my foundational problem statement for this entire study, has a notable limitation. Specifically, it fails to account for various operational time parameters, among others.…”

Section: Literature Surveymentioning

confidence: 95%

“…To ensure optimal resource utilization, sensors, devices, and objects often engage in resource-intensive interactions (Aqib et al, 2023). Consequently, the management of resources in fog computing demands careful consideration and implementation (Alguliyev et al, 2023). This section of the study delves into investigations that harness ML algorithms for the purpose of resource management within the domain of fog computing.…”

Section: Introductionmentioning

confidence: 99%

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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: 95%

Section: Introductionmentioning

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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Unsupervised Deep Clustering With Hard Balanced Constraint: Application in Disciplinary-Focused Student Section Formation

Chantamunee,

Thamrongrat,

Thanathamathee

et al. 2024

IEEE Access

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Training K-Means on Embedded Devices: A Deadline-Aware and Energy Efficient Design

Achira,

Slimani,

Boukhobza

2023

2023 31st International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)

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With the surge in data production, Machine Learning techniques are now commonly used to build intelligent models. Traditionally, powerful platforms process data collected from endpoint devices. However, to address security threats and minimize communication traffic, models can be learned near endpoint devices, despite their resource shortage. K-means clustering is among the most common machine learning tasks used for embedded applications. Because the system is running on scarce resources, the learning process needs to obey a certain time limit. Even if current implementations of K-means have been optimized for embedded devices, they do not consider running within a predefined time budget. In this paper, we propose a deadline-aware and energy-efficient version of K-means called Embedded K-means (EK-means) 1 , that relies on two main ideas : (1) smartly select the right subset of data to train on to meet the deadline at the expense of the smallest clustering error possible ; (2) by dropping part of the data, slack times are identified and exploited opportunistically to apply Dynamic Voltage and Frequency Scaling techniques (DVFS) so as to decrease the energy consumption of the learning task. EK-means has been built on top of an I/O optimized version of K-means for embedded devices to maintain a low I/O proportion regardless of memory constraints. EK-means allows to cluster data while meeting more than 98% of the deadlines with a loss of 1.43% of clustering quality, and an energy reduction of up to 84.26%.

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Constrained k-means algorithm for resource allocation in mobile cloudlets

Cited by 3 publications

References 29 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

Unsupervised Deep Clustering With Hard Balanced Constraint: Application in Disciplinary-Focused Student Section Formation

Training K-Means on Embedded Devices: A Deadline-Aware and Energy Efficient Design

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