Dynamic Resource Allocation Using an Adaptive Multi-Objective Teaching-Learning Based Optimization Algorithm in Cloud

Moazeni, Ali; Khorsand, Reihaneh; Ramezanpour, Mohammadreza

doi:10.1109/access.2023.3247639

Cited by 8 publications

(4 citation statements)

References 43 publications

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“…The dynamic resource allocation procedure presented in Moazeni et al 27 includes adaptive multiobjective teaching–learning‐based optimization (AMO‐TLBO) to enhance the exploitation and exploration characteristics in cloud computing resource allocation. The presented model additionally includes a grid‐based process that assesses the non‐dominated solutions and minimizes the makespan compared to the conventional TLBO model.…”

Section: Related Workmentioning

confidence: 99%

An improved resource scheduling strategy through concatenated deep learning model for edge computing IoT networks

Vijayasekaran,

Duraipandian

2024

Int J Communication

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SummaryWith increasing challenges and research in edge‐assisted IoT models, an improved resource scheduling approach exploiting deep learning concepts is proposed in this research work. Improvement in performance in the proposed work is achieved primarily by addressing the response time and waiting time. This could be achieved if the optimal resources are scheduled without any delay. The presented concatenated deep learning technique considers the time series IoT network source requirements and allocates optimal resources from the resource pool, considering resource availability, workload, and computation time. Two deep learning techniques, namely, CNN and GRU, are utilized for the concatenation process, while resource characteristics are considered as features that are extracted and classified to schedule optimal resources. Novelty in the proposed work is exhibited in the form of the concatenation process proposed. The proposed resource scheduling performance metrics are compared with the performance of the existing scheduling model through simulation analysis for better validation. The proposed model selects the optimal resources from the resource pool using concatenated features and schedules for respective requests with minimum delay and waiting time, which increases the overall efficiency of the edge computing IoT networks.

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Section: Related Workmentioning

confidence: 99%

An improved resource scheduling strategy through concatenated deep learning model for edge computing IoT networks

Vijayasekaran,

Duraipandian

2024

Int J Communication

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“…An adaptive multi-objective teaching-learning-based optimization (AMO-TLBO) technique is used to allocate cloud computing resources dynamically. 24 This system connects changing customer expectations to service infrastructure. AMO-TLBO improves exploration and exploitation skills through instructor quantity, adaptive instruction, tutorial training, and self-motivated learning.…”

Section: Secure and Qos Aware Workflow Executionmentioning

confidence: 99%

“…The authors 24 present a dynamic resource allocation technique in cloud computing that uses an adaptive multi‐objective teaching‐learning‐based optimization (AMO‐TLBO) algorithm to bridge the gap between constantly changing client requirements and available infrastructure for the services. AMO‐TLBO provides multiple teachers, adaptive teaching variables, tutorial training, and self‐motivated learning to improve exploration and exploitation capacities.…”

Section: Review Of Literaturementioning

confidence: 99%

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Transforming data‐intensive workflows: A cutting‐edge multi‐layer security and quality aware security framework

Aljohani

2024

Concurrency and Computation

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SummaryThe data‐intensive workflow application executes tasks on edge servers and cloud platforms in a heterogeneous big‐data computing environment. Cloud and edge servers are vulnerable to node attacks and malicious links due to their wireless connections. Thus, detecting and mitigating rogue nodes in edge server communication environments during workflow execution is crucial. In today's workflow execution landscape, there is a rising emphasis on resolving Quality of Service (QoS) and security concerns within homogeneous execution settings. Workflow execution on diverse computing platforms has received limited research. We are developing an edge‐cloud‐specific Multi‐Layer Security and Quality‐Aware (MLSQA) framework to solve research issues in this area. Node attacks are detected via reputation‐based security features in the MLSQA framework, whereas link attacks are detected using machine learning. A unique trade‐off metrics technique optimizes workflow security and QoS parameters, reducing energy usage and makespan. In addition, a fault‐tolerant, energy‐efficient job offloading technique is described in detail to improve the system's resilience to errors. The experiment is conducted using complex (i.e., memory and CPU intensive) scientific procedures with intermediate job dependencies, namely Inspiral workflow. The MLSQA framework excels in threat detection, demonstrating lower false alarms, reduced energy consumption, and quicker implementation than the recent secure workflow execution architecture.

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QoS and reliability aware matched bald eagle task scheduling framework based on IoT-cloud in educational applications

Chowdhary,

Rao

2024

Cluster Comput

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Dynamic Resource Allocation Using an Adaptive Multi-Objective Teaching-Learning Based Optimization Algorithm in Cloud

Cited by 8 publications

References 43 publications

An improved resource scheduling strategy through concatenated deep learning model for edge computing IoT networks

An improved resource scheduling strategy through concatenated deep learning model for edge computing IoT networks

Transforming data‐intensive workflows: A cutting‐edge multi‐layer security and quality aware security framework

QoS and reliability aware matched bald eagle task scheduling framework based on IoT-cloud in educational applications

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