Resource scheduling approach in cloud Testing as a Service using deep reinforcement learning algorithms

P, Karthik; Sekhar, Karthik

doi:10.1049/cit2.12041

Cited by 10 publications

(3 citation statements)

References 35 publications

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“…Reinforcement learning is a form of machine learning in which an intelligent agent learns how to interact with its environment through trial and error. The agent receives rewards or punishments for its behavior from the external environment, which helps it understand what behaviors reap desired results [32,33]. Deep reinforcement learning extends reinforcement learning algorithms by using deep neural networks to approximate the strategies of an agent, thus making it possible to deal with complex high-dimensional state spaces.…”

Section: Irs Control Based On Deep Reinforcement Learningmentioning

confidence: 99%

Intelligent reflecting surface‐assisted UAV inspection system based on transfer learning

Du,

Qi,

Wang

et al. 2024

IET Communications

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Intelligent reflective surface (IRS) provides an effective solution for reconfiguring air‐to‐ground wireless channels, and intelligent agents based on reinforcement learning can dynamically adjust the reflection coefficient of IRS to adapt to changing channels. However, most exiting IRS configuration schemes based on reinforcement learning require long training time and are difficult to be industrially deployed. This paper, proposes a model‐free IRS control scheme based on reinforcement learning and adopts transfer learning to accelerate the training process. A knowledge base of the source tasks has been constructed for transfer learning, allowing accumulation of experience from different source tasks. To mitigate potential negative effects of transfer learning, quantitative analysis of task similarity through unmanned aerial vehicle (UAV) flight path is conducted. After identifying the most similar source task to the target task, parameters of the source task model are used as the initial values for the target task model to accelerate the convergence process of reinforcement learning. Simulation results demonstrate that the proposed method can increase the convergence speed of the traditional DDQN algorithm by up to 60%.

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Section: Irs Control Based On Deep Reinforcement Learningmentioning

confidence: 99%

Intelligent reflecting surface‐assisted UAV inspection system based on transfer learning

Du,

Qi,

Wang

et al. 2024

IET Communications

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“…Yet another method employing Deep reinforcement learning was applied in [7] for optimal resource scheduling in cloud. However, only the response time were focused and less concentration was made on the aspect of revenue generation.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

Hypervolume Sen Task Scheduilng and Multi Objective Deep Auto Encoder based Resource Allocation in Cloud

George

Anoop

2023

IJRITCC

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Cloud Computing (CC) environment has restructured the Information Age by empowering on demand dispensing of resources on a pay-per-use base. Resource Scheduling and allocation is an approach of ascertaining schedule on which tasks should be carried out. Owing to the heterogeneity nature of resources, scheduling of resources in CC environment is considered as an intricate task. Allocating best resource for a cloud request remains a complicated task and the issue of identifying the best resource – task pair according to user requirements is considered as an optimization issue. Therefore the main objective of the Cloud Server remains in scheduling the tasks and allocating the resources in an optimal manner. In this work an optimized task scheduled resource allocation model is designed to effectively address large numbers of task request arriving from cloud users, while maintaining enhanced Quality of Service (QoS). The cloud user task requests are mapped in an optimal manner to cloud resources. The optimization process is carried out using the proposed Multi-objective Auto-encoder Deep Neural Network-based (MA-DNN) method which is a combination of Sen’s Multi-objective functions and Auto-encoder Deep Neural Network model. First tasks scheduling is performed by applying Hypervolume-based Sen’s Multi-objective programming model. With this, multi-objective optimization (i.e., optimization of cost and time during the scheduling of tasks) is performed by means of Hypervolume-based Sen’s Multi-objective programming. Second, Auto-encoder Deep Neural Network-based Resource allocation is performed with the scheduled tasks that in turn allocate the resources by utilizing Jensen–Shannon divergence function. The Jensen–Shannon divergence function has the advantage of minimizing the energy consumption that only with higher divergence results, mapping is performed, therefore improving the energy consumption to a greater extent. Finally, mapping tasks with the corresponding resources using Kronecker Delta function improves the makespan significantly. To show the efficiency of Multi-objective Auto-encoder Deep Neural Network-based (MA-DNN) cloud time scheduling and optimization between tasks and resources in the CC environment, we also perform thorough experiments on the basis of realistic traces derived from Personal Cloud Datasets. The experimental results show that compared with RAA-PI-NSGAII and DRL, MA-DNN not only significantly accelerates the task scheduling efficiency, task scheduling time but also reduces the energy usage and makespan considerably.

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“…It is found the development of efficient artificial intelligence-based LBS is still an open area of research. e used machine learning models such as SVM, RF, J48, and ANN suffer from the overfitting problem [14,15]. Besides this, the predicted LBS further need to be optimized using optimization techniques [16,17].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Metaheuristic-Based Location-Based Advertising

Rohilla

Chakraborty²,

Kaur

2022

Scientific Programming

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Location-based services (LBS) are necessary for obtaining the important details since the user needs vary based on the location. Location-based advertising (LBA) are utilized for abandoning the user location and to offer assistance by using the obtained information. Therefore, an efficient machine learning and metaheuristic-based model referred as GANM is designed for LBS. Initially, the potential location information is evaluated utilizing the geographic information system (GIS). Thereafter, significant features are computed using the location data. Obtained features are then segmented to improve the process of LBS. Adaptive network-based fuzzy inference system (ANFIS) is then utilized to efficiently classify the user data. Finally, the optimization of classified documents is achieved using the nondominated sorting genetic algorithm-III (NSGA-III). Extensive experiments are performed to validate GANM for LBS. Comparative analyses reveal that GANM outperforms the competitive LBS models in terms of F-score, accuracy, sensitivity, specificity, and area under curve by 2.2734%, 2.3981%, 2.3947%, 2.4271%, and 2.1638%, respectively.

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Resource scheduling approach in cloud Testing as a Service using deep reinforcement learning algorithms

Cited by 10 publications

References 35 publications

Intelligent reflecting surface‐assisted UAV inspection system based on transfer learning

Intelligent reflecting surface‐assisted UAV inspection system based on transfer learning

Hypervolume Sen Task Scheduilng and Multi Objective Deep Auto Encoder based Resource Allocation in Cloud

Artificial Intelligence and Metaheuristic-Based Location-Based Advertising

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