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A Hybrid AI-Driven Swarm Approach for Optimized Task Scheduling in cloud computing Environment 5

A Hybrid AI-Driven Swarm Approach for Optimized Task Schedul...

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5th international conference on Sustainable communication Networks and Application, ICSCNA 2024

作者： Kaur, Surinder Singh, Jaspreet Bharti, Vishal University Institute of Engineering Chandigarh University Department of Computer Science and Engineering Punjab Mohali India Department of Computer Science and Engineering Ambala India

ISBN: (纸本)9798331530013

The increasing number of internet users turning to modern cloud computing in the coming years presents challenges, especially when considering constraints such as limited completion times, optimal resource utilization, low operational costs, and short task scheduling durations. Task scheduling becomes more complex as the number of tasks and available resources grow, leading to a rapid increase in potential solutions and combinations, making the problem harder to solve. To address this, the paper proposes an Enhanced Task Scheduling (ETS) model driven by optimized artificial intelligence, incorporating swarm-based methods in cloud computing environments. The proposed method uses an Artificial Neural Network (ANN) with swarm-based optimization, specifically Moth Flame Optimization (MFO), to balance scheduling by classifying Virtual Machines (VMs) based on CPU utilization, memory, and bandwidth. Initially, tasks are scheduled conventionally, then the ETS model is optimized using ANN with the Back-Propagation Algorithm (ANN-BPA) to address resource allocation and balancing challenges. The study also introduces an alternative model using only ANN to evaluate the ETS model's efficiency, focusing on task allocation, completion, execution time, and energy consumption. The ANN-BP A-based model outperforms existing techniques and simple ANN models, increasing resource utilization by 7.54% and reducing task execution time by 0.6 seconds. © 2024 IEEE.

关键词： Premixed flames

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BACOA: Meta Heuristic Driven Hybrid Scheduling Algorithm for Improved Resource Allocation in cloud Environment

BACOA: Meta Heuristic Driven Hybrid Scheduling Algorithm for...

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2025 international conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Naveen, Yerukala Kasturi, Srinivasa Babu Ramya, Ch. Gayathri, Bandari Medishetti, Santhosh Kumar Mlr Institute of Technology T.S. Hyderabad India Nalla Narasimha Reddy Education Society's Group of Institutions T.S. Hyderabad India Cmr College of Engineering and Technology T.S. Hyderabad India

ISBN: (纸本)9798331509828

Energy-aware scheduling is a critical aspect of optimizing task execution in cloud computing environments, where efficient resource management can significantly reduce operational costs. This paper introduces the Butterfly Ant Colony Optimization Algorithm (BACOA), a novel hybrid approach designed to enhance local and global search capabilities. BACOA combines the exploratory strength of the Butterfly Optimization Algorithm (BOA) and the exploitation capabilities of Ant Colony Optimization (ACO) to minimize energy consumption, communication cost, and computation cost in Task Scheduling (TS). By balancing these parameters, BACOA achieves a more energy-efficient task scheduling process while reducing the overhead costs associated with communication and computation in cloud systems. Simulation results demonstrate that BACOA outperforms existing algorithms, achieving superior energy savings by 16%, and reduced communication cost and computation cost by 21.06% and 18% respectively, the proposed algorithm making it ideal for scalable cloud infrastructures. © 2025 IEEE.

关键词： Ant colony optimization

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Multi-Key Privacy-Preserving Training and Classification using Supervised Machine Learning Techniques in cloud computing 2

Multi-Key Privacy-Preserving Training and Classification usi...

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2nd IEEE international conference on Advances in computing, communication and Applied Informatics, ACCAI 2023

作者： Kishore, R. Hari Chandra Sekhar, A. Patro, Pramoda Swain, Debabrata Gitam University Gitam Institute of Science Department of Mathematics Andhra Pradesh Visakhapatnam530045 India Koneru Lakshmaiah Education Foundation Department of Engineering Mathematics Telengana Hyderabad500075 India Pandit Deendayal Energ University School of Technology Department of Computer Science and Engineering Gujarat Gandhinagar382421 India

ISBN: (纸本)9798350315905

cloud computing contains lots of processing power and storage. cloud computing and machine learning (ML) techniques enable large-scale data processing. The enhanced ML-based categorization technique is established in the cloud. However, there is a risk of privacy leaking of training data in the data processing. The computational and communication costs of the information possessor(s) must be maintained to a minimum. This study suggests a multi-key enhanced support vector machine (MK-FHE) and multi-key fully homomorphic encryption (MK-FHE) supervised machine learning method for encrypted data (ESVM).The results suggest that MK-FHE protects data privacy and is more effective in processing. © 2023 IEEE.

关键词： Support vector machines

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Review of Task Scheduling based on Different Parameters in cloud Environments

Review of Task Scheduling based on Different Parameters in C...

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2023 international conference on Intelligent Data communication Technologies and Internet of Things, IDCIoT 2023

作者： Pradeep, K. Sharma, Kshitij Edara, Jishnu Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665474511

In IoT applications where connectivity to cloud-based systems is irregular yet low latency is a crucial requirement, fog computing is necessary for success. Fog computing's dispersed functionality enables storage and execution to take place in totally separate places. Resource management and multiple programming paradigms are required due to the distributed capabilities and numerous user applications taken together. It is important to manage and evaluate failures in network-based systems and services caused by a variety of limitations. The study's main goal is to shed light on various QoS-based scheduling strategies that aim to improve execution in fog computing to evaluate several fault-tolerant methods used in the fog computing environment. © 2023 IEEE.

关键词： Fog computing

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Edge of Innovation: Exploring the Potential of Edge computing and Content Delivery Networks 5

Edge of Innovation: Exploring the Potential of Edge Computin...

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5th IEEE international conference on computing, Power, and communication Technologies, IC2PCT 2024

作者： Dash, Yajnaseni Kumar, Naween Abraham, Ajith Bennett University School of Computer Science Engineering and Technology Uttar Pradesh India

ISBN: (纸本)9798350383522

The digital landscape has changed in recent years due to the rise in popularity of edge computing and Content Delivery Networks (CDNs), which offer faster and more effective internet access. Edge computing, a solution to cloud computing constraints in the Internet of Things (IoT), offers scalability and smarter service delivery, but also faces challenges like power distribution and privacy protection. Conversely, CDNs enhance network performance by distributing content among cache servers near users, resulting in faster, more reliable apps and services, acting as reliable overlay networks. Edge computing and CDNs are becoming mainstream tech solutions that may improve many applications. This paper examines the advancements in both edge computing and CDNs across various disciplines, highlighting their revolutionary potential on the internet. It also investigates the intricate developments in these technologies, emphasizing their role in creating a networked world. © 2024 IEEE.

关键词： Content Delivery Networks Edge computing Internet of Things (IoT) IoT networks

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Optimizing Patient Satisfaction Surveys and Feedback with cloud computing and NLP Techniques 8

Optimizing Patient Satisfaction Surveys and Feedback with Cl...

引用

8th international conference on I-SMAC (IoT in Social, Mobile, Analytics and cloud), I-SMAC 2024

作者： Jayanthi, L.N. Shanmugam, Kannan Suma, Challapalli Sagar, B.S. Maniraj, S.P. Ganeshbabu, T.R. Saveetha College of Liberal Arts and Sciences Department of Commerce Tamil Nadu Chennai India Vit Bhopal University School of Computing Science and Engineering Department of Gaming Technology Madhapradesh Bhopal India Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Reva University Department of Electrical and Electronics Engineering Karnataka Bengaluru India Faculty of Engineering & Technology Department of Data Science and Business Systems Chennai Tamil Nadu Kattankulathur India Department of Electronics and Communication Tamil Nadu Namakkal India

ISBN: (纸本)9798350376425

Healthcare quality assessment and identification of opportunities for improvement are crucially facilitated by patient satisfaction surveys. Traditional approaches sometimes face difficulties in handling substantial amounts of written comments and obtaining real useful data. It presents a new method that combines cloud computing with Natural Language Processing (NLP) approaches to enhance the analysis of patient satisfaction data. By using cloud platforms, it can effectively manage large datasets and provide scalable, cost-efficient processing. The use of NLP methods, such as text mining and sentiment analysis, enables the conversion of unstructured input into structured insights, thereby uncovering the fundamental patient feelings and patterns. This connection enables the analysis of data in real-time also improving the accuracy of interpreting feedback and facilitating fast enhancements in healthcare services. Future research will prioritise the improvement of NLP algorithms and the investigation of supplementary cloud-based technologies to enhance the accuracy and efficiency of patient satisfaction analysis to improved patient care and experience. © 2024 IEEE.

关键词： Sentiment analysis

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Optimization of cloud-Native Application Execution over the Edge-cloud Continuum Enabled by DVFS 15

Optimization of Cloud-Native Application Execution over the ...

引用

15th international conference on cloud computing and Services Science, CLOSER 2025

作者： Kontos, Georgios Soumplis, Polyzois Varvarigos, Emmanouel National Technical University of Athens Athens Greece Institute of Communication and Computer Systems Athens Greece

ISBN: (纸本)9789897587474

Microservice-based application architecture, despite its many merits - including enhanced flexibility, scalability and robustness-, adds significant complexity to the application’s orchestration process. Complex execution paths emerge during runtime as the demands traverse the application’s graph within an edge-cloud topology. In this work, we leverage Dynamic Voltage and Frequency Scaling (DVFS) combined with the application’s structure-represented as a Directed Acyclic Graph (DAG)-to determine optimal configuration for each service. Our goal is to perform assignments that optimize the weighted combination of the application’s execution time (i.e., the resulting critical path’s length) and the total energy consumption, subject to node capacity and power constraints, the communication limits of the microservices, and the different frequency levels of the processing units. The problem is initially modeled as a Mixed Integer Linear Problem (MILP). To tackle its complexity, we segregate the problem into two closely related subproblems. The first is addressed by a genetic algorithm, while a best-fit heuristic algorithm obtains the final solution, leveraging the genetics’ decisions. Extensive simulations demonstrate the efficiency of the proposed mechanism by contrasting its results with two baseline policies, while highlighting the inherent trade-offs between performance and energy consumption. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Genetic algorithms

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Application and optimization of networked battery maintenance mode in power system

Application and optimization of networked battery maintenanc...

引用

2024 international conference on Physics, Photonics, and Optical engineering, ICPPOE 2024

作者： Duan, Xuesong Zhang, Jinjia Li, Yuhao Li, Hongchao Wang, Wei State Grid Henan Electric Power Company State Grid Zhenping County Power Supply Company 474250 China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

Traditional battery maintenance methods have some problems, such as low efficiency, difficult to find potential problems in time and accurately, which affect the stability and reliability of power system. This paper studies the application and optimization of networked battery maintenance mode in power system. By integrating sensor technology, network communication and data analysis, this mode realizes real-time monitoring and maintenance automation of battery state. Aiming at the challenges of lack of real-time and accuracy, optimization strategies such as deploying 5G base stations, adopting advanced algorithms and machine learning technologies are proposed. In addition, a big data analysis platform based on cloud computing is built, and machine learning and data mining are used to predict battery performance decline, and intelligent maintenance algorithms are developed. Experiments show that this mode improves voltage stability, capacity retention and internal resistance control, reduces maintenance cost and improves efficiency. These achievements provide theoretical and practical guidance for the stable operation of power system and efficient maintenance of battery. © 2025 SPIE.

关键词： Batteries Data transmission Resistance Data analysis Data modeling Machine learning Telecommunications cloud computing Decision making Reliability

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Optimizing Resource Allocation in cloud Environments Using Reinforcement Learning

Optimizing Resource Allocation in Cloud Environments Using R...

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2024 international conference on Innovative computing, Intelligent communication and Smart Electrical Systems, ICSES 2024

作者： Malathi, P. Dilipkumar, E. Rajasubha, J. Yokesh, A.S. Priya, L. Basha, H. Anwer Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Department of Computer Science Tamil Nadu Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Tamil Nadu Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331543617

Traditional cloud resource allocation techniques, which rely on static, rule-based systems, are inefficient and lead to greater costs and lower performance due to issues like over- and under-provisioning. The study presents a novel way to dynamically optimize resource allocation in real-time using reinforcement learning (RL) algorithms, such as Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Actor-Critic approaches. The RL-based system overcomes the limitations of traditional techniques by adjusting to shifting workloads and learning from the past. The average resource usage efficiencies of DQN, PPO, and Actor-Critic are 85.7%, 88.1%, and 87.4%, respectively, compared to 72.3% for traditional systems. The results demonstrate that RL approaches perform much better than traditional systems. The RL techniques also enhanced system performance and decreased expenses by as much as 29.4%;PPO had the highest results in response time (270 ms), throughput (190 requests/second), and user satisfaction (88%). © 2024 IEEE.

关键词： cloud platforms

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Mobile-based cloud computing with Autonomous Assets Supply 4

Mobile-based Cloud Computing with Autonomous Assets Supply

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4th international conference on Electronics and Sustainable communication Systems, ICESC 2023

作者： Shadaksharappa, B. Maruthu Kannan, B. Yuvaraj, S. Maniraj, S.P. Sri Sairam College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru India Sphoorthy Engineering College Nadergul Department of Computer Science and Engineering Telangana Hyderabad India Srm Institute of Science and Technology Kattankulathur Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350300093

The overwhelming rise in computing and networking skills makes Mobile platforms as predominant means of Internet access. Specific portable apps are unusable due to the inadequate computing capacities of mobile platforms. The constrained conne ctivity and reaction times involved in offloading data into multiple servers, such as a cloud data center, make permitting applications needing real-time information collection and processing on mobile stages are very challenging. The hybrid statics/mobile computing network is the latest supply system which has the heterogeneous ability for sensing, computing, and networking with static/mobile devices nearby. This robust computing network can allow creative mobile information and calculation-intensive applications, including omnipresent context-aware health and wellbeing checks of older adults, remote precipitation and flood risk evaluation, shared object detection and monitoring, and shared multimedia content searching and sharing. A detailed experimental study is provided to validate and demonstrate the advantages of the autonomous capability of the proposed system. © 2023 IEEE.

关键词： cloud computing

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