An Advanced Cloud Data Streaming Framework for Optimized Container Resource Allocation, Job Scheduling, And Security Enhancement DOI Open Access

S. Kokila

Tuijin Jishu/Journal of Propulsion Technology, Journal Year: 2023, Volume and Issue: 44(3), P. 1811 - 1819

Published: Oct. 5, 2023

In the realm of cloud data streaming, central concerns are Container resource allocation and job scheduling. Cloud infrastructure relies on container virtualization to facilitate construction migration processes. Previous models have employed techniques manage scheduling, but these come at cost increased response time network traffic. To address challenges, a novel approach is introduced, Reduced Optimal Migration model (ROM). This selectively triggers processes based recommendations, optimizing through Machine Learning (ML) Algorithm. Job scheduling enhanced dedicated Task Scheduling For robust security during migration, security-based technique implemented in 'Security-based Model,' which ensures integrity safeguards against attacks. system operates seamlessly online offline, utilizing Edge Computing. During offline periods, defensive containers maintain until owner restores connectivity. holistic framework proves highly effective resolving complex issues associated with large-scale optimization allocation, security. Empirical results confirm its efficiency enhancements. The proposed work introduces an advanced streaming that optimizes while enhancing migration. It addressing challenges inherent

Language: Английский

Using light weight container a mesh based dynamic allocation task scheduling algorithm for cloud with IoT network DOI
Santosh Shakya, Priyanka Tripathi

International Journal of Information Technology, Journal Year: 2024, Volume and Issue: 16(5), P. 2847 - 2861

Published: March 5, 2024

Language: Английский

Citations

7
A new approach for service activation management in fog computing using Cat Swarm Optimization algorithm DOI
Sayed Mohsen Hashemi, Amir Sahafi, Amir Masoud Rahmani

et al.

Computing, Journal Year: 2024, Volume and Issue: 106(11), P. 3537 - 3572

Published: July 4, 2024

Language: Английский

Citations

2
An improved black widow optimization algorithm for surfaces conversion DOI
Gang Hu, Bo Du, Xiaofeng Wang

et al.

Applied Intelligence, Journal Year: 2022, Volume and Issue: 53(6), P. 6629 - 6670

Published: July 11, 2022

Language: Английский

Citations

9
A hybrid approach for high-dimensional optimization: Combining particle swarm optimization with mechanisms in neuro-endocrine-immune systems DOI Creative Commons
Bao Liu, Mei Xu, Lei Gao

et al.

Knowledge-Based Systems, Journal Year: 2022, Volume and Issue: 253, P. 109527 - 109527

Published: Aug. 1, 2022

Particle swarm optimization (PSO) tends to fall into local optimum during the high-dimensional process To address this limitation, a hybrid approach by combining PSO with mechanisms in neuro-endocrine-immune systems (NEI-PSO) is proposed. The NEI-PSO includes nervous guidance unit, an endocrine regulation and immune orientation unit. unit are designed based on system mechanism respectively. Through joint effect of these two units, update mode particle movement changed; as result, global search ability can be improved. changes learning factor hormone law system, turn improves convergence speed proposed approach. In paper, evaluated using eight benchmark functions real-world application for non-Pieper six-axis robot. results demonstrate that has prominent advantages accuracy, ability, stability, compared some existing approaches.

Language: Английский

Citations

9
Band-Area Resource Management Platform and Accelerated Particle Swarm Optimization Algorithm for Container Deployment in Internet-of-Things Cloud DOI
Mingxue Ouyang, Jianqing Xi, Weihua Bai

et al.

IEEE Access, Journal Year: 2022, Volume and Issue: 10, P. 86844 - 86863

Published: Jan. 1, 2022

The method of building and deploying applications through the combination container virtualization technology a microservices framework has been widely used in Internet-of-Things clouds. However, there are gaps lack coordination mechanisms between cloud computing. This study constructs resource management platform, which is based on application combined with framework. platform provide support environment for construction deployment applications. no unified specification templates. Therefore, new service model called tool was designed. invocation relationship services studied, developers can combine to form function chain. container-based remains an unresolved issue. involves quality end users profit providers. To balance profits both parties, it necessary minimize response time improve utilization data center. address this problem, accelerated particle swarm optimization strategy proposed realize deployment. Through services, execution containers aggregated, so as reduce transmission overhead utilization. Compared experimental results existing strategies, significantly improved performance parameters such overhead, aggregation,

Language: Английский

Citations

9
A Privacy Preserving Federated Learning System for IoT Devices Using Blockchain and Optimization DOI Open Access

Yang Han

Journal of Computer and Communications, Journal Year: 2024, Volume and Issue: 12(09), P. 78 - 102

Published: Jan. 1, 2024

Language: Английский

Citations

1
A container deployment strategy for server clusters with different resource types DOI
Mingxue Ouyang, Jianqing Xi, Weihua Bai

et al.

Concurrency and Computation Practice and Experience, Journal Year: 2023, Volume and Issue: 35(10)

Published: March 7, 2023

Abstract The method of deploying microservices based on container technology is widely used in cloud environments. This can realize the rapid deployment and improve resource utilization datacenters. However, allocation container‐based are key issues. With continuous growth computing‐ storage‐intensive services, it necessary to consider different business types. study establishes a multi‐objective optimization problem model with similarity between containers servers, load balance clusters, reliability microservice execution as objectives. An improved artificial fish swarm algorithm proposed for microservices. comprehensive experimental results show that, compared existing strategies, matching degree server, cluster value, service reliability, other performance parameters while shortening running time algorithm. In addition, under constraint balancing, computing storage server clusters improved.

Language: Английский

Citations

2
Study of Clustering Technique Algorithms in IoT Networks DOI Open Access

Ahmed Soliman Soliman Deabes,

Michael Nasief Mikheal,

Esraa Ibraheem Eid

et al.

Deleted Journal, Journal Year: 2023, Volume and Issue: 52(4), P. 63 - 71

Published: Oct. 1, 2023

The Internet of Things (IoT) refers to a network interconnected devices that operate on the internet facilitating seamless and efficient data exchange improve human life.Energy consumption in IoT nodes is major challenge.To overcome this challenge, clustering became powerful gathering applications saves energy by organizing into clusters.The Cluster Head (CH) oversees all Member (CM) each group allowing for creation both intracluster inter-cluster connections.There are many algorithms lifespan network, increase number active nodes, extend remaining time IoT.These employ techniques such as optimization enhance efficiency overall performance network.In paper, Low Energy Adaptive Clustering Hierarchy (LEACH), Genetic Algorithm (GA), Artificial Fish Swarm (AFSA), Energy-Efficient Routing using Reinforcement Learning (EER-RL), Modified (MODLEACH) will be studied MATLAB code implemented, tested, results validated.

Language: Английский

Citations

1
Contention-aware Greedy Heuristic Method and Learning based Method for Load Balancing through Scheduling for Containers in Cloud Computing Environments DOI Creative Commons

Neelima Gogineni,

Saravanan Madderi Sivalingam

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: April 10, 2024

Abstract Containerization became indispensable in distributed environments for packaging software and dependencies a lightweight executable container. In the era of big data availability cloud infrastructure, it is more so as applications are resource data-intensive. Such High Performance Computing (HPC) deployed containerized services. However, data-intensive nature those lead to poor performance unless scheduler considers it. this paper, not only load balancing containers but also underlying considered. Towards end, scheduling algorithm with unified optimization considering application proposed. This algorithm, named Contention-aware Greedy Heuristic Scheduling Load Balancing Containers (CGHSLBC), helps improving containerization environments. Problems associated terms NP-hard. CGHSLBC has heuristics deal such issues. Empirical study revealed that better besides services infrastructure. We proposed learning based methodology schedule balance containers. It on Deep Reinforcement Learning (DRL) where state change continuously monitored while making well informed decisions. An Dynamic (RLbDS) empirical shows over art methods.

Language: Английский

Citations

0
Adaptive Scheduling Based on Intelligent Agents in Edge-Cloud Computing Environments DOI Creative Commons
JongBeom Lim

網際網路技術學刊, Journal Year: 2024, Volume and Issue: 25(4), P. 609 - 617

Published: July 31, 2024

Scheduling in cloud computing environments has been extended to support the Internet of Things (IoT) applications, which require additional quality services such as energy consumption and real-time properties. To this end, edge-cloud are prevalently deployed by encompassing fog management layer. However, traditional scheduling techniques for tasks have limited capabilities properties required IoT applications. In paper, we propose a deep learning-based dynamic technique using intelligent agents, intelligently adapt users’ requirements selective based on distributed learning environments. The proposed task method is composed two logical components: (learning distribution aggregation) intelligence multi-agents, independent each other. performance results show that self-employed agents their perform hyperparameter efficient effective

Language: Английский

Citations

0