Automatic Dosing System for Slime Water Sedimentation Process Based on Intelligent Optimization Algorithm DOI

Jingdan Sun,

Tao Li

Published: Dec. 16, 2022

Coal slime water treatment is a non-linear, strong coupling, large lag process, and it difficult to establish an accurate model. The model established by the existing research adopts simple linear relationship or empirical formula, dosage of drug fixed inaccurate, resulting in inaccuracy drug. Improper use waste will affect processing rate system. Therefore, according characteristics coal this paper studies automatic dosing system settlement process based on intelligent optimization algorithm. In paper, Lssvm prediction concentration established, multi-objective particle swarm algorithm (MOPSO) used optimize chemical agent. On basis ensuring effect carbon-dyed water, consumption agents reduced. Experiments show that LSTM has highest overall accuracy for flocculant prediction, P reaches 89.88%. compared with BP neural network RNN, more suitable flocculation amount paper.

Language: Английский

Dynamic task scheduling in edge cloud systems using deep recurrent neural networks and environment learning approaches DOI
S.K. Ammavasai

Journal of Intelligent & Fuzzy Systems, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 16

Published: March 22, 2024

The rapid growth of the cloud computing landscape has created significant challenges in managing escalating volume data and diverse resources within environment, catering to a broad spectrum users ranging from individuals large corporations. Ineffectual resource allocation systems poses threat overall performance, necessitating equitable distribution among stakeholders ensure profitability customer satisfaction. This paper addresses critical issue management through introduction Dynamic Task Scheduling with Virtual Machine (DTS-VM) strategy, incorporating Edge-Cloud for Internet Things (IoT). proposed approach begins by employing Recurrent Neural Network (RNN) algorithm classify user tasks into Low Priority, Mid High Priority categories. Tasks are then assigned Edge nodes based on their priority, optimizing efficiency application Spotted Hyena Optimization (SHO) selecting most suitable edge node. To address potential overloads edge, Fuzzy evaluates offloading decisions using multiple metrics. Finally, optimal is achieved Stable Matching algorithm. seamless integration these components ensures dynamic efficient resources, preventing prolonged withholding requests due absence essential resources. system aims enhance performance satisfaction while maintaining organizational profitability. effectiveness DTS-VM strategy validated comprehensive testing evaluation, showcasing its posed expanding landscape.

Language: Английский

Citations

5
SSEPC cloud: Carbon footprint aware power efficient virtual machine placement in cloud milieu DOI Creative Commons

Bivasa Ranjan Parida,

Amiya Kumar Rath, Bibudhendu Pati

et al.

Computer Science and Information Systems, Journal Year: 2024, Volume and Issue: 21(3), P. 759 - 780

Published: Jan. 1, 2024

The consumption of energy and carbon emission in cloud datacenters are the alarming issues recent times, while optimizing average response time service level agreement (SLA) violations. Handful researches have been conducted these domains during virtual machine placement (VMP) milieu. Moreover it is hard to find on VMP considering regions availability zones along with datacenters, although both them play significant roles VMP. Hence, we worked a novel approach propose hybrid metaheuristic technique combining salp swarm optimization emperor penguins colony algorithm, i.e. SSEPC place machines most suitable regions, zones, servers environment, mentioned quality parameters. Our suggested compared some contemporary algorithms this direction like Sine Cosine Algorithm Salp Swarm (SCA-SSA), Genetic Tabu-search (GATA), Order Exchange & Migration algorithm Ant Colony System (OEMACS) test its efficacy. It found that proposed consuming 4.4%, 8.2%, 16.6% less emitting 28.8%, 32.83%, 37.45% carbon, whereas reducing by 11.43%, 18.57%, 26% as counterparts GATA, OEMACS, SCA-SSA respectively. In case SLA violations, has shown effectiveness lessening value parameter 0.4%, 1.2%, 2.8% SCA-SSA, OEMACS

Language: Английский

Citations

4
An energy-saving virtual machine scheduling algorithm for resource management based on cloud computing technology DOI Creative Commons

Zhang Liang-yu

AIP Advances, Journal Year: 2025, Volume and Issue: 15(4)

Published: April 1, 2025

To solve the problem of imbalanced resource load in virtual machine clusters, an energy-saving scheduling algorithm based on cloud computing technology for management is proposed. In this paper, current research status and environments analyzed, concept characteristics, classification, application scenarios, key technologies are elaborated. This paper innovatively designs a universal chromosome structure with regions to adapt different data center server compositions introduces adaptive mutation operators genetic algorithms improve global search capabilities optimize schemes. addition, by restricting migration machines between homogeneous physical machines, energy loss during process can be reduced, more energy-efficient mapping scheme further calculated. Finally, collecting real loads reality, proposed experimentally validated using CloudSim simulation platform. The experimental results show that, same original configuration scheme, times greedy used GA2ND around 1000, while GA1ST 200 500, indicating that requires fewer than GA1ST. Therefore, effectively reduce consumption avoiding frequent innovation optimization strategy improves overall efficiency stability scheduling.

Language: Английский

Citations

0
A Comprehensive Review of Cloud Computing Virtual Machine Consolidation DOI Creative Commons
Jaspreet Singh, Navpreet Kaur Walia

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 106190 - 106209

Published: Jan. 1, 2023

In the last decade, users can access their applications, data, and services via cloud from any location with an internet connection. The scale of heterogeneous environments is continuously growing due to development computing-intensive smart devices. A data center central processing unit environment, it made up hardware-oriented machines known as Physical Machines (PMs) or server software-oriented Virtual (VMs). deployment a huge number physical servers result exponential in demand for has resulted high energy consumption ineffective resource usage. Efficient utilization minimizing power by have become crucial challenges. machine consolidation(VMC) method optimizing computing resources consolidating multiple VMs onto reduced PMs. By running fewer servers, VM consolidation lead reducing efficient utilization. This review paper presents comprehensive analysis virtual consolidation, exploring various strategies, benefits, challenges, future trends this domain. examining wide range literature year 2015 2023, attempts provide insight into current state its possible effects on performance sustainability computing. main flaw articles that authors focused different assessment metrics while emphasis should been improving efficiency quality service systems. Future research be aimed at developing multi-objective system emphasizes usage without sacrificing quality, preventing level agreements being compromised.

Language: Английский

Citations

9
A genetic algorithm‐based virtual machine scheduling algorithm for energy‐efficient resource management in cloud computing DOI
Shi Feng

Concurrency and Computation Practice and Experience, Journal Year: 2024, Volume and Issue: 36(22)

Published: July 2, 2024

Summary To address the unbalanced resource load of a virtual machine cluster, author proposes an energy‐saving scheduling algorithm based on management cloud computing technology. This article analyzes current and research in environment. It discusses concept, characteristics, classification, application scenarios, key technologies. A genetic is used to solve problem high energy consumption data center. The test results show that same original configuration scheme, migration times greedy adopted by GA2ND are about 1000, GA1ST between 200 500. scheme requires fewer machines. In result analysis, experiments compare proposed algorithms—DVFS, IMC, GA1ST, GA2ND—with focus migration. Notably, DVFS serves as reference for efficiency, IMC represents without optimization, denotes under heterogeneous model, signifies enhanced introduced this article. comparison aims assess efficiency performance each context simulated Therefore, can effectively reduce avoid frequent

Language: Английский

Citations

3
Resource Allocation in Cloud Computing Using Quasi-Opposition Learning Based Aquila Optimizer DOI

K. Aruna Kumari,

Vijaya Bhaskar Reddy Muvva,

Komuravelly Sudheer Kumar

et al.

Lecture notes in electrical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 61 - 70

Published: Jan. 1, 2025

Language: Английский

Citations

0
Privacy Preserving Face Recognition in Cloud Robotics: A Comparative Study DOI Creative Commons
Karri Chiranjeevi, Omar Cheikhrouhou, Ahmed Harbaoui

et al.

Applied Sciences, Journal Year: 2021, Volume and Issue: 11(14), P. 6522 - 6522

Published: July 15, 2021

Real-time robotic applications encounter the robot on board resources’ limitations. The speed of face recognition can be improved by incorporating cloud technology. However, transmission data to servers exposes security and privacy attacks. Therefore, encryption algorithms need set up. This paper aims study performance potential their impact deep-learning-based task’s accuracy. To this end, experiments are conducted for through various deep learning after encrypting images ORL database using cryptography image-processing based algorithms.

Language: Английский

Citations

11
Research on data mining method of network security situation awareness based on cloud computing DOI Creative Commons
Ying Zhou, Guodong Zhao, Roobaea Alroobaea

et al.

Journal of Intelligent Systems, Journal Year: 2022, Volume and Issue: 31(1), P. 520 - 531

Published: Jan. 1, 2022

Abstract Due to the complexity and versatility of network security alarm data, a cloud-based data extraction method is proposed address inability effectively understand situation. The information properties situation are generated by creating set spatial characteristics classification knowledge, which then used analyze optimize processing hybrid using cloud computing technology co-filtering technology. Knowledge about has been analyzed strategy. simulation results show that cyber crash occurs in window 20, after protection index drops 500. increase 500 windows consistent with effectiveness concept this document method, indicating can sense changes Starting from first attacked window, defense began decrease. In order simulate added defense, events 295th time were reduced original increased significantly corresponding period, perception results, further verifies reliability on event perception. This provides high-precision knowledge situations improves stability networks.

Language: Английский

Citations

8
An Autonomous Multi-Agent Framework using Quality of Service to prevent Service Level Agreement Violations in Cloud Environment DOI Open Access
Jaspal Singh, Major Singh Goraya

International Journal of Advanced Computer Science and Applications, Journal Year: 2023, Volume and Issue: 14(3)

Published: Jan. 1, 2023

Cloud is a specialized computing technology accommodating several million users to provide seamless services via the internet. The extension of this reverenced growing abruptly with increase in number users. One major issues cloud that it receives huge volume workloads requesting resources complete their executions. While executing these workloads, suffers from issue service level agreement (SLA) violations which impacts performance and reputation cloud. Therefore, there requirement for an effective design supports faster optimal execution without any violation SLA. To fill gap, article proposes automatic multi-agent framework ensures minimization SLA rate workload execution. proposed includes seven agents such as user agent, system negotiator coordinator monitoring arbitrator agent history agent. All work cooperatively enable irrespective dynamic nature. With model also resulted advantage minimized energy consumption data centres. inclusion within enabled predict future requirements based on records resource utilization. followed Poisson distribution generate random numbers are further used evaluation purposes. simulations proved more reliable reducing compared existing works. method average 55.71% 1200 47.84kWh 1500 workloads.

Language: Английский

Citations

4
Virtual Machine Allocation in Cloud Computing Environments using Giant Trevally Optimizer DOI Open Access

Hai-yu Zhang

International Journal of Advanced Computer Science and Applications, Journal Year: 2023, Volume and Issue: 14(9)

Published: Jan. 1, 2023

Cloud computing has gained prominence due to its potential for computational tasks, but the associated energy consumption and carbon emissions remain significant challenges. Allocating Virtual Machines (VMs) Physical (PMs) in cloud data centers, a known NP-hard problem, offers an avenue enhancing efficiency. This paper presents energy-conscious optimization approach utilizing Giant Trevally Optimizer (GTO) which is inspired by hunting strategies of giant trevally, proficient marine predator. Our study mathematically models trevally's behavior when targeting seabirds. The involves strategic selection optimal locations based on food availability, including pursuing seabird prey air or seizing it from water's surface. Through extensive simulations, our method demonstrates superior performance terms skewness, CPU utilization, memory overall resource allocation research promising addressing challenges centers while optimizing utilization sustainable cost-effective operations.

Language: Английский

Citations

4