An Improved Machine Learning Method by applying Cloud Forensic Meta-Model to Enhance the Data Collection Process in Cloud Environments DOI Open Access

ٍRafef Al-mugern,

Siti Hajar Othman,

Arafat Al-Dhaqm

и другие.

Engineering Technology & Applied Science Research, Год журнала: 2024, Номер 14(1), С. 13017 - 13025

Опубликована: Фев. 8, 2024

Cloud computing has revolutionized the way businesses operate by offering accuracy in Normalized Mutual Information (NMI). However, with growing adoption of cloud services, ensuring and validation common processes through machine learning clustering these concepts as well generated forensics experts’ data environments become a paramount concern. The current paper proposes an innovative approach to enhance collection procedure applying Forensic Meta-Model (CFMM) integrating it techniques improve forensic data. Through this approach, consistency compatibility across different terms are ensured. This research contributes ongoing efforts validate process for advance field standardizing representation data, certifying NMI environments.

Язык: Английский

Towards dynamic virtual machine placement based on safety parameters and resource utilization fluctuation for energy savings and QoS improvement in cloud computing DOI
Dan Wang, Jinjiang Wang,

Xize Liu

и другие.

Future Generation Computer Systems, Год журнала: 2025, Номер unknown, С. 107853 - 107853

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

1
Robust and Trustworthy Data Sharing Framework Leveraging On-Chain and Off-Chain Collaboration DOI Open Access

Jinyang Yu,

Xiao Zhang, Jinjiang Wang

и другие.

Computers, materials & continua/Computers, materials & continua (Print), Год журнала: 2024, Номер 78(2), С. 2159 - 2179

Опубликована: Янв. 1, 2024

The proliferation of Internet Things (IoT) systems has resulted in the generation substantial data, presenting new challenges reliable storage and trustworthy sharing.Conventional distributed are hindered by centralized management lack traceability, while blockchain limited low capacity high latency.To address these challenges, present study investigates sharing IoT presents a novel system architecture that integrates on-chain off-chain data manage systems.This architecture, integrating technologies, provides high-capacity, high-performance, traceable, verifiable access.The system, built on Hyperledger Fabric, manages metadata, verification permission information raw data.The implemented using IPFS Cluster, ensures efficient access to massive files.A collaborative server is designed integrate operation interfaces, facilitating comprehensive operations.We provide unified interface for user-friendly interaction.Extensive testing validates system's reliability stable performance.The proposed approach significantly enhances compared standalone systems.Rigorous tests consistently yield positive outcomes.With average upload download throughputs roughly 20 30 MB/s, respectively, throughput surpasses factor 4 18.

Язык: Английский

Процитировано

2
Towards virtual machine scheduling research based on multi-decision AHP method in the cloud computing platform DOI Creative Commons

Hangyu Gu,

Jinjiang Wang, Junyang Yu

и другие.

PeerJ Computer Science, Год журнала: 2023, Номер 9, С. e1675 - e1675

Опубликована: Ноя. 14, 2023

Virtual machine scheduling and resource allocation mechanism in the process of dynamic virtual consolidation is a promising access to alleviate cloud data centers prominent energy consumption service level agreement violations with improvement quality (QoS). In this article, we propose an efficient algorithm (AESVMP) based on Analytic Hierarchy Process (AHP) for accordance measure. Firstly, take into consideration three key criteria including host power consumption, available balance ratio, which ratio can be calculated by value between overall three-dimensional (CPU, RAM, BW) flat surface (when new migrated (VM) consumed targeted host's resource). Then, placement decision determined application multi-criteria making techniques AHP embedded above-mentioned criteria. Extensive experimental results CloudSim emulator using 10 PlanetLab workloads demonstrate that proposed approach reduce center number migration, violation (SLAV), aggregate indicators comsumption (ESV) average 51.76%, 67.4%, 67.6% compared cutting-edge method LBVMP, validates effectiveness.

Язык: Английский

Процитировано

5
An effective partition-based framework for virtual machine migration in cloud services DOI

L.X. Yun X. Zhang Z.H. Luo,

S. Wei, Hua Tang

и другие.

Cluster Computing, Год журнала: 2024, Номер 27(9), С. 12899 - 12917

Опубликована: Июнь 19, 2024

Язык: Английский

Процитировано

1
An Improved Machine Learning Method by applying Cloud Forensic Meta-Model to Enhance the Data Collection Process in Cloud Environments DOI Open Access

ٍRafef Al-mugern,

Siti Hajar Othman,

Arafat Al-Dhaqm

и другие.

Engineering Technology & Applied Science Research, Год журнала: 2024, Номер 14(1), С. 13017 - 13025

Опубликована: Фев. 8, 2024

Cloud computing has revolutionized the way businesses operate by offering accuracy in Normalized Mutual Information (NMI). However, with growing adoption of cloud services, ensuring and validation common processes through machine learning clustering these concepts as well generated forensics experts’ data environments become a paramount concern. The current paper proposes an innovative approach to enhance collection procedure applying Forensic Meta-Model (CFMM) integrating it techniques improve forensic data. Through this approach, consistency compatibility across different terms are ensured. This research contributes ongoing efforts validate process for advance field standardizing representation data, certifying NMI environments.

Язык: Английский

Процитировано

0