P-ACOHONEYBEE: A Novel Load Balancer for Cloud Computing Using燤athematical Approach

Ajagbe, Sunday Adeola; Oyediran, Mayowa O.; Nayyar, Anand; Awokola, Jinmisayo A.; Al-Amri, Jehad F.

doi:10.32604/cmc.2022.028331

Cited by 3 publications

(3 citation statements)

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“…These organizations typically share common concerns such as mission objectives, security requirements, compliance, and jurisdiction. While the costs of this cloud model are distributed among a smaller number of users compared to the public cloud, they are still shared among a larger user base compared to the private cloud [14].…”

Section: Overview Of Cloud Computingmentioning

confidence: 99%

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Comprehensive review of load balancing in cloud computing system

Oyediran,

Ojo,

Ajagbe

et al. 2024

IJECE

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Load balancing plays a critical role in optimizing resource utilization and enhancing performance in cloud computing systems. As cloud environments grow in scale and complexity, efficient load balancing mechanisms become increasingly vital. This paper presents a comprehensive review of load balancing techniques in cloud computing systems, with a focus on their applicability, advantages, and limitations. The review encompasses both static and dynamic load balancing approaches, evaluating their effectiveness in addressing the challenges posed by cloud infrastructure, such as heterogeneity, scalability, and variability in workload demands. Furthermore, the review examines load balancing algorithms considering factors such as resource utilization, response time, fault tolerance, and energy efficiency. Additionally, the impact of load balancing on cloud performance metrics, including throughput, latency, and scalability, is analyzed. This review aims to provide insights into the state-of-the-art load balancing strategies and serve as a valuable resource for researchers, practitioners, and system designers involved in the development and optimization of cloud computing systems.

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Section: Overview Of Cloud Computingmentioning

confidence: 99%

“…Ajagbe et al [14] describe load balancing as the process used to regulate incoming traffic and stop providing tasks to overburdened and unresponsive resources. It was also proposed that load balancing methods must be resilient enough to be compatible with a wide range of application types.…”

Section: Load Balancing In Cloud Computingmentioning

confidence: 99%

Comprehensive review of load balancing in cloud computing system

Oyediran,

Ojo,

Ajagbe

et al. 2024

IJECE

View full text Add to dashboard Cite

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“…The dramatical implementation of DL techniques made the easy access of using online business applications relating to the financial transaction, and these featured processes attracted the cyber threat (phishing, hacking, fraud) originated by cybercriminals, ERRATA: In the first accepted version of the paper published on October 26, 2023 in the author list was another author: José Luis Arias Gonzáles which turned out to be an error in the submission. This corrected author list was published on Web on November 9, 2023. some are operating via the dark web (DW), anonymous porting of hidden internet area, affecting directly and indirectly, economy, security and operation on the internet [3,4,5]. Figure 1 shows the fraud framework affecting organizations.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Artificial Intelligence Systems for Fraud Detection and Forensics in Dark Web Environments

RAWAT,

Oki,

Chakrawarti

et al. 2023

IJCAI

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Artificial Intelligence (AI) influenced technical aspects of research for generating automated intelligent behaviors covering divergent domains but has shown appreciable results when used in cyber forensic technology for crime analysis and detection. AI experts warned about possible security risks associated with algorithms and training data, as AI inherits computing features dealing with IoT-based Smart applications and autonomous transportation, and may be found to be susceptible to vulnerability and threats. The present work discusses models for analyzing terrorists related information and categorizing malicious events by covering the literature review on security risks and AI-related criminality by presenting a taxonomy of criminal behavior and signatures covering tools and criminal targets used in fraudulent activities using AI features by digital forensictechniques. We've also shown how AI may make existing crimes more potent, and that new sorts of crimes could emerge that haven't been identified previously. This study has presented a systematic structure for AI crime and dealing strategies. Furthermore, we have proposed AI forensics, a unique strategy for combating AI crime. We discovered that several concepts of DF are still not preferred in AI-based forensics after conducting a comparative examination of forensics.Povzetek: Narejen je pregled sistemov umetne inteligence za ugotavljanje kriminalnih spletnih aktivnosti z dodatno analizo, kje je možno AI uporabiti za te namene.

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