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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 112
Article Submitted : 17346
Article Published : 7552
Total Authors : 20065
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| Paper Title: | Leveraging Artificial Intelligence for Dynamic Workload Management in Edge and Cloud Environments |
| Authors Name: | Supriya Kumari , Rakshith M , Sibi Chakravarthy K S , Touhid Sayyed |
| Download E-Certificate: | Download |
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IJRTI_190151
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| Published Paper Id: | IJRTI2407034 |
| Published In: | Volume 9 Issue 7, July-2024 |
| DOI: | https://doi.org/10.5281/zenodo.12770747 |
| Abstract: | The rapid evolution of IT infrastructure has spurred a paradigm shift in computing architectures, with edge and cloud computing emerging as pivotal platforms. This comparative analysis investigates strategies for optimizing these infrastructures through the lens of AI/ML integration. Edge computing, characterized by its proximity to data sources, offers reduced latency and enhanced privacy but faces challenges in resource constraints and management complexity. In contrast, cloud computing provides scalability and centralized processing power but at the cost of increased latency. This study proposes novel frameworks for leveraging AI/ML algorithms to dynamically allocate workloads between edge nodes and the cloud, optimizing performance metrics such as latency, throughput, and energy efficiency. Key considerations include security implications, regulatory compliance, and the economic viability of hybrid edge-cloud architectures. Case studies from diverse sectors illustrate the practical application and benefits of AI/ML-driven optimizations in real-world scenarios. By addressing these complexities, this research contributes to the ongoing discourse on efficient IT infrastructure design, paving the way for scalable, secure, and adaptive computing environments tailored to meet the demands of modern applications. |
| Keywords: | Edge Computing, Cloud Computing, Artificial Intelligence (AI), Machine Learning (ML), Dynamic Resource Allocation, Latency Reduction |
| Cite Article: | "Leveraging Artificial Intelligence for Dynamic Workload Management in Edge and Cloud Environments ", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 7, page no.309 - 316, July-2024, Available :http://www.ijrti.org/papers/IJRTI2407034.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2407034
Registration ID:190151
Published In: Volume 9 Issue 7, July-2024
DOI (Digital Object Identifier): https://doi.org/10.5281/zenodo.12770747
Page No: 309 - 316 Country: Shastri nagar, Maharashtra, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2407034 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2407034 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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