Article detail · 2025
A Probabilistic Approach to Load Balancing in Multi-Cloud Environments via Machine Learning and Optimization Algorithms
- Year
- 2025
- Type
- article
Data source split
- YÖKSİS YÖKSİS article record
- YÖKSİS venue Journal of Grid Computing
- Catalog match (ISSN) Journal of Grid Computing
- OpenAlex OpenAlex enrichment (abstract, citations, topics)
Abstract
OpenAlex · English
Abstract Efficient load balancing stands out as a crucial challenge in multi-cloud environments, particularly for applications that demand ultra-reliable, low-latency communications (URLLC). This paper proposes a novel approach integrating Decision Functions with Normal Distributions (DFND) for precise probabilistic modeling of task-to-cloud compatibility. Multivariate normal distributions capture interdependencies between resource features such as CPU, memory, bandwidth, and latency, ensuring accurate resource compatibility evaluation. Additionally, the Tasmanian Devil Optimization (TDO) algorithm employs dynamic exploration and exploitation strategies inspired by natural behaviors, providing rigorous optimization to improve task assignment in dynamic, multi-cloud environments. It uses flexible methods to ensure the optimization process is both efficient and scalable. Simulation results using CloudSim demonstrate significant improvements over state-of-the-art methods in terms of makespan reduction, response time minimization, resource utilization, and cost efficiency. The proposed framework effectively supports latency-sensitive, large-scale applications in dynamic, heterogeneous multi-cloud environments.
Topics
Citations
OpenAlex cited_by_count. Not a WoS or Scopus citation count; those sources have no separate column here.
25 citations
OpenAlex cited_by_count (cache / database)
10 publications in the local catalog that cite this work (OpenAlex reference match; not the full global list).
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