Jul 2026· International Conference Computing Methodologies and Communication· pp. 1388-1394· 0 citations· 13 references
Abstract
Enterprises are increasingly adopting multi-cloud infrastructures to increase service availability, scalability, fault tolerance and vendor independence. Nevertheless, the allocation of resources among heterogeneous cloud providers is a highly critical issue because the workloads are variable, the latency is limited, the SLA compliance issues are present, and the privacy concerns are also central to the centralized scheduling models. Conventional centralized reinforcement schedulers in learning demand complete visibility of workload, which can cause overheads in the communication and expose risks of data disclosure. In this paper, we suggest a Federated Reinforcement Learning Scheduler (FRLS) in privacy-sensitive and adaptive optimization of resources on multi-clouds. The proposed architecture has every cloud node separately train a reinforcement learning-based scheduling agent on local observations of workload. Rather than exchanging raw data, nodes exchange model parameters with a federated aggregation server on an irregular basis and weighted averaging is used to create a global scheduling model. Simulations with synthetic heterogeneous workload traces show that FRLS utilizes its resources 15-18 percent more efficiently, SLA violations 8-10 percent fewer and converge quicker than heuristic and centralized RL schedulers. The framework offers scalable, secure and distributed intelligence on next-generation multi-cloud orchestration systems.
This research proposes an AI-driven resource scheduling framework that integrates workload prediction, resource classification, intelligent scheduling, and continuous feedback mechanisms that aims to optimize multiple objectives, including cost reduction, execution efficiency, energy consumption, and SLA compliance.
Michael Anderson· International Journal of App...· 0 citations
An explainable agentic AI framework for multi-cloud task allocation built on a contextual-bandit agent that observes each provider's current price, estimated latency, and load before autonomously selecting a placement, then updates its policy online from the resulting cost, latency, and service-level-agreement (SLA) outcome.
Dr. Sajitha A V· International Journal of Int...· 0 citations
While the advantages of fog computing in delivering low-latency Internet of Things (IoT) applications are well understood, efficient load balancing remains a constant challenge due to the diversity of node capabilities and the uncertainty of workloads. Current scheduling methods are typically reactive and only take action once they detect congestion, and require gathering data centrally, which can be privacy and bandwidth sensitive. In this paper, a Federated Predictive Load Balancing (FPLB) framework is proposed to combine Long Short-Term Memory (LSTM) workload forecasting with federated learning, which does not require fog nodes to share their operational data. Predicted workloads feed a normalized load index for proactive task assignment, while a differential-privacy mechanism with a Rényi accountant protects model updates during federated aggregation. All experiments are reported from a self-contained simulator. Across eight independent seeds under a moderate-to-high load, FPLB attains the lowest average task latency (149.2 ms), significantly below Deep Q-Network (DQN) scheduling (1.6% reduction; p < 0.01, Wilcoxon signed-rank) and a federated-DQN control, and well below reactive heuristics (24.3% below round-robin). The margin widens with load, reaching 2.9% over DQN at 10 tasks/s, and FPLB’s latency variance is consistently the lowest, indicating more predictable scheduling. An ablation confirms that workload prediction is the primary driver of the improvement and that federation lowers prediction error. Federated communication overhead is 0.4% of network traffic at 50 nodes, rising to only 3.7% at 500 nodes, and performance is robust to 30% per-round node dropout. This paper further characterizes the privacy-utility envelope as the budget tightens from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\epsilon\:$$\end{document} = 3.2 to 0.5. At low load, where congestion is rare, DQN is comparable, so the framework is most valuable for deployments that regularly experience dynamic or peak-heavy demand.
Arti Sharma, Rajendra Prasad Mahapatra, Vineet Sharma et al.· Scientific Reports· 0 citations
With the rapid development of the Internet of Things (IoT) and edge computing, Federated Learning (FL) has emerged as a promising distributed framework capable of effectively leveraging distributed devices for machine learning tasks while preserving data privacy. However, in practical scenarios characterized by significant device heterogeneity and communication delays, traditional synchronous FL often suffers from inefficiency due to the straggler effect. Although Asynchronous Federated Learning (AFL) has been proposed to mitigate this, it still faces challenges regarding model staleness and training bias, which hinder convergence performance. To address these issues, this paper presents an asynchronous framework named FedQS. First, FedQS employs a multi-dimensional staleness evaluation mechanism that dynamically assesses updates by combining the similarity between local and global models with client latency metrics. Second, to resolve the coupling between training bias and model staleness, we implement a decoupling solution via a queue scheduling algorithm. This algorithm reprocesses high-staleness model parameters on selected faster helper clients using only the helpers’ local private data while preserving the provenance of the original uploading clients, thereby aiming to reduce stale-update effects in aggregation. Finally, during the aggregation phase, the framework recalibrates aggregation weights based on client contributions to reduce training bias and improve global model accuracy. Comparative experiments on Fashion-MNIST and CIFAR-10 datasets demonstrate that FedQS outperforms four evaluated representative baselines—FedAsync, FedBuff, KAFL, and CAFL—achieving an average accuracy improvement of 8.62%.
Jia-Hui Zhou, Fang Li, Tian-Yu Shi et al.· Journal of Cloud Computing· 0 citations
Compute and memory resources in cloud environments are strictly managed and isolated by the control plane; in contrast, network resources lack equivalent management and isolation mechanisms. This best-effort treatment of networking leads to significant challenges for modern AI workloads, which have diverse and bandwidth-intensive communication patterns. Without fine-grained network resource control, these workloads suffer from interference, unpredictable throughput, and suboptimal cluster utilization. To address these issues, this paper demonstrates how network bandwidth can be elevated to a first-class, schedulable, and enforceable resource within Kubernetes, the de facto standard for cloud-native orchestration. We introduce a new scheduling capability that models network interfaces as allocatable resources and regulates bandwidth sharing through the Dynamic Resource Allocation (DRA) framework, with enforcement implemented using the Hierarchical Token Bucket (HTB) mechanism. We evaluate the system using multitenant AI workloads derived from real-world communication characteristics with a simulation-based approach and validate the proposed enforcement strategy in a real cluster. Results show that the proposed two-level bandwidth allocation improves tenant performance predictability and satisfaction while maintaining packed cluster utilization.
A lightweight, QoS-aware service placement algorithm that evaluates latency, bandwidth, and node load in real time is introduced that yields reduced latency and more consistent wait times relative to heuristic and genetic baselines.
Anshul Atre, K. Singh, Brijesh Kumar Chaurasia et al.· Journal of Circuits, Systems...· 0 citations
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