Jul 2026· Fall Joint Computer Conference· pp. 426-431· 0 citations· 13 references
Abstract
Multi-cloud and hybrid-cloud deployment has become a common architecture for enterprise artificial intelligence (AI) services, where inference requests may need to be processed across heterogeneous public, private, and regional cloud domains. Existing studies mainly model incoming requests as conventional resource-oriented tasks and focus on workload placement, latency reduction, or execution efficiency. However, such models are insufficient for enterprise AI services, because practical requests often carry richer service semantics, including business importance, service quality requirement, privacy sensitivity, compliance constraint, and feasible execution domain. To address this issue, this paper models each request as a semantic AI service request and investigates a semantic-aware scheduling and resource allocation problem in enterprise multi-cloud environments. To solve the formulated mixed-integer nonlinear problem, we develop a two-stage semantic-aware orchestration algorithm. In the first stage, the orchestrator selects the target cloud domain for each request and reserves the minimum feasible computation resource to satisfy its deadline. In the second stage, the remaining computation resource is allocated within each cloud domain to further reduce inference delay. Experimental results show that our proposed algorithm consistently outperforms representative baselines in terms of accepted request ratio, averaged delay, and mismatch penalty across different multi-cloud configurations.
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.
Y. Gaidhani, Vudutha Sravanthi, D. L. Narayana et al.· International Conference Com...· 0 citations
This study proposes a Context-Aware AI framework for dynamic cloud resource management that incorporates workload patterns, user behavior, network conditions, infrastructure health, and business objectives and provides a foundation for future technologies such as edge computing, IoT, 6G networks, and intelligent enterprise applications.
Richard Evans, Karen Lewis· International Journal of App...· 0 citations
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, B. Chaurasia et al.· Journal of Circuits, Systems...· 0 citations
Cloud-edge computing environments are evolving rapidly, requiring orchestration mechanisms that may automatically construct and manage complex multi-step workflows with little human intervention. We introduce a framework for the agentic AI and how it should be able to orchestrate an autonomous end-to-end workload of cloud-edge enterprise infrastructures in general. The proposed framework relies on large language model (LLM)-driven agents capable of dynamic task decomposition, real-time decision-making, and self-correcting execution pipelines to manage heterogeneous workloads. Through the incorporation of multi-agent coordination protocols, context-aware scheduling algorithms, and feedback-driven optimization loops, the system facilitates seamless task delegation throughout edge nodes and cloud backend systems while managing latency, resource allocation, and compliance constraints. Experimental evaluations show up to percentage improvements in workflow completion rates, resource utilization, and fault tolerance over traditional static-command Rule-based orchestration approaches. Additionally, the framework features explainability modules and audit trails to promote transparency and accountability in autonomous operations. The results provide evidence that agentic AI architectures can serve as a scalable, resilient and intelligent control mechanism for next generation enterprise workflow management across hybrid cloud-edge settings. This has laid a foundation and is to our best of knowledge, the first systematic pioneers work that lays down a roadmap for production-grade autonomous orchestration deployed in analytics and enterprise domains.
Shiza Arshad, Anusha Joodala, A. Agade et al.· 2026 International Conferenc...· 0 citations
The paper argues for a shift from proof of concept scheduling studies toward reproducible, transparent, and deployable fog systems, and identifies several priorities for future work: standardized benchmark workloads, cloud native scheduling that accounts for container lifecycle and microservice dependencies, resilience aware scheduling that treats failures and migration as first class concerns, and carbon aware orchestration that extends beyond energy minimization.
Albahlool M Abood· Asian Journal of Research in...· 0 citations
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
