Authors: Komal Jain, Ajeet Kumar, Shravanthi R, Ritu Chauhan
Abstract: Oracle Solaris Logical Domains (LDOMs) offer flexible, high-performance virtualization at the hardware layer, enabling fine-grained resource allocation across critical workloads. However, as enterprise infrastructures grow in complexity and scale particularly in edge and hybrid environments the need for dynamic and intelligent load balancing becomes paramount. Traditional static and reactive policies fall short in addressing modern demands marked by workload volatility, bursty usage patterns, and constrained physical resources. In this context, Edge AI models present a transformative approach to adaptive load management. This review explores how AI particularly Edge-deployed supervised, unsupervised, time-series, and reinforcement learning models can be leveraged to predict resource saturation, detect faults, and proactively manage LDOM reallocation and live migrations. Emphasis is placed on integrating AI pipelines with Solaris-native telemetry tools (kstat, vmstat, prstat) and automating control actions using the ldm command suite. Real-world case studies across telecom, financial, and healthcare sectors are analyzed to demonstrate improvements in SLA compliance, resource efficiency, and fault avoidance through AI-assisted decisions. We further address system-level integration with Oracle Ops Center, highlight governance concerns such as model explainability and override control, and explore lightweight inference frameworks suitable for constrained control domains. Challenges in data quality, model trust, and automation safety are also discussed. The review concludes by outlining future directions including federated learning, policy-aware AI agents, cross-domain telemetry fusion, and convergence with AI-Ops ecosystems. By embedding intelligence directly into the LDOM infrastructure, organizations can evolve from static resource provisioning to a self-optimizing virtualization platform—capable of continuous learning, rapid adaptation, and resilience at the edge. This shift is vital to meet the performance and operational demands of modern digital infrastructure.
DOI: https://doi.org/10.5281/zenodo.15846618
