From HPC to Edge: A Web-Based Workflow for AI Model Testing and Deployment

Nix to the Rescue for a Reproducible HPC-AI Software Stack

Reproducibility in HPC remains difficult under the constraints of production supercomputers: no root access, limited internet, and software stacks that increasingly span C/C++, Fortran, Python, MPI, and GPU runtimes. Traditional approaches based on environment modules and Conda require manual intervention to locate dependencies, leak system libraries into builds, and fail to compose across projects. Containers help with deployment but do not by themselves guarantee reproducibility. We report on our experience building a hybrid HPC/AI software stack with Nix, covering local development on a workstation without root and remote deployment as an Apptainer image on a production cluster. Nix's consistent package layout, full environment isolation, and flake-based composition resolve the dependency discovery, leakage, and composition problems we encountered, while unifying C/C++ and Python management under a single declarative specification that also generates the deployment container. We discuss trade-offs against Spack and Guix, the development-versus-production split addressed via CMake presets, and current gaps in ML package coverage in Nixpkgs.

Future Generation Computer Systems

Towards Edge AI Native Service Platforms: Rethinking Runtime, Deployment, and Migration

Edge AI is evolving from isolated inference toward long-running services that coordinate model pipelines, data streams, state, and accelerators near users and physical environments. Cloud-native and edge-native platforms offer useful foundations, but their primary control objects–containers, nodes, links, and enrolled sites–generally do not expose the model, data, state, quality, and participation semantics required by these services. This paper presents eAI+, a vision for an edgeAI-native service platform built around three first-class control objects: AI service graphs, dynamic edge resource fabrics, and participant contracts. eAI+ aims to preserve service quality under latency, privacy, reliability, cost, and participation constraints through three coordinated mechanisms. Runtime would select safe execution adaptations based on current workload, environment, and contract signals. Deployment would map service-graph components and prepared fallbacks to heterogeneous resources. PolyLink is the participant-contract module for plug-and-play resource onboarding; it would register contributors, verified resource offers, capabilities, and participation terms. Once a resource is onboarded, it would become available to Deployment for placing eligible service-graph components under the registered contract, while PolyLink would maintain metering, reputation, rewards, and exit events. Migration would transfer only continuity-critical state or control when mobility, overload, policy changes, or contributor lifecycle events invalidate the current placement. This framing treats edge AI as a coordinated service-platform problem across models, data, state, resources, and contracts.

Serverless Data Engineering: Innovations in Python-Driven ETL Automation on AWS

Traditional cluster-based ETL architectures impose a structural tax on data engineering organisations: fixed compute resources provisioned for peak demand, scheduled batch cycles that introduce latency regardless of downstream urgency, and operational overhead that redirects engineering capacity from pipeline design to cluster maintenance. AWS serverless services Lambda, Glue, Step Functions, and Kinesis address this tax by decoupling compute from provisioning, enabling pipelines that execute in response to events, scale automatically with workload demand, and charge only for resources consumed. Python, with native Lambda runtime support and an ecosystem that spans Pandas, PySpark, and the AWS SDK for Pandas, has become the primary implementation language for serverless ETL logic at an enterprise scale. This article examines the engineering decisions that determine whether that architectural promise translates into operational reality. The analysis covers Lambda’s execution model and its hard constraints, Glue’s managed Spark environment and its cost engineering implications, Step Functions orchestration patterns, S3-native data lake architecture, cost optimization strategies, security and governance controls, and real-time processing patterns. The central argument is that serverless ETL does not simplify data engineering; it redistributes engineering effort from infrastructure management to execution model design. Lambda timeout decomposition, cold start management, Glue worker right-sizing, IAM role scoping, and Step Functions state machine cost design are not configuration afterthoughts; they are first-class engineering decisions with direct consequences for pipeline reliability, cost, and compliance readiness. The analysis draws on the practitioner perspective of a data engineer with extensive experience building ETL pipelines in regulated financial services and healthcare environments where infrastructure decisions carry audit consequences.

Analysis of Deployment Patterns for Edge Hosted Multimodal AI in Cloud–Edge Robotic Systems Utilizing ROS 2

Cloud-edge robotics combines on-board computing with cloud services to enhance the intelligence and scalability of robotic systems. However, professionals still lack guidance on how to package and deploy AI components. This article presents a comparative evaluation of four ROS 2 deployment patterns for cloud-edge robotic AI: monolithic containers, microservices, dynamic module loading and overlay workspaces. All AI inference components run on the edge node in every pattern, so the comparison characterises the packaging and lifecycle mechanics of an edge-hosted, orchestrated, delivered, and observed multimodal workload. The four patterns are deployed on a K3s cluster combining an NVIDIA Jetson AGX Orin edge node with two amd64 cloud nodes, and run an identical workload combining perception (YOLOv8-nano), visual reasoning (LLaVA-1.5-7B) and voice interaction (Voxtral-Mini-3B), with a deterministic LLaVA trigger ensuring identical contention across patterns. Deployment cost is measured under three explicit regimes, warm restart, image cache cold start and fully clean deployment, with means and 95 per cent confidence intervals over replicated campaigns, and runtime behaviour is characterised with queue wait and frame drop instrumentation together with a sensitivity sweep of the reasoning trigger period from 30 to 300 seconds and of the camera input rate. The results reveal a clear trade-off. The dynamic pattern hot swaps a module in the live process in 0.78 to 31.10 seconds depending on the module, which is about two orders of magnitude faster than the rebuild and redeploy cycle for the lightweight nodes but only about one order of magnitude faster for the two model heavy nodes, and it collapses the real-time perception path when several AI nodes share a single Python interpreter, a degradation that a controlled hybrid experiment and a GPU utilisation control attribute to single interpreter serialisation rather than to the reasoning schedule or to accelerator contention. The overlay pattern restarts an operating point on par with the monolithic baseline yet is the slowest to provision from scratch or to update because of its coarse layer granularity, while the process isolated monolithic and microservices patterns trade update agility for runtime robustness. The results show that no pattern dominates. The overlay pattern offers an operating point restart on par with the monolithic warm baseline and, once its per layer carrier is activated, the smallest recurring update payload, but the slowest fully clean deployment of the four patterns, and the runtime collapse of dynamic loading is specific to the evaluated all Python single process composition and persists across the whole range of trigger periods, so it reflects the composition rather than the workload intensity. Fleet-scale benefits discussed for the overlay pattern are extrapolations from a single-edge testbed rather than measured results.

Provisioning AI-Ready Infrastructure at Scale: Engineering Considerations for Infrastructure Architects

Enterprise adoption of artificial intelligence is restructuring the discipline of infrastructure planning in ways that conventional capacity models cannot accommodate. Artificial intelligence workloads span a heterogeneous spectrum of training, fine-tuning, inference, and batch scoring operations, each imposing qualitatively distinct demands on accelerator compute, storage throughput, and network fabric. The proliferation of graphics processing unit-accelerated clusters, high-bandwidth interconnects, and multi-cloud execution environments has rendered traditional provisioning frameworks inadequate for governing the scale, velocity, and compliance complexity inherent to production artificial intelligence platforms. This article presents a practitioner-oriented engineering framework for provisioning artificial intelligence-ready infrastructure that remains architecturally stable across accelerator generations, managed service evolutions, and organizational growth trajectories. Drawing on operational patterns from large-scale cloud transformation programs, the framework addresses workload segmentation, layered platform architecture, accelerator cluster governance, data provenance, network engineering, security, reliability, and cost governance as interdependent engineering concerns. The central argument is that organizations achieving sustained operational excellence in artificial intelligence infrastructure do so through deliberate platform architecture governed by automation-first operational practices, not through hardware procurement alone. The article concludes by projecting the long-term strategic implications of multi-cloud artificial intelligence transformation as a governed maturity progression, offering forward-looking guidance for infrastructure architects navigating an accelerating and mission-critical technology landscape