The AnyLog Edge Data Fabric

Network Programming and Microservices: Building Scalable AI-Driven Distributed Systems for Real-Time Data Processing

This paper explores the integration of network programming and microservices architecture to build scalable, AI-driven distributed systems for real-time data processing. As artificial intelligence becomes increasingly crucial for real-time decision-making in industries like healthcare, finance, and e-commerce, there is a growing need for systems that can process vast amounts of data efficiently while ensuring scalability and low latency. Network programming techniques are foundational to distributed systems, enabling seamless communication between services. Meanwhile, microservices provide a modular approach that supports scalability and flexibility, essential for AI applications. The paper discusses the role of these technologies in building AI-powered distributed systems, challenges related to network latency, data consistency, and fault tolerance, and real-world applications across industries. Additionally, it delves into future trends such as edge computing and automated scaling in the context of AI-driven distributed systems.

Resilient Edge-to-Cloud AI Architectures for Distributed Real-Time Decision Making

A research-driven conceptual framework for resilient edge-to-cloud AI architectures supporting distributed real-time decision making and identifies limitations associated with heterogeneous devices, uncertain ground truth, model drift, communication failures, and the absence of uniform evaluation criteria are identified.

3 Evolving the Digital Industrial Infrastructure for Production: Steps Taken and the Road Ahead

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.

Autonomous Data Products: Enabling AI-Driven Data Interoperability in Cloud Architectures

As enterprises transition toward increasingly distributed and cloud-native architectures, the need for seamless data interoperability has become paramount. Traditional data integration and governance approaches often fall short in dynamic, multi-cloud environments. Autonomous Data Products (ADPs) emerge as a transformative paradigm—self-contained, self-describing, and AI-enabled units that encapsulate data, metadata, policies, and processing logic. This paper explores the architecture, capabilities, and implementation strategies of ADPs to enhance data interoperability across cloud ecosystems. We discuss how AI enables adaptive schema evolution, smart data discovery, and automated quality checks within ADPs, and how they align with principles of data mesh and data fabric. Through technical frameworks and real-world use cases, we demonstrate how autonomous data products can drive scalability, agility, and intelligence in modern data architectures.

AI-Augmented Data Pipeline Optimization for Scalable Cloud Systems

Deterministic ETL architectures - scheduled, fixed, and failure-reactive - cannot sustain the operational requirements of modern enterprise data environments, where volume growth, schema instability, and SLA pressure compound continuously. This paper presents a five-layer AI-augmented pipeline operating model that replaces reactive recovery with proactive, adaptive operation. The model integrates intelligent scheduling, continuous anomaly detection, and an operational copilot capability within a coherent Azure-native reference architecture anchored by a persistent feedback store. A structured implementation pathway and a three-dimensional evaluation framework - covering operational reliability, data quality, and delivery performance - are provided alongside the architectural specification. The model is grounded in operational observability as a prerequisite for automation, with governance controls embedded as non-optional cross-cutting elements.