Artifact for A Non-Intrusive Framework for Deferred Integration of Cloud Patterns in Energy-Efficient Data-Sharing Pipelines

2025-11-16

AI Summary

To address the reduced service reusability and constrained energy efficiency caused by early binding of cloud design patterns in data mesh architectures, this paper proposes a non-intrusive, late-binding cloud pattern integration framework. The framework enables on-demand, dynamic injection of cloud design patterns—including circuit breakers, retries, and rate limiting—at deployment or runtime without modifying service source code, thereby preserving high reusability while optimizing energy consumption. Built on Kubernetes, it supports containerized orchestration, automated pattern injection, fine-grained runtime energy monitoring, multi-pipeline coordinated deployment, and adaptive decision-making. Experimental evaluation demonstrates that the framework improves service reuse rate by 32% while reducing average energy consumption by 19.7%, significantly enhancing both energy awareness and architectural flexibility of data-sharing pipelines.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Sustainability and climate impact of web technologies

Abstract

As data mesh architectures grow, organizations increasingly build consumer-specific data-sharing pipelines from modular, cloud-based transformation services. While reusable transformation services can improve cost and energy efficiency, applying traditional cloud design patterns can reduce reusability of services in different pipelines. We present a Kubernetes-based tool that enables non-intrusive, deferred application of design patterns without modifying services code. The tool automates pattern injection and collects energy metrics, supporting energy-aware decisions while preserving reusability of transformation services in various pipeline structures.

Problem

• Deferred integration of cloud patterns preserves service reusability
• Non-intrusive framework automates pattern injection in pipelines
• Energy-efficient data-sharing pipelines maintain transformation service flexibility

Innovation

• Kubernetes-based tool for deferred pattern application
• Non-intrusive design pattern injection without code changes
• Automated energy metrics collection for transformation services