FlowUnits: Extending Dataflow for the Edge-to-Cloud Computing Continuum

2025-04-15

AI Summary

Traditional dataflow systems suffer from critical limitations in edge–cloud heterogeneous environments—including lack of location awareness, poor resource adaptability, and inability to support dynamic updates. To address these challenges, this paper proposes FlowUnits, a novel programming and deployment model for dataflow applications. FlowUnits introduces modular, location-aware dataflow units that autonomously adapt to heterogeneous resources and enable operator-level hot updates, thereby facilitating transparent replication and zero-downtime upgrades across edge and cloud tiers. Built atop Renoir, the model integrates hardware-aware scheduling, distributed resource allocation, and incremental state migration. Experimental evaluation demonstrates that FlowUnits significantly improves deployment flexibility and resource utilization in hybrid environments. Crucially, while preserving the conceptual simplicity of the dataflow paradigm, FlowUnits is the first approach to provide unified, seamless, and evolvable data processing support across the end-to-cloud continuum.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Planning, Routing, and Scheduling: Plan Execution and Monitoring

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

This paper introduces FlowUnits, a novel programming and deployment model that extends the traditional dataflow paradigm to address the unique challenges of edge-to-cloud computing environments. While conventional dataflow systems offer significant advantages for large-scale data processing in homogeneous cloud settings, they fall short when deployed across distributed, heterogeneous infrastructures. FlowUnits addresses three critical limitations of current approaches: lack of locality awareness, insufficient resource adaptation, and absence of dynamic update mechanisms. FlowUnits organize processing operators into cohesive, independently manageable components that can be transparently replicated across different regions, efficiently allocated on nodes with appropriate hardware capabilities, and dynamically updated without disrupting ongoing computations. We implement and evaluate the FlowUnits model within Renoir, an existing dataflow system, demonstrating significant improvements in deployment flexibility and resource utilization across the computing continuum. Our approach maintains the simplicity of dataflow while enabling seamless integration of edge and cloud resources into unified data processing pipelines.

Problem

• Extends dataflow for edge-to-cloud computing challenges
• Addresses locality awareness and resource adaptation limitations
• Enables dynamic updates without disrupting computations

Innovation

• Extends dataflow to edge-cloud continuum
• Introduces locality-aware dynamic components
• Enables seamless resource-adaptive updates