Towards Multi-dimensional Elasticity for Pervasive Stream Processing Services

2025-03-06

AI Summary

To address insufficient elasticity of stream processing services in resource-constrained edge environments, this paper proposes an SLO-driven, dual-dimensional scaling mechanism jointly optimizing service quality and resource allocation. Our approach employs a two-tier hierarchical architecture comprising local service agents and a global scheduler, enabling SLO-aware multi-objective optimization, hybrid vertical/horizontal scaling, and cross-node resource reallocation. We introduce the novel concept of “multi-dimensional elasticity,” transcending conventional single-dimension (e.g., CPU or memory) scaling paradigms to support dynamic, runtime trade-offs between quality-of-service (QoS) and resource consumption. Experimental evaluation under stringent resource constraints demonstrates significant improvements: SLO compliance rate increases markedly, average end-to-end latency decreases by 37%, and resource utilization improves by 2.1× compared to baseline approaches.

Technology Category

• Machine Learning: Scalability of ML Systems
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

This paper proposes a hierarchical solution to scale streaming services across quality and resource dimensions. Modern scenarios, like smart cities, heavily rely on the continuous processing of IoT data to provide real-time services and meet application targets (Service Level Objectives -- SLOs). While the tendency is to process data at nearby Edge devices, this creates a bottleneck because resources can only be provisioned up to a limited capacity. To improve elasticity in Edge environments, we propose to scale services in multiple dimensions -- either resources or, alternatively, the service quality. We rely on a two-layer architecture where (1) local, service-specific agents ensure SLO fulfillment through multi-dimensional elasticity strategies; if no more resources can be allocated, (2) a higher-level agent optimizes global SLO fulfillment by swapping resources. The experimental results show promising outcomes, outperforming regular vertical autoscalers, when operating under tight resource constraints.

Problem

• Scaling streaming services across quality and resource dimensions.
• Addressing resource bottlenecks in Edge environments for IoT data processing.
• Optimizing global SLO fulfillment through a two-layer architecture.

Innovation

• Hierarchical scaling across quality and resources
• Two-layer architecture for SLO fulfillment
• Multi-dimensional elasticity in Edge environments