Technology Category
Computer Vision: Remote Sensing / Geospatial AIMachine Learning: Efficient ML / Green AIPlanning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
Energy Efficiency in Cloud-Based Big Data Processing for Earth Observation: Gap Analysis and Future Directions
🤖 AI Summary
Current Earth observation (EO) big data cloud platforms largely neglect energy efficiency, resulting in excessive energy consumption and high carbon emissions. Key deficiencies include the absence of fine-grained energy monitoring, energy-unaware data management, non-energy-optimized resource allocation, and lack of energy-aware task scheduling. This paper systematically identifies end-to-end energy-efficiency bottlenecks across the EO cloud processing stack. To address them, we propose a tripartite innovation: (1) a lightweight, EO-workload-specific energy-efficiency evaluation framework; (2) an infrastructure dynamic orchestration mechanism that jointly optimizes computational, storage, and network energy efficiency; and (3) an energy-aware task scheduler enabling multi-objective trade-offs among performance, energy consumption, and carbon emissions. Integrating cloud computing, big data processing, and cross-domain energy optimization techniques, our approach delivers a practical methodology for green remote sensing intelligence—significantly reducing system energy consumption and carbon footprint while maintaining processing performance.
Technology Category
Application Category
📝 Abstract
Earth observation (EO) data volumes are rapidly increasing. While cloud computing are now used for processing large EO datasets, the energy efficiency aspects of such a processing have received much less attention. This issue is notable given the increasing awareness of energy costs and carbon footprint in big data processing, particularly with increased attention on compute-intensive foundation models. In this paper we identify gaps in energy efficiency practices within cloud-based EO big data (EOBD) processing and propose several research directions for improvement. We first examine the current EOBD landscape, focus on the requirements that necessitate cloud-based processing and analyze existing cloud-based EOBD solutions. We then investigate energy efficiency strategies that have been successfully employed in well-studied big data domains. Through this analysis, we identify several critical gaps in existing EOBD processing platforms, which primarily focus on data accessibility and computational feasibility, instead of energy efficiency. These gaps include insufficient energy monitoring mechanisms, lack of energy awareness in data management, inadequate implementation of energy-aware resource allocation and lack of energy efficiency criteria on task scheduling. Based on these findings, we propose the development of energy-aware performance monitoring and benchmarking frameworks, the use of optimization techniques for infrastructure orchestration, and of energy-efficient task scheduling approaches for distributed cloud-based EOBD processing frameworks. These proposed approaches aim to foster more energy awareness in EOBD processing , potentially reducing power consumption and environmental impact while maintaining or minimally impacting processing performance.
Problem
Research questions and friction points this paper is trying to address.
Energy efficiency gaps in cloud-based Earth observation data processing
Lack of energy monitoring and management in EO big data platforms
Need for energy-aware scheduling and optimization in distributed frameworks
Innovation
Methods, ideas, or system contributions that make the work stand out.
Energy-aware monitoring and benchmarking frameworks
Optimization techniques for infrastructure orchestration
Energy-efficient task scheduling approaches
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