Edge-Cloud Homomorphic Compression Under Battery-Aware Workload Shifting
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Solution Overview
Problem
Existing deep learning-based compression systems lack energy efficiency, leading to inefficient energy usage and premature device shutdowns in resource-constrained environments like edge computing devices and IoT sensors, without dynamically balancing tasks across edge and cloud resources.
Innovation Solution
An energy-aware homomorphic compression system using a split variational autoencoder architecture that dynamically adjusts compression parameters and workload distribution based on real-time energy availability, incorporating resource monitoring and edge-cloud coordination to extend operational lifespan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning-based compression systems operate with fixed or static compression strategies, then data compression and restoration quality is maintained, but energy consumption increases and device operational lifespan decreases
Solution Approach 1:
The system implements dynamic compression strategies that adapt to real-time energy availability. The edge-cloud coordination layer continuously monitors battery state and dynamically adjusts compression parameters, transitioning from static to dynamic operation modes. This allows the system to maintain high compression quality when energy is abundant while reducing quality requirements when energy is constrained, thereby resolving the contradiction between compression quality and energy consumption.
Solution Approach 2:
The system changes compression parameters based on energy availability. The coordination layer modifies compression ratios, model complexity, and processing depth as adjustable parameters in response to battery state changes. By dynamically altering these parameters, the system optimizes the trade-off between maintaining compression quality and reducing energy consumption during different operational phases.
2Ease of manufacture
If deep learning-based compression systems use fixed compression strategies, then implementation simplicity is maintained, but device operational lifespan decreases due to inefficient energy usage
Solution Approach 1:
The system segments the compression functionality into modular components distributed across edge and cloud devices. The variational autoencoder is split between edge computing devices and cloud infrastructure, with each segment handling specific compression tasks. This modular segmentation allows independent optimization of each component's energy efficiency while maintaining overall system simplicity through standardized interfaces and protocols.
Solution Approach 2:
The system incorporates feedback mechanisms where the coordination layer continuously monitors battery state and energy consumption, then adjusts compression strategies accordingly. This closed-loop feedback enables the system to automatically optimize for extended operational lifespan without requiring complex manual configuration, balancing implementation simplicity with energy efficiency through adaptive control.
3Device complexity
If workload is not dynamically balanced across edge and cloud resources, then system complexity is reduced, but energy efficiency decreases and resource utilization becomes uneven
Solution Approach 1:
The system implements dynamic workload balancing through the edge-cloud coordination layer, which continuously monitors resource availability and energy states. Workload distribution is adjusted in real-time based on changing conditions, transitioning from static to dynamic task allocation. This enables the system to optimize energy efficiency by routing tasks to the most appropriate execution location (edge or cloud) while adapting to resource constraints.
Solution Approach 2:
The coordination layer acts as an intermediary between edge devices and cloud infrastructure, managing workload distribution and energy optimization. This intermediary component simplifies the overall system architecture by centralizing the complexity of dynamic workload balancing, allowing edge and cloud components to focus on their core functions while the coordinator handles the complex decisions about task allocation and energy management.
4Ease of operation
If compression parameters are not dynamically adjusted based on energy availability, then system operation is simpler, but energy waste increases leading to premature device shutdowns
Solution Approach 1:
The system implements self-service through automated energy-aware compression parameter adjustment. The coordination layer autonomously monitors battery state and adjusts compression parameters without requiring manual intervention. This self-adjusting mechanism maintains operational simplicity by automating the complex task of energy optimization, allowing the system to adapt to energy constraints while minimizing energy waste through intelligent parameter selection.
Solution Approach 2:
The system uses feedback from battery state monitoring to dynamically adjust compression parameters. The coordination layer receives feedback about energy availability and automatically modifies compression settings to prevent energy waste. This feedback-driven approach maintains operational simplicity by automating the adjustment process, eliminating the need for manual parameter tuning while preventing energy waste through real-time adaptation.
Data Source
AI summary
A distributed system and method for compressing and restoring data across edge computing devices and cloud infrastructure is disclosed. The system dynamically adjusts compression based on available computing resources, network conditions, and now energy constraints. Edge devices monitor power consumption and battery levels, optimizing compression parameters to extend battery life while maintaining data quality. A workload scheduler prioritizes tasks based on energy availability, offloading intensive processing to cloud infrastructure when necessary. The system utilizes an energy-aware coordination layer to balance workloads across multiple devices, ensuring efficient data flow and long-term operational stability. Homomorphic operations allow secure distributed processing on compressed data, while an adaptive neural upsampler enhances reconstructed outputs. By integrating energy optimization, the system improves performance and longevity of edge devices in power-limited environments.