Privacy-Preserving Visual Workload Scheduling Across Edge, Fog, and Cloud
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Solution Overview
Problem
Existing visual computing approaches suffer from inefficiencies in resource utilization, high latency, inaccuracy, unreliability, inflexibility, and an inability to scale effectively due to rigid designs and over-reliance on cloud resources for large-scale visual data processing.
Innovation Solution
The visual fog computing system leverages both cloud and edge resources to perform visual computing tasks more efficiently, utilizing a flexible design that supports ad-hoc queries and is highly scalable, thereby improving resource utilization, latency, accuracy, and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If visual computing tasks are performed using cloud resources only, then processing power is sufficient, but latency is high and resource utilization is inefficient
Solution Approach 1:
The patent segments visual computing tasks into different components and distributes them across multiple locations: edge devices perform preliminary processing locally, fog nodes handle intermediate processing, and cloud resources handle complex analytics. This segmentation reduces latency by keeping time-sensitive operations local while maintaining adequate processing power through distributed architecture.
Solution Approach 2:
The patent introduces a spatial dimension to the computing architecture by adding fog nodes between cloud and edge devices. This creates a multi-layered distributed system that processes visual data closer to its source, reducing transmission time and improving overall processing speed without sacrificing computational capacity.
2Device complexity
If visual computing uses a rigid centralized design, then system management is simple, but resource utilization is inefficient and scalability is limited
Solution Approach 1:
The patent implements a dynamic distributed architecture where fog nodes can be added or removed based on demand, and task allocation adapts to available resources. This dynamic design improves resource utilization efficiency by allowing flexible scaling and load balancing while maintaining manageable complexity through standardized protocols and automated orchestration.
3Measurement precision
If all visual data is processed centrally in the cloud, then processing accuracy can be maintained, but network bandwidth is consumed excessively and latency increases
Solution Approach 1:
The patent extracts and processes critical visual data components at the edge and fog layers before transmitting to the cloud. By taking out only the essential data that requires centralized processing while handling preliminary analysis locally, the system maintains processing accuracy for important metrics while dramatically reducing network bandwidth consumption.
4Adaptability or versatility
If visual computing systems are designed to be highly scalable, then they can handle large-scale data, but system complexity and deployment difficulty increase
Solution Approach 1:
The patent designs fog nodes with universal functionality that can perform multiple roles: they can act as edge servers, relay points, or processing nodes depending on configuration. This multi-functionality enables easy scaling across different deployment scenarios without increasing deployment complexity, as the same node type serves various purposes in the distributed architecture.
Data Source
AI summary
In one embodiment, an apparatus comprises a processor to: identify a workload comprising a plurality of tasks; generate a workload graph based on the workload, wherein the workload graph comprises information associated with the plurality of tasks; identify a device connectivity graph, wherein the device connectivity graph comprises device connectivity information associated with a plurality of processing devices; identify a privacy policy associated with the workload; identify privacy level information associated with the plurality of processing devices; identify a privacy constraint based on the privacy policy and the privacy level information; and determine a workload schedule, wherein the workload schedule comprises a mapping of the workload onto the plurality of processing devices, and wherein the workload schedule is determined based on the privacy constraint, the workload graph, and the device connectivity graph. The apparatus further comprises a communication interface to send the workload schedule to the plurality of processing devices.