Virtualized Software Engine for Edge-Cloud Task Distribution
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Solution Overview
Problem
Existing software engines face inefficiencies in distributed computing architectures, particularly in fog computing, where resources are not optimally distributed across edge and cloud, leading to high latency and limited performance in applications like VR, AR, and IoT due to the inefficient distribution of software engine tasks and resources.
Innovation Solution
A system for software engine virtualization and dynamic resource and task distribution across edge and cloud, utilizing a virtual layer that optimizes and allocates memory, bandwidth, and computing resources by virtualizing software engines and dynamically distributing tasks across cloud servers, fog servers, and client devices, leveraging virtualization logic, optimization components, and distribution platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If software engines are hosted on centralized servers, then computing capabilities are improved, but latency increases due to distance between user and data centers
Solution Approach 1:
The patent segments the software engine into multiple virtualized components (graphics rendering, physics simulation, audio processing, AI) that can be distributed across different locations. This allows computing-intensive tasks to remain centralized while latency-sensitive tasks are executed at the edge, resolving the contradiction between centralized computing power and low latency.
Solution Approach 2:
The patent introduces a virtualization layer as an intermediary between the user device and centralized servers. This virtualized software engine layer enables dynamic task distribution and resource allocation, allowing the system to achieve both centralized computing capabilities and low latency by routing tasks appropriately.
2Loss of time
If software engines are moved to client devices, then latency is reduced due to proximity, but performance is limited by local resources
Solution Approach 1:
The patent segments the software engine functionality and assigns different segments to different locations based on their characteristics. Latency-sensitive functions (graphics rendering, audio processing) are executed locally on client devices, while compute-intensive but less time-critical functions (physics simulation, AI) are executed on centralized servers, thus achieving both low latency and high performance.
Solution Approach 2:
The patent adds a spatial dimension to software engine execution by distributing different engine components across multiple locations (edge devices and cloud servers). This multi-dimensional architecture allows the system to simultaneously achieve low latency for local operations and high performance for remote operations.
3Productivity
If resources are distributed across edge and cloud, then resource allocation efficiency is improved, but system complexity increases
Solution Approach 1:
The patent creates a universal virtualized software engine layer that can function across different platforms and locations (edge devices and cloud servers). This standardized virtualization interface simplifies resource management and task distribution, enabling efficient resource allocation without proportionally increasing system complexity.
Solution Approach 2:
The patent implements feedback mechanisms that continuously monitor resource availability, task performance, and system state across the distributed architecture. This feedback enables dynamic adjustment of task distribution and resource allocation, optimizing efficiency while managing complexity through adaptive control rather than static complex configurations.
Data Source
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AI summary
A system and method for enabling software engine virtualization and dynamic resource and task distribution across edge and cloud, comprising at least one cloud server comprising memory and at least one processor, the at least one cloud server hosting at least one cloud engine configured to store and process application data from one or more applications; one or more client devices connected to the cloud server via a network, the one or more client devices hosting at least one local engine configured to store and process application data from the one or more applications and to provide output to users; and a virtual engine hosted across edge and cloud configured to virtualize, via a virtualization logic component, one or more system network components, applications, and engine components, creating a virtual layer connected to the one or more client devices and cloud server via the network.