Compute Gravity Workload Distribution Across Cloud and Edge Nodes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional cloud computing paradigms break down under high user demand, especially for large-scale computing systems with complex artificial intelligence components, as they struggle with latency, load times, and data processing efficiency due to inadequate distribution of computation workloads across different computing nodes.
Innovation Solution
Calculating the 'compute gravity' of each computing node within differing paradigms to distribute computation workloads effectively among cloud and client network nodes, utilizing fog computing to cache results on content delivery network edge servers and optimize processing across multiple paradigms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computation workload is concentrated in traditional cloud computing paradigms, then centralized processing capability is maintained, but system performance degrades under high user demand due to latency and load time issues
Solution Approach 1:
The patent segments the centralized cloud computing workload into distributed computing tasks across multiple paradigms (cloud, fog, edge, and client network computing nodes). By dividing the computation workload and distributing it geographically and architecturally, the system reduces network latency while maintaining processing capability, directly resolving the contradiction between centralized processing and low latency.
Solution Approach 2:
The patent introduces a new dimensional approach by integrating multiple computing paradigms (cloud, fog, edge, client network) into a multi-paradigm architecture. This dimensional expansion allows computation to occur at different levels of the network hierarchy simultaneously, reducing latency without sacrificing processing power by moving from a single-dimensional cloud model to a multi-dimensional distributed model.
2Loss of time
If computation workload is distributed across multiple computing nodes, then network latency is reduced, but system complexity increases due to workload distribution management
Solution Approach 1:
The patent creates a universal compute gravity calculation mechanism that works across all computing paradigms (cloud, fog, edge, client network). This unified approach to workload distribution uses a common methodology (compute gravity based on available capacity, request volume, and network distance) to manage diverse computing nodes, reducing the complexity that would otherwise arise from managing each paradigm separately.
Solution Approach 2:
The patent implements feedback mechanisms where computing nodes continuously report their available capacity, and the system dynamically adjusts workload distribution based on real-time conditions. This feedback loop enables adaptive workload management that automatically optimizes distribution across the multi-paradigm architecture, reducing the complexity of manual or static workload management.
3Productivity
If more computing nodes are utilized across different paradigms, then processing capacity increases, but coordination and communication overhead increases
Solution Approach 1:
The patent applies local quality by having each computing node calculate its own compute gravity based on local conditions (available capacity, request volume, network distance). This decentralized approach allows nodes to make autonomous decisions about workload acceptance, reducing the need for extensive coordination and communication overhead while still achieving optimal distributed processing across multiple paradigms.
Data Source
AI summary
Distributing computation workload among computing nodes of differing computing paradigms is provided. Compute gravity of each computing node in a cloud computing paradigm and each computing node in a client network computing paradigm within an Internet of Systems is calculated. Each component part of an algorithm is distributed to an appropriate computing node of the cloud computing paradigm and client network computing paradigm based on calculated compute gravity of each respective computing node within the Internet of Systems. Computation workload of each component part of the algorithm is distributed to a respective computing node of the cloud computing paradigm and the client network computing paradigm having a corresponding component part of the algorithm for processing.