Edge-Based Resource Spin-Up for Cloud Latency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Centralized cloud computing models often result in delayed user experiences due to the distance between compute resources and end-users, and lack flexibility in routing requests to alternative clouds during provider difficulties, leading to suboptimal performance and responsiveness.
Innovation Solution
Implementing edge-based resource spin-up by distributing virtualized instances closer to users and dynamically routing requests to multiple cloud environments based on specified criteria, such as proximity and resource availability, to enhance user experience and ensure uninterrupted service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If compute resources are centralized in a data center, then resource management is simplified, but user response time deteriorates due to distance
Solution Approach 1:
The patent segments the centralized cloud computing model by deploying virtualized compute instances at the network edge (access networks, aggregation networks) rather than concentrating all resources in a centralized data center. This segmentation allows resources to be distributed closer to users while maintaining centralized management capabilities, thereby reducing response time without completely eliminating resource management simplicity.
Solution Approach 2:
The patent introduces a new spatial dimension for resource deployment by moving compute instances from the traditional centralized data center location to the network edge locations (access networks, aggregation networks). This dimensional shift in resource placement enables proximity to users while maintaining centralized orchestration, resolving the contradiction between centralization benefits and response time requirements.
2Device complexity
If applications are deployed to a single cloud, then deployment complexity is reduced, but service reliability deteriorates when the cloud experiences difficulties
Solution Approach 1:
The patent implements multi-cloud deployment capability where the same application can be deployed across multiple cloud environments (public cloud, private cloud, hybrid cloud). The system provides universal routing functionality that can direct requests to any of these cloud environments based on specified criteria, enabling both simplified deployment through a unified interface and improved reliability through redundancy across multiple clouds.
Solution Approach 2:
The patent changes the routing parameter from fixed (single cloud) to dynamic (multiple clouds with selectable criteria). The routing mechanism allows specification of multiple cloud destinations and enables dynamic selection based on parameters such as cost, performance, and availability, thereby improving service reliability while maintaining deployment simplicity through centralized routing control.
3Loss of time
If compute resources are distributed at the network edge, then user response time is improved, but resource management complexity increases
Solution Approach 1:
The patent introduces a routing mechanism as an intermediary layer between users and distributed edge resources. This routing component manages the complexity of resource distribution by providing a unified interface for request routing based on specified criteria (cost, performance, availability), thereby enabling edge-based resource deployment with improved response times while abstracting away the management complexity through intelligent routing decisions.
4Quantity of substance
If requests are routed to distant centralized clusters, then resource consolidation is achieved, but computational performance for intensive tasks deteriorates
Solution Approach 1:
The patent segments the monolithic centralized compute cluster into distributed virtualized instances deployed across multiple network locations (access networks, aggregation networks). This segmentation enables resource consolidation benefits to be maintained through virtualization while improving computational performance for intensive tasks by placing compute resources closer to users, reducing network latency and improving responsiveness.
Data Source
AI summary
Aspects of the present invention include distributing new resources closer to end-users which are making increased demands by spinning-up additional virtualized instances (as part of a cloud provisioning) within servers that are physically near to the network equipment (i.e., web servers, switches, routers, load balancers) that are receiving the requests.