Dynamic Weighting for Cloud Provisioning Systems
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Solution Overview
Problem
Cloud infrastructure systems face inefficiencies in processing provisioning requests due to their inability to scale well with large clusters of application servers, leading to prolonged processing times and resource inefficiencies, as they typically operate on a first-come-first-served basis.
Innovation Solution
Implementing a dynamically weighted selection protocol within the Service Deployment Infrastructure (SDI) to prioritize provisioning requests based on calculated weights that consider availability, request states, and historical data, allowing for more efficient processing and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If provisioning requests are processed on a first-come-first-served basis, then the system is simple to operate, but the processing time and resource efficiency deteriorate
Solution Approach 1:
The patent implements dynamic request prioritization where request weights are adjusted in real-time based on current system state, infrastructure component availability, and historical performance data. This transforms the static first-come-first-served queue into a dynamic priority-based system that adapts to changing conditions, reducing processing time for critical requests while maintaining operational simplicity through automated weight calculation.
Solution Approach 2:
The system changes the parameter of request processing from sequential FIFO ordering to weight-based priority ordering. Each request is assigned a calculated weight based on multiple factors including infrastructure availability, request type, and historical data, transforming the processing order parameter to optimize overall system efficiency rather than simple chronological order.
2Productivity
If provisioning computer devices process hundreds of requests concurrently, then the system handles high volume, but scaling capability deteriorates due to infrastructure component load
Solution Approach 1:
The patent implements selective request processing where not all queued requests are processed in every provisioning cycle. Instead, requests are processed in priority order based on calculated weights, and lower-priority requests are deferred to subsequent cycles. This partial action approach prevents infrastructure overload while maintaining high throughput for critical requests, enabling better scaling capability.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor infrastructure component performance and availability in real-time. This feedback is used to dynamically adjust request prioritization weights, preventing the system from overwhelming infrastructure components. The feedback loop enables the system to scale effectively by adapting processing volume to current infrastructure capacity.
3Productivity
If dynamic weighting protocol is implemented to prioritize requests, then processing efficiency improves, but system complexity increases
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically calculates request weights and determines processing priorities without external intervention. The weighting protocol uses predefined algorithms that consider infrastructure availability, request characteristics, and historical data, enabling the system to self-optimize its processing queue. This automation improves throughput while managing complexity by eliminating the need for manual queue management.
Solution Approach 2:
The dynamic weighting protocol serves multiple functions simultaneously: it prioritizes requests based on urgency, balances infrastructure load, predicts processing times, and optimizes resource allocation. This multi-functionality achieves high throughput while managing system complexity by consolidating multiple control functions into a single integrated weighting mechanism rather than separate complex control systems.
Data Source
AI summary
In certain embodiments, a Service Deployment Infrastructure (SDI) request engine is disclosed. The SDI request engine can perform tracking, management and provisioning of services subscribed to by users of a computer infrastructure system. The SDI request engine can be deployed to process large volumes of provisioning requests and deliver time critical applications for customers. The SDI request engine can translate each request into a list of tasks of various sizes based on the requirement and configuration of the request, and store each task in a queue. In certain embodiments, the SDI request engine may evaluate a dynamic weight associated with each task to prioritize the processing of tasks, which may improve the overall throughput of request processing.