Service Request Bundling for Contiguous Downtime Scheduling
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Solution Overview
Problem
Existing systems face inefficiencies in scheduling multiple service requests that require downtime, leading to prolonged and non-contiguous disruptions due to sequential execution, which complicates scheduling and increases overall downtime.
Innovation Solution
A unified interface bundles multiple service requests into a single execution plan using vertical bundling and modularization, leveraging machine learning models to rank and sequence services to minimize downtime by identifying parallelizable processes and scheduling them contiguously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple service requests are executed sequentially, then each service can be completed individually with proper resource allocation, but the overall downtime is prolonged and becomes non-contiguous
Solution Approach 1:
The patent combines multiple service requests into a single bundled execution unit. The service request bundler groups multiple services that require downtime into one consolidated request, allowing them to be executed together in a single contiguous downtime window rather than sequentially across multiple separate windows.
Solution Approach 2:
The system performs preliminary analysis to identify parallelizable services and determines the optimal execution plan before actual execution. The service analyzer evaluates service dependencies and identifies which services can run in parallel, preparing an optimized execution strategy in advance to minimize total downtime.
2Reliability
If services are executed sequentially to ensure proper resource allocation, then resource conflicts are avoided, but scheduling complexity increases
Solution Approach 1:
The patent introduces a service analyzer as an intermediary component that sits between service requestors and the execution system. This analyzer pre-evaluates service dependencies, identifies parallelizable services, and generates an optimized execution plan, thereby simplifying the scheduling complexity while maintaining resource allocation reliability.
Solution Approach 2:
The system segments services into different execution groups based on their dependency relationships and parallelizability. Services are divided into independent groups that can be executed in parallel, with the service analyzer determining the optimal segmentation to reduce scheduling complexity while ensuring proper resource allocation.
3Loss of time
If multiple services are bundled and executed in parallel, then overall downtime is reduced, but the system requires sophisticated analysis to identify parallelizable processes
Solution Approach 1:
The service analyzer acts as an intermediary that performs sophisticated dependency analysis and parallelizability assessment. It evaluates service relationships, identifies which services can safely run in parallel, and generates an optimized execution plan, thereby enabling time reduction through parallel execution without requiring the end system to implement complex analysis logic.
Solution Approach 2:
The system performs preliminary dependency analysis and parallelizability assessment before execution. The service analyzer evaluates all service relationships in advance, identifies optimal parallel execution groups, and prepares an execution plan that maximizes time efficiency while managing analysis complexity in a controlled manner.
Data Source
AI summary
In an example embodiment, multiple service requests are bundled into a single bundle via the vertical bundling of the service requests. This involves modularizing the services into subcomponents, identifying common processes and identifying the modularized processes that can run in parallel, then ranking the modularized processes to create an execution plan that minimizes downtime and also schedules downtime in a single contiguous block. The execution plan represents an optimized executable sequence that can contain both related and non-related services in a single bundle for fulfillment. It also represents a blueprint of services requested by the user from a service catalog.