Real-Time Distributed Service Management with Quantum Resource Selection
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Solution Overview
Problem
Large enterprise organizations face delays in processing requests due to varying volumes of requests and users, leading to capacity issues and performance impacts, which current systems address inadequately by pre-instantiating services, resulting in inefficiencies.
Innovation Solution
A computing platform trains a machine learning model using historical data to identify the appropriate type of computing resource (classical, quantum, or hybrid) for a request, monitors current availability, and executes remediation actions to resolve delays in real-time, optimizing resource allocation and workload balancing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If services are instantiated ahead of time to address delays, then request processing speed is improved, but server capacity and computing resource efficiency deteriorate
Solution Approach 1:
The system dynamically selects between classical computing, quantum computing, or hybrid computing based on real-time analysis of request parameters and current system state. This dynamic adaptation allows the system to optimize both processing speed and resource utilization by matching the computing approach to the specific workload characteristics rather than using a fixed pre-instantiated service configuration.
Solution Approach 2:
The system changes the computing resource allocation parameters based on analyzed request characteristics and system state. By adjusting parameters such as resource type selection (classical vs. quantum vs. hybrid) and resource allocation strategies in real-time, the system achieves optimal performance without the capacity issues associated with over-provisioning for peak demands.
2Reliability
If pre-instantiated services are used to handle varying request volumes, then system availability is improved, but resource allocation efficiency deteriorates
Solution Approach 1:
The system continuously monitors system state including request volume, user count, and resource availability, then uses this feedback to dynamically adjust computing resource allocation. This closed-loop approach ensures high system availability by adapting to changing conditions while optimizing resource allocation efficiency by deploying resources only where and when needed based on actual system state.
Solution Approach 2:
The system performs preliminary analysis of request parameters and system state before selecting computing resources, using machine learning models trained on historical data to predict optimal resource allocation. This preliminary action enables the system to proactively allocate resources efficiently rather than reacting to delays after they occur.
3Measurement precision
If machine learning models are trained on historical data to predict computing resource needs, then workload balancing accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system uses machine learning models that learn from historical data patterns to create predictive copies of future system state and resource needs. By training on past data, the model generates simplified predictive representations that capture essential workload characteristics without requiring complex real-time analysis, thus achieving high prediction accuracy while managing data processing complexity.
Data Source
AI summary
Arrangements for resource balancing and inferential service are provided. A computing platform may receive monitoring data including current availability data of a plurality of computing resources associated with different types of computing systems, and a first request including parameters of the first request. A machine learning model may receive, as inputs, the parameters of the first request and the monitoring data and may be executed to output a particular type of computing system to process the first request. The computing platform may determine whether a delay exists with the particular type of computing system. If not, the first request may be sent to the particular type of computing system for processing. If a delay exists, the delay may be evaluated to identify a cause and a remediation action may be identified and executed. The first request may then be sent to the particular type of computing system for processing.