Quantum-Classical Graph Orchestration for SLA-Aware Resource Allocation
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Solution Overview
Problem
Current technologies lack an intelligent orchestration engine to efficiently allocate resources for hybrid quantum-classical workflows, leading to issues like under-subscription and over-subscription, and fail to satisfy Service Level Agreements (SLAs) in quantum computing environments.
Innovation Solution
An intelligent orchestration engine that estimates resource allocation for hybrid quantum-classical workflows by leveraging experimental observations and transforming computation graphs to satisfy SLA constraints, utilizing both classical and quantum computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual resource specification is used in manifest files, then resource allocation is simple to implement, but it leads to under-subscription and over-subscription of resources
Solution Approach 1:
The system enables self-service through automated resource allocation where the orchestration engine automatically determines and assigns quantum computing resources based on workflow requirements, eliminating manual specification and achieving both ease of operation and accurate resource subscription
Solution Approach 2:
The system implements feedback mechanisms by monitoring resource usage and performance metrics, then using this information to dynamically adjust resource allocation decisions, preventing both under-subscription and over-subscription through continuous optimization
2Ease of manufacture
If resources are manually specified in manifest files, then implementation is straightforward, but Service Level Agreements cannot be satisfied
Solution Approach 1:
The system performs preliminary action by pre-calculating and optimizing resource allocation configurations before workflow execution, using experimental observations to predict resource requirements and ensure SLA constraints are met from the outset
Solution Approach 2:
The system applies parameter changes by dynamically adjusting resource allocation parameters based on workflow characteristics and SLA requirements, transforming fixed manual specifications into adaptive allocations that satisfy service level agreements
3Reliability
If intelligent orchestration is implemented, then resource allocation accuracy improves, but system complexity increases
Solution Approach 1:
The system uses an intermediary approach by introducing an orchestration engine that mediates between workflow requirements and resource allocation, managing the complexity internally while presenting a simplified interface to users
Solution Approach 2:
The system applies segmentation by dividing the orchestration functionality into modular components including workflow analysis, resource estimation, and allocation decision-making, making the complex system manageable and maintainable
Data Source
AI summary
One example method includes receiving a computation workflow defined by a graph that includes quantum computing nodes, receiving a catalogue of quantum computing instances that are available in a hybrid classic-quantum computation infrastructure, transforming the graph to create a first graph transformation, and each of the quantum computing nodes is assigned a respective candidate resource allocation that identifies candidate resources operable to execute a respective quantum algorithm associated with that quantum computing node, and the transforming is performed using information from the catalogue, and optimizing the computation workflow by selecting, for each of the quantum computing nodes, a resource from the candidate resource allocation associated with that quantum computing node, and the optimizing includes transforming the first graph transformation to create a second graph transformation that specifies the selected resources for each node.