Workload Migration via Graph Genetic Algorithms
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Solution Overview
Problem
Current workload management systems face challenges in optimizing workload distribution across cloud and edge infrastructure, leading to suboptimal performance and resource inefficiencies due to factors like latency, disruption susceptibility, and overhead costs.
Innovation Solution
A system employing graph representations and genetic algorithms to identify and evaluate potential migration plans for workload components, allowing for temporary or permanent redistribution between cloud and edge infrastructure to enhance performance and reduce disruptions, while considering cost and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If workload components are distributed across cloud and edge infrastructure, then service performance and latency are improved, but system complexity and management difficulty increase
Solution Approach 1:
The system segments workload components into distinct categories (cloud-hosted and edge-hosted) and manages them through separate graph representations. This segmentation allows independent optimization of each workload type while maintaining overall system coherence through the unified management framework.
Solution Approach 2:
The system dynamically adjusts workload distribution by evaluating migration plans between cloud and edge infrastructure. The graph representations are continuously updated to reflect current system state, enabling adaptive workload management that responds to changing conditions without requiring manual reconfiguration.
2Reliability
If workload components are migrated between cloud and edge infrastructure, then disruption resilience is improved, but migration overhead and operational complexity increase
Solution Approach 1:
The system performs preliminary evaluation of migration plans using graph representations before actual migration occurs. By assessing potential disruptions and migration impacts in advance, the system can prepare appropriate migration strategies and avoid unnecessary migrations, reducing operational complexity while maintaining resilience.
Solution Approach 2:
The system continuously monitors infrastructure health and workload performance, using this feedback to update graph representations and trigger migration plans when disruptions are detected. This closed-loop approach automates resilience management, reducing operational complexity through systematic feedback-driven decisions.
3Productivity
If comprehensive workload distribution optimization is implemented, then resource utilization efficiency is improved, but computational overhead and processing time increase
Solution Approach 1:
The system divides the optimization problem into separate graph representations for cloud and edge workloads, allowing parallel processing and independent optimization. This segmentation reduces the computational complexity of analyzing the entire workload distribution, improving processing time while maintaining resource utilization efficiency.
Solution Approach 2:
The system dynamically adjusts optimization parameters based on current system conditions, such as changing the weightings in the objective function or modifying graph representation update frequencies. This adaptive parameter adjustment allows the system to achieve high resource utilization efficiency without excessive computational overhead by tuning parameters to current operational context.
Data Source
AI summary
Methods and systems for managing workloads are disclosed. The workloads may be supported by operation of workload components that are hosted by infrastructure. The hosted locations of the workload components by the infrastructure may impact the performance of the workloads. To manage performance of the workloads, an optimization process may be performed to identify a migration plan for migrating some of the workload components to other infrastructure such as shared edge infrastructure and cloud infrastructure temporarily or permanently. Migration of the workload components may reduce the computing resource cost for performing various workloads and/or reduce workload performance disruptions.