Workload Component Migration to Edge Infrastructure
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Solution Overview
Problem
Current workload management systems face challenges in optimizing the distribution of workload components across cloud and edge infrastructure, leading to suboptimal performance and resource inefficiencies due to factors like latency and computational overhead.
Innovation Solution
A system that employs graph representations and global optimization processes, such as genetic algorithms, to identify and evaluate potential migration plans for workload components from cloud to edge infrastructure, balancing performance gains with cost considerations, and enabling co-migration of multiple workloads to shared edge infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If workload components are migrated to edge infrastructure, then workload performance is improved, but device complexity increases
Solution Approach 1:
The system segments workload components into migratable units that can be selectively moved between cloud and edge infrastructure. Graph representations divide workloads into nodes and edges, enabling granular migration decisions for individual components rather than migrating entire workloads, thus improving performance while managing complexity.
Solution Approach 2:
The system implements dynamic workload migration where components can be moved between cloud and edge infrastructure based on real-time performance requirements, cost considerations, and resource availability. This dynamic approach allows the system to adapt to changing conditions rather than using static deployment configurations.
2Loss of energy
If multiple workloads are co-migrated to shared edge infrastructure, then resource costs are reduced, but reliability may deteriorate due to resource sharing
Solution Approach 1:
The system merges multiple workloads onto shared edge infrastructure components to consolidate resources and reduce costs. By evaluating migration plans that group multiple workloads together, the system achieves cost efficiencies through resource sharing while using simulation to ensure performance requirements are met.
Solution Approach 2:
The system uses simulation-based evaluation to provide feedback on the expected performance impact of co-migration decisions. Before implementing migrations, the system simulates workload interactions on shared infrastructure to predict performance outcomes, allowing it to adjust migration plans to maintain reliability while achieving cost savings.
3Productivity
If workload components are redistributed across infrastructure, then productivity is improved, but device complexity increases
Solution Approach 1:
The system introduces a migration management intermediary that handles the complexity of redistributing workload components. This intermediary evaluates multiple potential migration plans, simulates their impacts, and selects optimal migration strategies, thereby improving productivity while shielding operators from the underlying complexity of component-level migration management.
Solution Approach 2:
The system performs preliminary evaluation and simulation of migration plans before implementing actual redistributions. By pre-assessing the impact of potential migrations on productivity and performance, the system can make informed decisions about component redistribution, improving productivity while managing complexity through advance planning.
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. Migration of the workload components may reduce the computing resource cost for performing various workloads.