Endpoint Telemetry for Adaptive Workload Migration Across Cloud Services
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Solution Overview
Problem
Existing systems lack efficient methods for migrating workloads across cloud services based on endpoint performance, leading to suboptimal resource utilization and user experience.
Innovation Solution
An Information Handling System (IHS) that collects telemetry data to determine performance differences and migrates workloads to cloud services with higher or lower performance based on predefined thresholds, using machine learning models and automation to optimize workload distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If workloads are migrated to higher performance cloud services, then user experience and performance are improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The system dynamically adjusts workload migration decisions based on real-time telemetry data from the IHS, transitioning from static to dynamic resource allocation. The workload migration is determined by comparing IHS capability against utilization metrics, allowing the system to adapt cloud service selection to current endpoint conditions
Solution Approach 2:
The system changes the performance parameter of cloud services based on IHS telemetry data. When IHS capability significantly exceeds utilization (difference greater than first threshold), workloads are migrated to lower performance cloud services. When the difference is within acceptable ranges, higher performance services are selected, thus optimizing resource utilization while maintaining user experience
2Productivity
If workload migration decisions are made dynamically, then resource utilization is optimized, but system complexity increases
Solution Approach 1:
The system performs self-service by automatically collecting telemetry data from the IHS, analyzing capability versus utilization differences, and making workload migration decisions without manual intervention. The IHS itself provides the data needed for decision-making, reducing the complexity burden on external systems
Solution Approach 2:
The system implements feedback loops by continuously monitoring IHS telemetry data and using this information to adjust workload migration decisions. The telemetry data provides feedback on IHS capability and utilization, which feeds back into the migration decision process to optimize resource allocation
Data Source
AI summary
Systems and methods for migration of workloads across cloud services based upon endpoint performance are described. In some embodiments, an endpoint Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: receive telemetry data indicative of a utilization of the IHS during execution of a workload by a first cloud service; determine a difference between a capability of the IHS and the utilization; and at least one of: (a) in response to the difference being greater than a first threshold, migrate the workload to a second cloud-based service with higher performance than the first cloud service; or (b) in response to the difference being smaller than a second threshold, migrate the workload to a third cloud-based service with lower performance than the first cloud service.