Orchestration Service Triggering Workload Migration Between Cloud and Local Resources
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Solution Overview
Problem
Current cloud monitoring tools are inadequate for effectively managing per-user cloud resource costs across multiple cloud service providers, as they cannot track resource usage across providers, map usage to specific users or client systems, or push policies to manage costs.
Innovation Solution
A system comprising a client agent on client systems and a cloud-based orchestration service that collects telemetry data from client systems and cloud resource usage data from multiple cloud providers to determine expected usage and trigger workload migration from cloud-based to local resources, or vice versa, based on predetermined thresholds or rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cloud monitoring tools are used to track resource usage, then cloud resource usage can be monitored, but they cannot track usage across multiple cloud service providers or map usage to specific users
Solution Approach 1:
The system segments cloud resource monitoring by implementing provider-specific monitoring components that each track resources for a particular cloud service provider. These segmented monitoring units are then aggregated through a central orchestration service that consolidates data across multiple providers and maps usage to specific users, thereby achieving both precise tracking and multi-provider versatility.
Solution Approach 2:
The orchestration service acts as an intermediary layer between individual cloud service providers and the enterprise IT management system. This intermediary consolidates resource usage data from multiple providers, standardizes the information format, and presents unified multi-provider tracking capabilities along with user-level cost allocation, resolving the contradiction between precise monitoring and versatile tracking.
2Power
If workloads are run on cloud-based resources, then additional computing power and storage are available, but costs increase based on consumption
Solution Approach 1:
The system dynamically adjusts workload placement between cloud and local resources based on real-time monitoring of usage patterns, cost thresholds, and performance requirements. Workloads are automatically migrated to or from cloud services according to changing conditions, enabling the system to optimize the balance between accessing additional computing power and controlling consumption-based costs.
Solution Approach 2:
The orchestration service implements continuous feedback loops that monitor cloud resource consumption, calculate costs, and trigger automated workload migration decisions. When cost thresholds are approached or exceeded, the system receives feedback and automatically migrates workloads back to local resources, thereby maintaining access to cloud computing power when needed while preventing uncontrolled cost increases.
3Loss of energy
If workloads are migrated automatically based on usage thresholds, then cloud resource costs are optimized, but complexity of the management system increases
Solution Approach 1:
The orchestration service implements self-service automation where the system automatically monitors its own performance, detects when cost thresholds are approached, and executes workload migration decisions without requiring complex manual configuration or intervention. This self-managing approach optimizes cloud costs while containing system complexity through automated decision-making algorithms and pre-configured migration policies.
4Productivity
If per-user cost tracking is implemented across multiple cloud providers, then cloud resource costs can be managed efficiently, but difficulty of detecting and measuring usage increases
Solution Approach 1:
The orchestration service implements a universal measurement framework that standardizes the detection and tracking of cloud resource usage across multiple different cloud service providers. This multi-functional system performs both provider-specific monitoring and aggregated user-level cost calculation simultaneously, enabling efficient per-user cost tracking despite the complexity of measuring usage across diverse cloud platforms.
Data Source
AI summary
Embodiments of systems and methods are provided to trigger migration of a workload from cloud-based resources to local resources, or vice versa. In the disclosed embodiments, an orchestration service receives telemetry data from a client system associated with a user and cloud resource usage data corresponding to the user from a plurality of cloud service providers. Before the end of each cloud computing service billing cycle, the orchestration service: uses the cloud resource usage data and/or the telemetry data to determine a cloud resource usage, which is expected for the user at the end of the cloud computing service billing cycle; generates a trigger to migrate the user's workload from cloud-based resources to local resources, or vice versa, based on the expected cloud resource usage; and initiates migration of the user's workload if a trigger is generated. As such, the orchestration service can be used to effectively manage per-user cloud resource costs.