Intelligent Orchestration System Hybrid Cloud Capacity Prediction
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Solution Overview
Problem
Existing orchestration techniques in traditional data center environments lack self-recovery, awareness of environment capacity and availability, and self-restoration capabilities, leading to workflow failures and inefficiencies due to insufficient infrastructure, heavy loads, network disruptions, and maintenance issues.
Innovation Solution
A method and system for intelligent orchestration in a hybrid cloud environment that monitors the capacity and availability of end-point computing devices, predicts the ability to execute orchestration workflows with a confidence score using an intelligent database, and adjusts execution accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional orchestration workflows are executed in data center environments, then automation and resource management are improved, but workflow failures occur due to insufficient capacity, heavy loads, network disruptions, and maintenance issues
Solution Approach 1:
The system performs preliminary actions by monitoring capacity and availability metrics before executing orchestration workflows. It queries the intelligent database to predict potential failures and obtain confidence scores in advance, allowing the system to prepare alternative actions or warnings before the actual workflow execution, thereby preventing failures rather than reacting to them afterward
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring capacity and availability of end-point computing devices, querying historical execution data from the intelligent database, and using this information to adjust workflow execution decisions. The feedback loop includes obtaining confidence scores that indicate the likelihood of successful execution, which feeds back into the decision-making process for whether to proceed with workflow execution
2Reliability
If orchestration workflows are re-executed after failures to restore functionality, then service restoration is achieved, but time loss and computational resource wastage increase
Solution Approach 1:
By querying the intelligent database for historical execution data and obtaining confidence scores before workflow execution, the system performs preliminary assessment to predict potential failures. This allows the system to avoid re-executing workflows that are likely to fail again, saving time and computational resources that would otherwise be wasted on doomed retry attempts
Solution Approach 2:
The system provides self-service capabilities by automatically monitoring capacity and availability, querying the intelligent database for predictions, and making autonomous decisions about workflow execution without requiring manual intervention. This self-service approach reduces the time and effort needed for service restoration by eliminating manual troubleshooting and re-execution decisions
3Reliability
If capacity monitoring and predictive querying are implemented before workflow execution, then execution success probability is improved, but system complexity and processing overhead increase
Solution Approach 1:
The system introduces an intermediary component - the intelligent database - that stores historical execution data and provides predictive queries. This intermediary handles the complexity of data analysis and prediction algorithms, allowing the orchestration system to obtain confidence scores without implementing complex prediction logic itself, thereby reducing the complexity burden on the main orchestration system while still achieving improved execution success rates
Data Source
AI summary
This disclosure relates generally to orchestration, and more particularly to method and system for performing intelligent orchestration within a hybrid cloud environment. In one embodiment, a method of performing intelligent orchestration within a hybrid cloud environment including a plurality of end-point computing devices is disclosed. The method may include monitoring a capacity and an availability of each of the plurality of end-point computing devices, predicting an ability to successfully execute a requested orchestration workflow along with a confidence score, based on the capacity and the availability, by querying an intelligent database that includes historical execution data of past orchestration workflows, and effecting an execution of the requested orchestration workflow based on the ability and the confidence score.