Distributed Multi-Tier Computing Load Prediction and Capacity Planning
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Solution Overview
Problem
Managing complex distributed multi-tiered computing environments with dynamic loads and efficiently provisioning applications across diverse devices is challenging due to the complexity and variability of these ecosystems, making it difficult to determine optimal placement and resource allocation.
Innovation Solution
A method and system utilizing a global controller to manage load predictions by obtaining offline data from local controllers, adjusting scheduling policies, and initiating infrastructure adjustments based on load management predictions to optimize resource allocation and provisioning across multiple domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If applications are provisioned in complex distributed multi-tiered computing environments with dynamic loads, then service coverage and adaptability improve, but determining optimal placement and resource allocation becomes difficult
Solution Approach 1:
The system segments the complex distributed computing environment into multiple hierarchical levels (global controller, domain controllers, local controllers) and divides resource management into separate functional modules (load prediction, scheduling policy adjustment, infrastructure adjustment). This segmentation allows each component to manage specific aspects independently, reducing overall system complexity while maintaining comprehensive service coverage.
Solution Approach 2:
The system performs load management predictions in advance using offline data before actual load occurs. By predicting future load conditions and adjusting scheduling policies proactively, the system prepares optimal resource allocation strategies beforehand, simplifying real-time decision-making in complex environments.
2Productivity
If scheduling policies are adjusted dynamically to handle varying loads, then system performance improves, but control complexity increases
Solution Approach 1:
The system implements dynamic scheduling policies that automatically adapt to changing load conditions. Load prediction events trigger automated adjustments to scheduling parameters based on predicted future states, allowing the system to respond dynamically to varying demands without manual intervention, thus improving performance while managing control complexity through automation.
Solution Approach 2:
The system establishes a feedback loop where load management predictions are continuously generated from offline data, used to adjust scheduling policies, and then evaluated against actual system performance. This closed-loop feedback mechanism enables automatic optimization of control parameters based on real-world outcomes, improving performance while reducing the need for complex manual control.
3Productivity
If infrastructure capacity is adjusted based on load predictions, then resource allocation efficiency improves, but system complexity increases
Solution Approach 1:
The system performs infrastructure capacity adjustments proactively based on load management predictions generated from offline data. By predicting future load requirements in advance and pre-adjusting infrastructure capacity accordingly, the system optimizes resource allocation efficiency while avoiding the complexity of reactive, real-time infrastructure modifications.
Solution Approach 2:
The system enables automated self-adjustment of infrastructure capacity through load prediction-driven policies. The global controller and domain controllers automatically modify resource allocation and infrastructure capacity based on predicted load conditions, reducing the need for complex manual infrastructure management while improving allocation efficiency.
Data Source
AI summary
Techniques described herein relate to a method for managing a distributed multi-tiered computing (DMC) environment. The method includes identifying, by a global controller, a load management prediction event; in response to identifying the load management prediction event: obtaining offline data from local controllers associated with a plurality of DMC domains of the DMC environment; generating load management predictions using the offline data; adjusting local controller scheduling policies based on the load management predictions; adjusting global controller scheduling policies based on the load management predictions; making a first determination that the load management predictions require infrastructure adjustments; and in response to the first determination: initiating infrastructure capacity adjustment based on the load management predictions.