Policy Controlled Semi-Autonomous Infrastructure Evaluator for Dynamic Resource Allocation
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Solution Overview
Problem
Legacy and cloud data centers, as well as edge computing systems, often allocate resources statically, leading to inefficient utilization of hardware resources, particularly in network function virtualization (NFV) infrastructure, resulting in suboptimal performance and resource management.
Innovation Solution
A policy-controlled semi-autonomous infrastructure evaluator is introduced, utilizing machine-learning algorithms and models to monitor telemetry signals from infrastructure components, detect service level agreement (SLA) deviations, and dynamically adjust resource allocation by sending recommendations to orchestrators or other management applications to ensure efficient resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static resource allocation is used in legacy and cloud data centers, then infrastructure management is simplified, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring infrastructure components and automatically adjusting resource distribution based on actual demand. The system transitions from static to dynamic management, allowing resources to be reallocated in real-time to optimize utilization efficiency while maintaining manageable complexity through automated decision-making processes.
Solution Approach 2:
The system incorporates feedback mechanisms by monitoring telemetry signals from infrastructure components and using this information to adjust resource allocation. The closed-loop feedback enables the system to respond to changing conditions and optimize resource utilization dynamically, resolving the contradiction between simplified management and efficient resource use.
2Device complexity
If static resource allocation is used, then infrastructure configuration is simpler, but service level agreement compliance deteriorates
Solution Approach 1:
The system dynamically adjusts resource allocation to maintain SLA compliance under varying workload conditions. By continuously monitoring infrastructure state and demand, the system can adapt resource distribution to ensure service level agreements are met, resolving the contradiction between simple configuration and reliable SLA compliance.
Solution Approach 2:
The monitoring and automated adjustment system provides continuous feedback to ensure SLA compliance. The system detects deviations from SLA requirements and automatically reallocates resources to correct them, maintaining reliability without requiring complex manual configuration management.
3Ease of operation
If manual resource management is used, then system control is more precise, but response time to demand changes deteriorates
Solution Approach 1:
The system implements self-service automation where the infrastructure management system autonomously monitors, analyzes, and adjusts resource allocation without human intervention. This maintains the precision of manual control while eliminating response delays, as the automated system can react instantaneously to demand changes.
Solution Approach 2:
The real-time feedback loop enables the system to detect demand changes and automatically adjust resource allocation without manual intervention. This maintains control precision through automated decision-making while dramatically reducing response time compared to manual management processes.
Data Source
AI summary
Embodiments of the present disclosure may relate to an apparatus for infrastructure management with an interface to receive a plurality of telemetry signals from first one or more infrastructure components of an infrastructure; and a policy controlled semi-autonomous (PCSA) infrastructure evaluator coupled with the interface, where the PCSA infrastructure evaluator includes a machine-learning (ML) model of service level metric (SLM) deviation by second one or more application or infrastructure components of the infrastructure and the PCSA infrastructure evaluator is to: determine a deviation from a SLM of third one or more infrastructure components based at least in part the ML model and one or more of the plurality of telemetry signals; and send a message, based at least in part on the deviation from the SLM. Other embodiments may be described and/or claimed.