Adaptive IT Infrastructure Optimization via Policy Selection
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Solution Overview
Problem
Conventional approaches to managing complex virtual data centers and IT infrastructure struggle to efficiently monitor and react to dynamic workload changes, leading to a disconnect between application needs and infrastructure actions, making it difficult to achieve service level objectives (SLOs) while ensuring fair resource usage.
Innovation Solution
An adaptive input/output optimization system with modules such as a collector, analyzer, policy module, and controller that gather, analyze, and adjust infrastructure parameters to translate SLOs into key performance indicators, using a situational analysis framework to select and deploy policy sets based on changing conditions, thereby automating resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If labor-intensive monitoring at multiple levels is used, then detailed status monitoring is achieved, but management efficiency deteriorates and SLO achievement becomes difficult
Solution Approach 1:
The system enables self-service through automated policy execution. The controller automatically selects policies and executes actions based on monitored conditions, eliminating the need for manual intervention. The system monitors itself and executes corrective actions autonomously, transforming labor-intensive monitoring into automated self-management that maintains detailed status awareness while dramatically improving management efficiency.
Solution Approach 2:
The system implements continuous feedback loops where monitored conditions are constantly evaluated against defined policies. When conditions change, the system receives feedback about current state, selects appropriate policies, and executes actions to restore desired states. This closed-loop feedback mechanism maintains precise monitoring while automating the response process, resolving the contradiction between detailed monitoring and management efficiency.
2Productivity
If automated policy execution is implemented, then management efficiency is improved, but adaptability to dynamic workload changes deteriorates
Solution Approach 1:
The system achieves dynamics by making policy selection adaptive to current conditions. The controller continuously monitors monitored conditions and dynamically selects which policies to execute based on real-time state. Policies are not static but are actively chosen and adjusted according to changing workload conditions, allowing the automated system to adapt flexibly while maintaining high management efficiency through automation.
Solution Approach 2:
The system changes parameters by adjusting policy selection based on monitored condition thresholds. When conditions cross defined thresholds, the system transitions between different policies, effectively changing operational parameters dynamically. This parameter-based adaptation allows automated management to respond appropriately to dynamic workloads by switching between policies with different characteristics based on current system state.
3Adaptability or versatility
If manual resource allocation is used, then flexibility in resource distribution is achieved, but fairness and SLO achievement deteriorate
Solution Approach 1:
The system segments resource allocation by dividing resources into shareable units with defined policies. Each resource or resource group is assigned specific policies that define allocation behavior. This segmentation allows flexible distribution while ensuring fairness through consistent, rule-based application of policies to each segment, eliminating manual allocation biases and improving SLO achievement through uniform policy enforcement.
Solution Approach 2:
The system uses feedback to ensure fair resource allocation by continuously monitoring resource usage and comparing it against defined policies and SLOs. When allocation becomes unfair or SLOs are at risk, the feedback mechanism detects the condition and triggers policy execution to correct the imbalance. This closed-loop feedback ensures both flexibility in distribution and reliability in fairness through automated enforcement of equitable policies.
Data Source
AI summary
A processing platform comprises at least one server, computer or other processing device having a processor coupled to a memory, and implements a plurality of modules for adaptive optimization across an information technology (IT) infrastructure. The modules include a collector configured to gather information from the infrastructure, an analyzer coupled to the collector and configured to analyze the information gathered by the collector, a policy module specifying a plurality of policy sets, and a controller that is coupled to the collector, the analyzer and the policy module. The controller is configured to adjust one or more parameters of the infrastructure via corresponding control points. Associated with the analyzer is a situational analysis framework configured to periodically select and deploy for use by the controller a particular one of the specified plurality of policy sets responsive to changing operating conditions of the infrastructure. The infrastructure may comprise a virtual data center (VDC) or other type of virtual infrastructure.