Scenario Planning Engine for Distributed Storage Infrastructure
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Legacy techniques for managing distributed storage systems are inadequate in predicting capacity requirements and considering multiple objectives and constraints, leading to inaccurate infrastructure planning and poor resource allocation.
Innovation Solution
The development of a user interface for simulating planning scenarios using predictive models and remediation rules to assess and optimize distributed storage infrastructure plans, reducing computational demands and improving resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If legacy techniques are used for managing distributed storage systems, then device complexity is reduced, but measurement precision of capacity requirements deteriorates
Solution Approach 1:
The patent introduces an intermediary planning system that acts as a mediator between legacy management techniques and infrastructure planning tasks. This system captures observable periodicities and seasonalities in demand patterns, performs scenario planning simulations, and provides recommended plans to administrators, thereby improving measurement precision without directly increasing the complexity of legacy systems themselves.
Solution Approach 2:
The planning system performs self-service by automatically capturing demand patterns, simulating multiple planning scenarios, and generating recommended plans without requiring administrators to manually analyze complex data. The system serves itself by identifying its own capabilities to assess planning scenarios and provide meaningful recommendations, reducing the burden on administrators while improving accuracy.
2Productivity
If comprehensive scenario planning is implemented, then productivity of infrastructure planning is improved, but use of energy increases
Solution Approach 1:
The patent applies partial action by implementing scenario planning selectively for the most critical infrastructure planning decisions rather than continuously for all planning tasks. The system assesses planning scenarios based on captured demand patterns and provides recommendations only when significant improvements are expected, thereby improving productivity while avoiding excessive computational resource consumption for routine planning activities.
3Adaptability or versatility
If multiple planning scenarios are assessed, then adaptability of infrastructure planning is improved, but loss of time in analysis increases
Solution Approach 1:
The patent applies preliminary action by capturing observable periodicities and seasonalities in demand patterns in advance, before actual infrastructure planning decisions are made. This pre-processing of demand data enables the system to quickly assess multiple planning scenarios by comparing them against pre-captured patterns, thereby improving adaptability while reducing the time required for scenario analysis when planning decisions are actually needed.
Data Source
AI summary
Systems and methods for “what-if” scenario planning of a distributed data storage system. A scenario planning engine has a user interface to facilitate user interactions to describe “what if” scenarios. A method comprises steps to collect system performance measurements pertaining to measurable characteristics of the distributed storage system. A predictive model is generated and formatted for use as a predictor of one or more predictive model parameters that are derived from the collected system performance measurements and/or any calculated predictions and/or correlations. A user can vary a set of scenario input parameters so as to characterize one or more “what if” scenarios. The user-defined scenario input parameters are formatted and used as predictive model inputs. The predictive model is used to simulate predicted system performance parameters corresponding to respective “what-if” planning scenarios. A user interface is provided to present a graphical depiction of predicted system performance corresponding to the scenarios.