System Optimization Module for Datacenter Performance Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Complex systems like datacenters with virtual machines experience performance degradation due to unresponsive hosts or resource constraints, making it difficult to determine the causes and optimize overall system performance, as existing self-healing systems primarily focus on individual objects rather than the system as a whole.
Innovation Solution
An optimization module that receives health determinations from a monitoring module, identifies available actions, determines their expected utility, selects the most beneficial action, implements it, and updates probabilities using Bayesian updating functions to improve future decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If self-healing systems address performance degradation of individual objects, then the object performance can be corrected, but the overall system performance cannot be optimized
Solution Approach 1:
The patent merges individual object self-healing capabilities with system-wide optimization by implementing a centralized optimization module that considers inter-object dependencies. The system combines monitoring of individual objects with analysis of system-wide consequences, allowing actions to be taken that benefit the overall system rather than just individual components.
Solution Approach 2:
The system implements feedback mechanisms by monitoring actual consequences of actions taken and using this information to update probability models. The optimization module receives feedback about which actions improve system performance and adjusts its decision-making accordingly, enabling continuous improvement of system-wide optimization.
2Reliability
If actions are taken to correct performance degradation, then object reliability improves, but it becomes difficult to determine which actions maximize overall system performance
Solution Approach 1:
The optimization module performs preliminary analysis by evaluating multiple possible actions and their expected consequences before implementing any changes. It calculates expected utility for each potential action based on probability models, allowing the system to prepare and select optimal actions in advance rather than reacting to performance degradation alone.
Solution Approach 2:
The system dynamically adjusts its optimization strategy based on real-time monitoring of action consequences. The probability models are continuously updated with new data about which actions work best, allowing the system to adapt its decision-making to changing system conditions and learn from past experiences.
3Extent of automation
If existing self-healing agents are used, then individual object issues can be automatically corrected, but system-wide optimization and understanding of inter-object dependencies is limited
Solution Approach 1:
The optimization module serves multiple functions simultaneously: it monitors system-wide health, evaluates inter-object dependencies, calculates expected utility of actions, selects optimal actions, and updates probability models. This multi-functional approach consolidates what would otherwise require separate systems into a single unified optimization module.
Solution Approach 2:
The optimization module acts as an intermediary between monitoring data and action execution. It receives health determinations from monitoring modules, processes this information through probability models, and generates optimized actions that consider system-wide consequences, serving as a mediating layer that translates individual object data into system-wide optimization decisions.
Data Source
AI summary
Embodiments provide a system including a plurality of objects and a monitoring module coupled to the objects. The monitoring module is configured to determine a health value of each object. The system also includes an optimization module coupled to the monitoring module. The optimization module is configured to receive a user input indicating a utility to be increased within the system, wherein the utility is based on the health value of each object. The optimization module is also configured to identify a plurality of available actions to be performed on each object. Each available action is associated with at least one expected consequence. The optimization module is also configured to calculate an expected utility of each action based on an effect of each expected consequence on the health value of each object and select, from the available actions, an action based on the expected utility for the system.