Virtual Machine Resource Allocation via Dynamic Threshold Monitoring
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Solution Overview
Problem
Enterprises face challenges in dynamically allocating resources among virtual machines in service groups within communications networks, leading to inefficiencies due to over- or under-allocation of resources such as memory, CPU usage, and disk space, which can impact business process performance.
Innovation Solution
A system and method that utilize ontological network descriptions to monitor virtual machine performance, determine service tier thresholds, and automatically reallocate resources based on current operating conditions, using data collection agents and an ontological description creation engine to dynamically adjust resource allocation and maintain optimal performance levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If resources are statically allocated to virtual machines, then resource allocation is simple and stable, but resource utilization efficiency deteriorates due to over- or under-allocation
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring virtual machine performance metrics (CPU usage, memory consumption, disk I/O) and automatically adjusting resource allocation based on current workload conditions. This transforms the static allocation model into a dynamic one that adapts to changing demands, resolving the contradiction between simplicity and efficiency.
Solution Approach 2:
The system establishes a feedback loop where resource allocation decisions are based on real-time performance monitoring and threshold evaluations. The monitoring component continuously collects data, compares it against predefined thresholds, and triggers reallocation when thresholds are exceeded, creating a closed-loop control system that improves efficiency while maintaining manageable complexity.
2Speed
If manual resource allocation is used, then allocation decisions are simple to make, but response time to changing conditions deteriorates
Solution Approach 1:
The system enables self-service automation where the resource allocation process operates autonomously without manual intervention. The monitoring component automatically detects threshold violations and triggers reallocation actions, allowing the system to respond immediately to changing conditions while eliminating the delays inherent in manual decision-making processes.
Solution Approach 2:
The patent implements preliminary action by pre-defining performance thresholds and allocation rules before runtime conditions change. When monitored metrics exceed these pre-set thresholds, the system automatically executes predetermined reallocation actions, enabling rapid response to changing conditions without requiring complex real-time decision-making logic.
3Reliability
If resources are over-allocated to ensure performance, then service reliability is improved, but resource waste increases
Solution Approach 1:
The system dynamically changes resource allocation parameters (CPU shares, memory limits, disk I/O quotas) based on monitored performance metrics. By adjusting these parameters in response to actual workload conditions, the system ensures sufficient resources are allocated to maintain service reliability while avoiding the persistent over-allocation that leads to waste.
Solution Approach 2:
The patent applies partial action by allocating resources based on actual needs rather than providing excessive resources to all virtual machines uniformly. The system monitors individual virtual machine performance and allocates additional resources only to those that exceed performance thresholds, rather than over-allocating to all machines, thereby maintaining reliability while reducing overall resource waste.
Data Source
AI summary
Virtual machine resources may be monitored for optimal allocation. One example method may include monitoring a virtual machine operating in a network to determine whether at least one predefined service tier threshold has been exceeded for a predefined amount of time, initiating a query to determine current performance threshold data of the at least one predefined service tier threshold from a database, determining at least one component state of at least one component of the virtual machine based on the at least one service tier threshold assigned to the at least one component, and reallocating the resource provided by the virtual machine when the component state indicates a high warning state.