Dynamic Resource Provisioning Engine for Virtual Workloads
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Solution Overview
Problem
Existing resource provisioning systems in virtual environments face inefficiencies due to peak usage times requiring large resources, which often lead to idle resources during off-peak times, resulting in suboptimal utilization and increased costs.
Innovation Solution
A dynamic resource provisioning method and system that determines current resource requirements based on workload demand, using a provisioning engine to allocate and reallocate resources such as virtual machines and bandwidth, optimizing resource utilization through comparison with current allocations and communication with library instances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large amounts of resources are provisioned to meet peak usage demand, then service reliability is improved, but resource utilization efficiency deteriorates due to idle resources during off-peak times
Solution Approach 1:
The patent implements dynamic resource provisioning that automatically adjusts resource allocation based on real-time workload monitoring. The system transitions from static pre-provisioning to dynamic adjustment, scaling resources up during peak demand and down during off-peak periods, thereby maintaining service reliability while improving resource utilization efficiency
Solution Approach 2:
The system employs feedback mechanisms through workload monitoring and analysis components that continuously track resource usage patterns. This feedback loop enables the provisioning engine to make informed decisions about resource allocation, adjusting provisions based on actual demand signals rather than static predictions, thus resolving the contradiction between reliability and efficiency
2Device complexity
If static resource provisioning is used to simplify management, then device complexity is reduced, but adaptability to varying workload demands deteriorates
Solution Approach 1:
The patent implements self-service automation where the provisioning system automatically monitors workload, analyzes demand patterns, and adjusts resource allocation without manual intervention. The intelligent provisioning engine autonomously makes scaling decisions based on predefined policies and real-time metrics, eliminating the need for complex manual provisioning management while maintaining high adaptability to workload changes
Solution Approach 2:
The system dynamically changes provisioning parameters such as resource allocation levels, scaling thresholds, and provisioning timelines based on workload conditions. By making parameters adjustable and condition-dependent rather than fixed, the system achieves both simplicity through automation and adaptability to varying demands
3Reliability
If resources are over-provisioned to ensure availability, then service continuity is improved, but cost efficiency deteriorates due to paying for unused capacity
Solution Approach 1:
The patent implements periodic resource provisioning adjustments that align with actual usage patterns. Instead of continuously maintaining maximum resource levels, the system periodically scales resources up and down based on predicted and actual demand cycles, ensuring service continuity during peak periods while reducing capacity during low-utilization periods to improve cost efficiency
Solution Approach 2:
The system performs preliminary resource provisioning based on workload predictions and historical patterns. By anticipating demand spikes and pre-allocation resources in advance, the system ensures service continuity is maintained during expected peak periods without needing to permanently over-provision, thus improving cost efficiency while maintaining reliability
Data Source
AI summary
A system and method dynamically provisions resources in a virtual environment. A current resource requirement is determined based on a current workload demand using one or more computer systems providing resources and access to the resources. The method and system includes comparing the current resource requirement with a current resource allocation using an engine communicating with resources. The engine is configured to allocate the resources, and the engine determines the resource requirement responsive to communications with a plurality of library instances. The library exposes a single-node interface for use by a user-application. The current resource allocation is modified based on the comparison of the current resource requirement with the current resource allocation, and in response to the current resource requirement, using the engine.