Remote Desktop Session Performance Model for Resource Allocation
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Solution Overview
Problem
Current resource allocation methods in IT systems often result in over-provisioning, leading to substantial waste due to unpredictable computing demands, particularly in remote desktop sessions where it's challenging to predict user applications and their resource requirements.
Innovation Solution
A performance modeling approach is used to allocate resources for remote desktop sessions based on the applications to be used, generating a remote desktop session performance model that determines the necessary resources to maintain predetermined quality of service (QoS) without over-provisioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If resources are over-provisioned to meet unpredictable computing demands in remote desktop sessions, then quality of service is ensured, but resource waste and expenses increase substantially
Solution Approach 1:
The patent implements dynamic resource allocation that adjusts computing resources based on actual user needs and application requirements. The system continuously monitors resource usage patterns and reallocates resources dynamically, transitioning from static over-provisioning to adaptive resource management that matches supply with demand in real-time
Solution Approach 2:
The system changes resource allocation parameters based on predicted user behavior and application characteristics. By analyzing historical data and application profiles, the system adjusts CPU, memory, and storage parameters dynamically to match actual needs rather than maintaining fixed over-provisioned levels
2Productivity
If resources are allocated based on predictable computing demands, then resource efficiency improves, but service quality may deteriorate when demands are unpredictable
Solution Approach 1:
The system performs preliminary resource allocation based on predicted computing demands before users actually execute applications. By analyzing application profiles, historical usage patterns, and user behavior, the system pre-allocates appropriate resources, then adjusts during execution based on actual needs, ensuring both efficiency and service quality
Solution Approach 2:
The patent implements continuous feedback mechanisms that monitor actual resource usage against predictions. The system compares predicted demands with actual consumption patterns and uses this feedback to refine future predictions and adjust resource allocation, creating a closed-loop system that improves accuracy over time
3Reliability
If a large number of resources are used in resource-on-demand environments to meet customer computing demands, then service availability is maintained, but expenses for wasted resources increase substantially
Solution Approach 1:
The patent enables resources to serve multiple functions and multiple users dynamically. By virtualizing and sharing computing resources across different users and applications based on actual demand, the system maintains service availability for all users while reducing the total quantity of physical resources needed in the data center
Data Source
AI summary
A request for a remote desktop session is received. A remote desktop session performance model is generated based on the applications to be used in the remote desktop session, and resources are assigned to the remote desktop session using the remote desktop session performance model.