Virtual Desktop OS Disk Tier Switching for Auto-Scaling Cost Control
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Solution Overview
Problem
Public cloud environments face challenges in dynamically scaling virtual desktop environments to balance capacity with demand, leading to poor user experience or excessive costs due to inefficient allocation of resources, particularly during varying demand periods.
Innovation Solution
Implement systems and methods for automatically scaling up and down allocated capacity in virtual desktop environments using base and burst capacity, with load-balancing algorithms and user-defined triggers to optimize resource utilization and cost efficiency, including auto-scaling logic for pooled and personal host pools, and dynamic cost estimation tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If capacity is allocated to meet peak demand, then user experience is improved, but cost increases due to paying for excess capacity during non-peak times
Solution Approach 1:
The patent implements dynamic capacity allocation through auto-scaling mechanisms that automatically adjust the number of virtual desktop instances based on real-time demand metrics. The system transitions from static capacity allocation to dynamic scaling, where capacity is provisioned during peak demand and de-provisioned during non-peak periods, resolving the contradiction between maintaining user experience and reducing costs.
Solution Approach 2:
The system employs feedback loops that continuously monitor utilization metrics, queue lengths, and performance thresholds to trigger scaling decisions. This closed-loop control ensures capacity is adjusted in response to actual demand conditions, preventing both over-provisioning and under-provisioning, thereby balancing user experience with cost efficiency.
2Loss of energy
If capacity is reduced to lower costs, then cost efficiency is improved, but user experience deteriorates due to insufficient resources during peak demand
Solution Approach 1:
The system performs preliminary scaling actions by anticipating peak demand periods and provisioning capacity in advance based on predicted workload patterns. This proactive approach ensures sufficient capacity is available before demand spikes occur, preventing user experience degradation while avoiding the need to maintain permanently high capacity levels.
Solution Approach 2:
The auto-scaling system autonomously monitors its own performance metrics and automatically triggers capacity adjustments without manual intervention. This self-service capability enables the system to respond dynamically to changing conditions, ensuring user experience is maintained while optimizing cost efficiency through automated decision-making.
3Loss of energy
If manual capacity management is used, then cost control is improved, but productivity decreases due to time-consuming capacity adjustments
Solution Approach 1:
The system implements self-service automation where the virtual desktop infrastructure autonomously monitors its own state and performs capacity scaling operations without requiring manual administrator intervention. This eliminates the time-consuming manual processes while maintaining cost control through policy-driven automated decisions, directly resolving the productivity vs. cost control contradiction.
Solution Approach 2:
The patent replaces manual mechanical capacity management processes with automated software-based control systems. The manual adjustment mechanism is substituted with an automated scaling engine that uses algorithms and policies to make capacity decisions, dramatically increasing the speed of capacity adjustment while maintaining or improving cost control through systematic decision-making.
4Reliability
If excess capacity is allocated, then user experience is maintained, but resource utilization efficiency decreases
Solution Approach 1:
The system transitions from static capacity allocation to dynamic scaling, where the number of active virtual desktop instances fluctuates based on real-time demand. This dynamic approach ensures capacity matches actual utilization needs, maintaining user experience during peak periods while eliminating wasted resources during low-demand periods, thereby improving overall resource utilization efficiency.
Solution Approach 2:
The system changes key operational parameters such as the number of active instances, scaling thresholds, and target utilization levels based on demand conditions. By dynamically adjusting these parameters rather than maintaining fixed values, the system optimizes the balance between user experience and resource utilization efficiency across varying workload conditions.
Data Source
AI summary
A system for optimizing OS disk resource utilization in a dynamically auto-scaling virtual desktop environment includes: a scalable virtual desktop environment in which a VM is selectively powered on and off, wherein the VM is associated with a corresponding OS disk that is alternatively assigned to a higher performance OS disk tier or a lower cost OS disk tier; a server including a controller controlling the powering on and the powering off of the VM and assigning the OS disk tier to the VM; and a memory coupled to the controller, wherein the memory stores executable code programming the controller to: in response to the controller powering off the VM, assign the lower cost OS disk tier to the virtual machine; and prior to the controller powering on the virtual machine, assign the higher performance OS disk tier to the virtual machine.