Virtualized Session Resource Management via Adaptive Scheduling
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Solution Overview
Problem
System administrators face challenges in accurately predicting user demand for virtualized user sessions, leading to overprovisioning or underprovisioning, which results in wasted resources and potential delays due to the need for manual scheduling.
Innovation Solution
An adaptive technique that generates schedules based on actual session usage data, maintaining a pool of virtualized session capacity that adjusts dynamically in response to demand and time intervals, ensuring that powered-on servers are available to meet user needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If administrators manually enter schedules specifying minimum numbers of virtualized sessions to be kept available, then session capacity is ensured during various times of day, but the system either overprovisions or underprovisions sessions leading to wasted power and potential delays
Solution Approach 1:
The system automatically monitors actual user session demand in real-time and uses this feedback to dynamically adjust the number of powered-on compute servers. This closed-loop control eliminates the need for manual schedule entry and prevents both overprovisioning (wasting power) and underprovisioning (causing delays) by continuously adapting to actual usage patterns.
Solution Approach 2:
The system performs its own demand prediction and capacity management without requiring administrator intervention. It automatically analyzes usage data, determines optimal server capacity requirements, and adjusts powered-on servers accordingly, freeing administrators from the burden of manual schedule creation while ensuring reliable session availability.
2Reliability
If administrators manually predict and enter schedules of expected virtualized session demand, then session capacity can be planned in advance, but this places additional burdens on administrators whose time is limited
Solution Approach 1:
The system automatically performs demand prediction and capacity planning without requiring administrator time or effort. It collects usage data, analyzes patterns, and autonomously determines optimal server capacity schedules, completely eliminating the administrative burden of manual prediction and schedule entry while maintaining reliable session capacity planning.
Solution Approach 2:
The manual mechanical process of administrator prediction and schedule entry is replaced with an automated computational system that uses algorithms to analyze usage data and generate capacity schedules. This substitution eliminates human time investment while improving the accuracy and reliability of session capacity planning.
3Reliability
If the system keeps a minimum number of servers running and available to satisfy user demand, then session availability is maintained, but this can cause the system to run out of available session capacity during peak demand periods
Solution Approach 1:
The system dynamically adjusts the number of powered-on compute servers based on real-time monitoring of actual user session demand. Rather than maintaining a static minimum number of servers, the system continuously adapts capacity to match actual usage patterns, ensuring both session availability during low demand and sufficient capacity during peak periods without manual intervention.
Data Source
AI summary
A technique for managing virtualized user sessions in an electronic system generates schedules of expected session usage adaptively, based on actual numbers of user sessions allocated while operating the electronic system, and provides capacity for running user sessions from powered-on servers ready to accept new user sessions. The electronic system allocates user sessions in response to requests and tracks numbers of allocated user sessions. As schedules of expected session usage are adjusted based on actual session usage, schedules tend to become more accurate over time and can adapt to changes in usage patterns.