Virtual Machine Scheduling via Resource Prediction

The patent applies preliminary action by predicting future resource requirements of virtual machines before peak usage occurs. The prediction module analyzes historical resource usage data to forecast future needs, allowing the scheduling system to allocate resources in advance based on actual predicted demand rather than reserving peak resources continuously. This resolves the contradiction by maintaining reliability through accurate prediction while reducing resource waste through dynamic allocation.

The patent changes the parameter of resource allocation from static peak-based reservation to dynamic prediction-based allocation. By continuously updating resource predictions based on historical data and adjusting allocations accordingly, the system maintains virtual machine reliability while optimizing resource utilization. The scheduling decisions are made based on predicted resource parameters rather than fixed peak values, reducing waste.

The patent applies local quality by implementing differentiated scheduling strategies for different virtual machine types. The system classifies virtual machines into categories (e.g., computation-intensive, memory-intensive, I/O-intensive) and applies specific scheduling algorithms optimized for each type. This maintains ease of operation through automated classification and strategy selection while dramatically improving resource allocation efficiency by matching VM characteristics with appropriate scheduling approaches.

The patent introduces dynamics by making scheduling strategies adaptive rather than static. The system dynamically adjusts scheduling decisions based on real-time resource usage patterns, virtual machine priorities, and system conditions. This allows the system to maintain operational simplicity through automated adaptation while achieving high resource allocation efficiency through context-aware scheduling.

The patent applies preliminary action by predicting future resource requirements before migration decisions are made. The system forecasts when and where migrations will be beneficial, allowing it to plan migrations proactively rather than reactively. This reduces the frequency of migrations by only moving virtual machines when predictions indicate genuine benefit, thereby improving resource utilization while maintaining system stability through reduced migration-induced disruptions.

The patent implements feedback mechanisms that monitor the impact of migrations on system stability and user experience. The system learns from migration outcomes and adjusts future migration decisions accordingly. This feedback loop allows the system to optimize resource utilization through strategic migrations while maintaining stability by avoiding migrations that would cause excessive disruption, thus resolving the contradiction between productivity and stability.