Proactive Workload Placement Using Forecast Data
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Solution Overview
Problem
Current workload balancing approaches in hyper-converged infrastructures are reactive and resource-intensive, often causing near-future contention due to lack of consideration for future demand fluctuations, leading to inefficient resource allocation and increased contention.
Innovation Solution
A proactive workload placement framework that uses forecasted demand data to determine optimal placement decisions, considering current and future resource usage patterns across CPU, memory, storage, and network resources, and matches workloads with providers based on predicted demand spikes to minimize near-future contention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If workload migration is performed reactively to balance current resource usage, then resource balance is improved, but resource contention occurs in near-future due to lack of demand prediction
Solution Approach 1:
The system performs workload placement decisions proactively by predicting future resource demand using forecasted demand data before contention occurs. The workload placement engine anticipates future resource needs and makes placement decisions in advance, preventing near-future contention rather than reacting to it after it happens.
Solution Approach 2:
The system uses forecasted demand data as feedback to continuously optimize workload placement. By monitoring predicted future demand patterns and using this information to adjust placement decisions, the system adapts to changing resource requirements and maintains optimal balance while preventing future contention.
2Productivity
If constant workload migration is performed to maintain optimal balance, then resource utilization is improved, but system stability deteriorates due to unnecessary contention
Solution Approach 1:
By using forecasted demand data to make advance placement decisions, the system avoids constant reactive migrations. Workloads are placed optimally for future conditions, reducing the need for frequent migrations and thereby maintaining environment stability while still achieving high resource utilization.
Solution Approach 2:
The workload placement engine autonomously uses forecasted demand data to make intelligent placement decisions without requiring constant external intervention or reactive adjustments. This self-service capability stabilizes the environment by preventing unnecessary migrations while maintaining optimal resource utilization.
3Loss of time
If workload placement decisions are made without forecasted demand data, then decision speed is improved, but resource allocation efficiency deteriorates due to frequent reallocation
Solution Approach 1:
The system prepares forecasted demand data in advance, allowing workload placement decisions to be made quickly based on pre-computed predictions. This preliminary preparation of demand forecasts enables fast decision-making while ensuring optimal resource allocation efficiency by considering future demand patterns.
Solution Approach 2:
The system transforms raw demand data into forecasted demand parameters that capture future resource requirements. By changing the parameter representation from current usage to predicted future demand, the system enables both rapid decision-making and efficient resource allocation simultaneously.
Data Source
AI summary
Various examples are disclosed for workload placement using forecast data. Forecast data for workloads and providers during a predefined period of time in the future is considered when identifying stressed providers and the feasibility of a workload move. Workloads with demand spikes at different future times can be matched by stacking current demand and forecast demand by timestamps. The possibility of stress can be reduced by making moves preemptively and considering forecast demand when evaluating the feasibility of a workload move.