Data Center Workload Consolidation With Pareto Migration Tradeoffs
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Solution Overview
Problem
Traditional data centers with server-based architecture suffer from inefficiencies such as memory stranding and insufficient memory channel bandwidth, leading to suboptimal resource utilization, while existing workload consolidation methods incur significant network overheads and downtime.
Innovation Solution
A computer-implemented method for workload consolidation in data centers using a Q-learning based reinforcement learning approach to optimize energy efficiency and minimize migrations, generating Pareto optimal solutions for resource allocation in composable or disaggregated infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing workload consolidation methods are used, then resource efficiency is improved, but network overheads and service interruption increase
Solution Approach 1:
The patent introduces a reinforcement learning agent as an intermediary that learns optimal workload consolidation policies through interaction with the data center environment. The agent observes system states, selects migration actions, and receives rewards based on energy efficiency improvements, thereby reducing network overheads compared to traditional heuristic methods that require extensive data copying and coordination
Solution Approach 2:
The patent changes the approach from fixed heuristic rules to dynamic parameter optimization through reinforcement learning. The learning agent adapts migration thresholds, timing, and target selection based on real-time system conditions, workload characteristics, and resource utilization patterns, enabling more efficient consolidation with reduced network overhead
2Productivity
If existing workload consolidation methods are used, then resource efficiency is improved, but service interruption increases
Solution Approach 1:
The reinforcement learning agent performs preliminary learning and exploration during periods of low system criticality, building knowledge about optimal consolidation strategies before they are needed. This allows the system to execute migrations more efficiently when actual consolidation is required, minimizing service interruption time while achieving resource efficiency goals
Solution Approach 2:
The patent implements dynamic migration decision-making where the reinforcement learning agent continuously adapts its strategy based on real-time system state changes, workload characteristics, and service priority levels. This dynamic approach allows the system to minimize service interruption by selecting optimal migration timing and targets based on current conditions rather than following static consolidation schedules
3Use of energy by stationary object
If the number of active nodes is reduced, then energy efficiency is improved, but the number of migrations increases
Solution Approach 1:
The reinforcement learning agent receives feedback in the form of rewards that balance energy efficiency gains against migration costs. The reward function incorporates both the energy savings from consolidating workloads onto fewer active nodes and the penalty associated with performing migrations. This feedback mechanism enables the agent to learn optimal consolidation strategies that achieve energy efficiency targets while minimizing the total number of migrations required
Data Source
AI summary
A computer-implemented method for consolidating workloads in a data center. The method includes obtaining information associated with workloads in the data center and optimizing an objective function for workload consolidation based at least in part on the obtained information. The objective function is established for optimizing energy efficiency and workload migration in the data center. The method further includes obtaining, based at least in part on optimizing the objective function, a plurality of Pareto optimal solutions. Each Pareto optimal solution respectively represents an optimal energy efficiency for a corresponding number of migrations associated with the workloads or an optimal number of migrations associated with the workloads for a corresponding energy efficiency. The method further includes consolidating the workloads in the data center based at least in part on at least one of the plurality of Pareto optimal solutions.