Dynamic CPU Core Allocation via MIP Workload Prediction
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Solution Overview
Problem
Current computing systems lack an efficient method for dynamically allocating CPU processor cores based on predicted workloads, leading to suboptimal resource utilization and potential performance issues due to sudden workload changes or inefficient core reassignments.
Innovation Solution
A method utilizing a Mixed Integer Programming (MIP) engine to predict and allocate CPU processor cores among emulations, employing statistical models or machine learning algorithms to forecast workloads and optimize core distribution, ensuring maximum spare capacity and minimizing reassignments, tailored for both homogeneous and heterogeneous CPU environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If CPU processor cores are statically allocated to emulations, then system stability is maintained, but resource utilization efficiency deteriorates during sudden workload changes
Solution Approach 1:
The patent implements dynamic CPU core allocation by continuously monitoring workload metrics (CPU usage, memory usage, disk I/O, network I/O) and adjusting the number of cores assigned to each emulation in real-time. This allows the system to adapt to sudden workload changes while maintaining stability through controlled adjustment mechanisms.
Solution Approach 2:
The system employs feedback loops where workload performance metrics are continuously measured, compared against thresholds, and used to trigger allocation adjustments. The feedback mechanism ensures that allocation changes are made only when necessary, maintaining system stability while improving resource utilization.
2Productivity
If CPU processor cores are dynamically reallocated based on current workload, then resource utilization improves, but performance deteriorates due to reassignment overhead
Solution Approach 1:
The patent implements periodic allocation reviews at predetermined intervals, allowing the system to balance resource utilization improvements against reassignment overhead. By reviewing allocations periodically rather than continuously, the system reduces reassignment frequency while still responding to significant workload changes.
Solution Approach 2:
The system uses automated workload metric collection and analysis to make allocation decisions without manual intervention, reducing the time and overhead associated with reassignments. The self-service approach allows rapid, programmatic adjustments that minimize performance impact.
3Reliability
If the number of CPU processor cores is increased to handle peak workloads, then fault tolerance improves, but resource allocation efficiency deteriorates during low utilization periods
Solution Approach 1:
The patent dynamically changes the allocation parameters (number of CPU cores per emulation) based on monitored workload metrics. When workload exceeds thresholds, additional cores are allocated to maintain fault tolerance; when workload decreases, cores are released to improve efficiency. This parameter adjustment resolves the contradiction between maintaining fault tolerance and achieving efficiency.
Data Source
AI summary
A method of workload aware dynamic CPU processor core allocation includes the steps of predicting estimated individual workloads for each emulation in a set of emulations for each decision period of a set of decision periods over a predictive time span. The method includes using, by a Mixed Integer Programming (MIP) engine, the predicted estimated individual workloads for each emulation in the set of emulations, a set of constraints, and an optimization function, to determine sets of CPU processor cores to be allocated to each emulation during each decision period over the predictive time span. The method further includes dynamically allocating, by the host computer system, the sets of CPU processor cores to each emulation during each decision period over the predictive time span based on the output from the MIP engine.