wheatley
VMAgent
| wheatley | VMAgent | |
|---|---|---|
| 1 | 1 | |
| 32 | 75 | |
| - | - | |
| 8.6 | 1.4 | |
| 5 days ago | about 1 year ago | |
| Python | Python | |
| GNU General Public License v3.0 only | MIT License |
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wheatley
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[D] What's the current state/consensus on using neural networks for solving combinatorial scheduling problems?
See L2D https://github.com/zcaicaros/L2D for a seminal work, and https://github.com/jolibrain/wheatley/ that beats L2D and extends to RCPSPs (aka scheduling with resources), a work by colleagues of mine, with application to real-world scheduling problems.
VMAgent
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Huawei Research Introduces ‘VMAgent’: A Platform for Exploiting Reinforcement Learning (RL) on Virtual Machine (VM) Scheduling Tasks
In a recent study, researchers from Huawei Cloud’s Multi-Agent Artificial Intelligence Lab and Algorithm Innovation Lab suggested VMAgent, a unique VM scheduling simulator based on real data from Huawei Cloud’s actual operation situations. VMAgent seeks to replicate the scheduling of virtual machine requests across many servers (allocating and releasing CPU and memory resources). It creates virtual machine scheduling scenarios using real-world system design, such as fading, recovering, and expanding virtual machines. Only requests can be allocated in the fading situation, whereas the recovering scenario permits both allocating and releasing VM resources.
What are some alternatives?
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maro - Multi-Agent Resource Optimization (MARO) platform is an instance of Reinforcement Learning as a Service (RaaS) for real-world resource optimization problems.
oncall - Oncall is a calendar tool designed for scheduling and managing on-call shifts. It can be used as source of dynamic ownership info for paging systems like http://iris.claims.