✨Workload Schedulers -- Genesis, Algorithms and Differences
📝 Summary:
This paper categorizes modern workload schedulers into three classes: OS, Cluster, and Big Data. It details their evolution, algorithms, and differences. The conclusion highlights similarities in scheduling strategy design across both local and distributed systems.
🔹 Publication Date: Published on Nov 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.10258
• PDF: https://arxiv.org/pdf/2511.10258
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#WorkloadScheduling #OperatingSystems #DistributedComputing #SchedulingAlgorithms #ComputerScience
📝 Summary:
This paper categorizes modern workload schedulers into three classes: OS, Cluster, and Big Data. It details their evolution, algorithms, and differences. The conclusion highlights similarities in scheduling strategy design across both local and distributed systems.
🔹 Publication Date: Published on Nov 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.10258
• PDF: https://arxiv.org/pdf/2511.10258
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#WorkloadScheduling #OperatingSystems #DistributedComputing #SchedulingAlgorithms #ComputerScience
✨Agent S2: A Compositional Generalist-Specialist Framework for Computer Use Agents
📝 Summary:
Agent S2 is a new compositional framework for computer use agents. It uses Mixture-of-Grounding and Proactive Hierarchical Planning to achieve state-of-the-art performance across various benchmarks and operating systems, significantly improving automation.
🔹 Publication Date: Published on Apr 1, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2504.00906
• PDF: https://arxiv.org/pdf/2504.00906
• Project Page: https://www.simular.ai/articles/agent-s2-technical-review
• Github: https://github.com/simular-ai/Agent-S
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #AIagents #Automation #MachineLearning #ComputerScience
📝 Summary:
Agent S2 is a new compositional framework for computer use agents. It uses Mixture-of-Grounding and Proactive Hierarchical Planning to achieve state-of-the-art performance across various benchmarks and operating systems, significantly improving automation.
🔹 Publication Date: Published on Apr 1, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2504.00906
• PDF: https://arxiv.org/pdf/2504.00906
• Project Page: https://www.simular.ai/articles/agent-s2-technical-review
• Github: https://github.com/simular-ai/Agent-S
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #AIagents #Automation #MachineLearning #ComputerScience