For the machine learning approach, we use models learned from previous system behaviors in order to predict power consumption levels, CPU loads, and SLA timings ...
ABSTRACT. As energy-related costs have become a major economical fac- tor for IT infrastructures and data-centers, companies and the research community are ...
This paper proposes algorithms to reduce energy consumption by data centers by considering the placement of virtual machines onto the servers in the data ...
Towards energy-aware scheduling in data centers using machine learning. In: 1st International Conference on Energy-Efficient Computing and Networking ...
Challenges for autonomic energetic management: – Datacenters policies require adaption towards constant optimization. – Complexity can be saved through ...
Energy-aware task scheduling approaches reduce energy consumption in data centers. They rely on a full profiling, not feasible in long-running applications.
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Jul 30, 2012 · This chapter contains sections titled: Introduction. Intelligent Self-Management. Introducing Power-Aware Approaches.
1st International Conference on Energy-Efficient Computing and Networking (e-Energy'10) Place Published: University of Passau, Germany
Towards energy-aware scheduling in data centers using machine learning. https ... with time constraints for energy-efficient cloud-computing data centers.
Apr 15, 2010 · As energy-related costs have become a major economical factor for IT infrastructures and data-centers, companies and the research community ...