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Optimizing Cloud Data Center Energy Efficiency via Dynamic Prediction of CPU Idle Intervals
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers
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CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms
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Computer Architecture Techniques for Power-Efficiency
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Worldwide electricity used in data centers
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Versatile prediction and fast estimation of Architectural Vulnerability Factor from processor performance metrics
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SleepScale: Runtime joint speed scaling and sleep states management for power efficient data centers
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BigHouse: A simulation infrastructure for data center systems
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The Case for Energy-Proportional Computing
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inferredcites →https://doi.org/10.1145/2591971.2592017openalex-snapshot
A comparison of core power gating strategies implemented in modern hardware
inferredcites →https://doi.org/10.1145/2637364.2592017openalex-snapshot
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Greedy function approximation: A gradient boosting machine.
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Computer Architecture Techniques for Power-Efficiency
inferredcites →https://doi.org/10.2200/s00193ed1v01y200905cac006openalex-snapshot
The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines
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The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines, Second edition
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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