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Empirical evaluation of power saving policies for data centers

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Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-540-69814-2_16openalex-snapshot
Dynamic Server Allocation for Power and Performance
inferredcites →https://doi.org/10.1007/978-3-540-71918-2openalex-snapshot
Forecasting with Exponential Smoothing : The State Space Approach
inferredcites →https://doi.org/10.1007/s10479-011-0932-1openalex-snapshot
Managing performance and power consumption in a server farm
inferredcites →https://doi.org/10.1007/s10586-008-0052-0openalex-snapshot
A regression-based analytic model for capacity planning of multi-tier applications
inferredcites →https://doi.org/10.1007/s11134-005-1740-6openalex-snapshot
Analysis of a Multiserver Queue with Setup Times
inferredcites →https://doi.org/10.1016/j.comnet.2009.02.019openalex-snapshot
Wikipedia workload analysis for decentralized hosting
inferredcites →https://doi.org/10.1016/j.peva.2010.08.009openalex-snapshot
Optimality analysis of energy-performance trade-off for server farm management
inferredcites →https://doi.org/10.1016/j.suscom.2011.11.001openalex-snapshot
Optimizing Cloud providers revenues via energy efficient server allocation
inferredcites →https://doi.org/10.1109/icoin.2012.6164352openalex-snapshot
A queuing analysis of an energy-saving mechanism in data centers
inferredcites →https://doi.org/10.1109/inm.2007.374776openalex-snapshot
Dynamic Placement of Virtual Machines for Managing SLA Violations
inferredcites →https://doi.org/10.1109/mascots.2012.13openalex-snapshot
Squeezing Out the Cloud via Profit-Maximizing Resource Allocation Policies
inferredcites →https://doi.org/10.1109/mnet.2011.5730525openalex-snapshot
Enabling backbone networks to sleep
inferredcites →https://doi.org/10.1145/1342171.1342172openalex-snapshot
Agile dynamic provisioning of multi-tier Internet applications
inferredcites →https://doi.org/10.1145/1555228.1555262openalex-snapshot
vManage
inferredcites →https://doi.org/10.1145/1891719.1891721openalex-snapshot
The cost of reconfiguration in a cloud
inferredcites →https://doi.org/10.1145/502034.502045openalex-snapshot
Managing energy and server resources in hosting centers
inferredcites →https://doi.org/10.1145/502059.502045openalex-snapshot
Managing energy and server resources in hosting centers

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/s42979-021-00633-5openalex-snapshot
Incentive-Scheduling Algorithms to Provide Green Computational Data Center
inferred← citeshttps://doi.org/10.1016/b978-0-12-823395-5.00005-7openalex-snapshot
A survey and critical analysis on energy generation from datacenter
inferred← citeshttps://doi.org/10.1109/seams.2015.20openalex-snapshot
Adaptive Management of Energy Consumption Using Adaptive Runtime Models
inferred← citeshttps://doi.org/10.1145/2593743.2593745openalex-snapshot
Towards software-adaptive green computing based on server power consumption
inferred← citeshttps://doi.org/10.1145/3489048.3522644openalex-snapshot
A Comprehensive Empirical Study of Query Performance Across GPU DBMSes
inferred← citeshttps://doi.org/10.1145/3508024openalex-snapshot
A Comprehensive Empirical Study of Query Performance Across GPU DBMSes
inferred← citeshttps://doi.org/10.1145/3547353.3522644openalex-snapshot
A Comprehensive Empirical Study of Query Performance Across GPU DBMSes
inferred← citeshttps://doi.org/10.2172/1150909openalex-snapshot
Item Unavailable
inferred← citeshttps://doi.org/10.25560/25093openalex-snapshot
Modelling bursty flows with fluid queues
inferred← citeshttps://doi.org/10.5555/2821357.2821380openalex-snapshot
Adaptive management of energy consumption using adaptive runtime models
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