Power provisioning for a warehouse-sized computer

By Xiaobo Fan, Wolf-Dietrich Weber, Luiz André Barroso. International Symposium on Computer Architecture, 2007

2,268 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Hardware and Architecture, Information Systems

View Paper: https://www.semanticscholar.org/paper/b44e3a9b0afb925b8cd6477495981d7b73d1d0af

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Hardware and Architecture

In Information Systems

Citations in the corpus

A multi-objective ant colony system algorithm for virtual machine placement in cloud computing (2013, background): “Recent studies show that the power consumption of servers can be accurately described by a linear relationship between the power consumption and CPU utilization [38].”
A Holistic Approach for Designing Carbon Aware Datacenters (2022, background): “Such schedules increase power utilization, amortizing infrastructure costs for the facility, power delivery, and servers [15].”
Data Center Energy Consumption Modeling: A Survey (2016, background): “have shown that the linear power model can track the dynamic power usage with a greater accuracy at the PDU level [6][109].”
Distributed Internet-Based Load Altering Attacks Against Smart Power Grids (2011, methodology): “The energy consumption parameters for all data centers are assumed to be the same as those reported in [13] and [31].”
Renewable and cooling aware workload management for sustainable data centers (2012, methodology): “, [1, 2, 3, 4, 5, 6, 7, 8]; however, the integration of these silos is an important next step.”
Carbon-Aware Computing for Datacenters (2021, positive): “Ū (c) pow, to prevent power domains’ circuit breakers from tripping ([31], [39]).”
A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning (2017, neutral): “[21,22].”
Optimal power allocation in server farms (2009, methodology): “We consider server farms where each incoming job can be routed to any server, i.e., it has no a.nity for a particular server.”
Multi-Objective Virtual Machine Placement in Virtualized Data Center Environments (2010, background): “Currently, power consumption [2][ 9 ][22][27] and thermal dissipation [18] are significant contributors to data center operational costs.”
A Survey on Mobile Edge Computing: The Communication Perspective (2017, methodology): “The other model is based on an observation in recent works [89]–[91] that the”
Minimizing Electricity Cost: Optimization of Distributed Internet Data Centers in a Multi-Electricity-Market Environment (2010, background): “While most of the new technologies have been developed aim to reduce the total power consumptions of IDCs[5], [8], [10], [18], [25], [28], [ 35 ], we are lacking a holistic approach to help better reduce the total electricity cost of IDC service providers.”
On the energy (in)efficiency of Hadoop clusters (2010, methodology): “This clearly demonstrates that this mechanism can be used to implement cluster-level power-capping [7] (with a commensurate reduction in service-level).”
Environment-conscious scheduling of HPC applications on distributed Cloud-oriented data centers (2011, background): “Within a data center, the total energy usage of a server depends on its CPUs, memory, disks, fans, and other components [22].”
EnerJ: approximate data types for safe and general low-power computation (2011, methodology): “To put the potential energy savings in perspective, according to recent studies [12, 24], the CPU and memory together account for well over 50% of the overall system power in servers as well as notebooks.”
Mistral: Dynamically Managing Power, Performance, and Adaptation Cost in Cloud Infrastructures (2010, methodology): “To estimate the power consumption of a configuration, we employ a utilization-based model used in previous studies (e.g., [ 3 ], [6]).”
Dynamo: Facebook's Data Center-Wide Power Management System (2016, methodology): “This characterization uses fine-grained power samples from tens of thousands of servers and spanning a period of over six months.”
A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems (2010, background): “Power consumption to CPU utilization relationship [10].”
TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings (2023, background): “Computer architects improved PUE by helping advance the state-of-the-art of warehouse scale computers (WSCs) [2, 3, 16, 30, 44, 62].”