Journal ArticleDOI
Experimental and quantitative analysis of server power model for cloud data centers
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TLDR
The ideology of component-level power modeling presented in this paper helps realize fine-grained power control and provides CSPs with useful guidance on optimizing energy management of cloud data centers.About:Â
This article is published in Future Generation Computer Systems.The article was published on 2018-09-01. It has received 33 citations till now. The article focuses on the topics: Server farm & Cloud computing.read more
Citations
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Journal ArticleDOI
Q-learning based dynamic task scheduling for energy-efficient cloud computing
TL;DR: Simulation experiments have confirmed that implementing a M/M/S queueing system in a cloud can help to reduce the average task response time, and demonstrated that the QEEC approach is the most energy-efficient as compared to other task scheduling policies.
Journal ArticleDOI
Using the internet of things in smart energy systems and networks
TL;DR: A clear insight into IoT devices' recent developments in smart energy systems is provided, supported by high-quality published literature, and key industries for IoT revenue generation and application development are described.
Journal ArticleDOI
Accelerating Federated Learning over Reliability-Agnostic Clients in Mobile Edge Computing Systems
TL;DR: The results show that HybridFL improves the FL training process significantly in terms of shortening the federated round length, speeding up the global model’s convergence and reducing end device energy consumption.
Journal ArticleDOI
A cloud server energy consumption measurement system for heterogeneous cloud environments
TL;DR: Experimental studies conducted on a heterogeneous cluster with workloads generated by PCMark and Sysbench demonstrate that the proposed DEM system outperforms the state-of-art models in estimating the energy consumption of heterogeneous cloud environments.
Journal ArticleDOI
An Artificial Neural Network Approach to Power Consumption Model Construction for Servers in Cloud Data Centers
TL;DR: The ANN (Artificial Neural Network) method is proposed to model the power consumption of the servers in datacenters, a kind of end-to-end black box model, and results show that the proposed three power models have better performance in predicting the server's real-time power consumption.
References
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Journal ArticleDOI
Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing
TL;DR: An architectural framework and principles for energy-efficient Cloud computing are defined and the proposed energy-aware allocation heuristics provision data center resources to client applications in a way that improves energy efficiency of the data center, while delivering the negotiated Quality of Service (QoS).
Proceedings ArticleDOI
Power provisioning for a warehouse-sized computer
TL;DR: This paper presents the aggregate power usage characteristics of large collections of servers for different classes of applications over a period of approximately six months, and uses the modelling framework to estimate the potential of power management schemes to reduce peak power and energy usage.
Proceedings ArticleDOI
ElasticTree: saving energy in data center networks
Brandon Heller,Srini Seetharaman,Priya Mahadevan,Yiannis Yiakoumis,Puneet Sharma,Sujata Banerjee,Nick McKeown +6 more
TL;DR: This work presents ElasticTree, a network-wide power1 manager, which dynamically adjusts the set of active network elements -- links and switches--to satisfy changing data center traffic loads, and demonstrates that for data center workloads, ElasticTree can save up to 50% of network energy, while maintaining the ability to handle traffic surges.
Proceedings ArticleDOI
pMapper: power and migration cost aware application placement in virtualized systems
TL;DR: This work investigates the design, implementation, and evaluation of a power-aware application placement controller in the context of an environment with heterogeneous virtualized server clusters, and presents the pMapper architecture and placement algorithms to solve one practical formulation of the problem: minimizing power subject to a fixed performance requirement.
Proceedings ArticleDOI
Virtual machine power metering and provisioning
TL;DR: Joulemeter builds power models to infer power consumption from resource usage at runtime and identifies the challenges that arise when applying such models for VM power metering, and shows how existing instrumentation in server hardware and hypervisors can be used to build the required power models on real platforms with low error.