59,351 research outputs found
Computing server power modeling in a data center: survey,taxonomy and performance evaluation
Data centers are large scale, energy-hungry infrastructure serving the
increasing computational demands as the world is becoming more connected in
smart cities. The emergence of advanced technologies such as cloud-based
services, internet of things (IoT) and big data analytics has augmented the
growth of global data centers, leading to high energy consumption. This upsurge
in energy consumption of the data centers not only incurs the issue of surging
high cost (operational and maintenance) but also has an adverse effect on the
environment. Dynamic power management in a data center environment requires the
cognizance of the correlation between the system and hardware level performance
counters and the power consumption. Power consumption modeling exhibits this
correlation and is crucial in designing energy-efficient optimization
strategies based on resource utilization. Several works in power modeling are
proposed and used in the literature. However, these power models have been
evaluated using different benchmarking applications, power measurement
techniques and error calculation formula on different machines. In this work,
we present a taxonomy and evaluation of 24 software-based power models using a
unified environment, benchmarking applications, power measurement technique and
error formula, with the aim of achieving an objective comparison. We use
different servers architectures to assess the impact of heterogeneity on the
models' comparison. The performance analysis of these models is elaborated in
the paper
Examining Future Data Center Power Supply Infrastructures
The rapid expansion of data processing in the past few years has created a massive demand for data center installations worldwide, and energy conservation strategies have become crucial. The enormous increase in data center installations and their significant contribution to global energy consumption require the implementing of energy saving techniques and participating in supporting the power grid. This thesis presents an architecture-level review of power distribution systems in data centers, examining AC, DC, and hybrid architectures with a focus on enhancing efficiency and reliability One of the key areas that can be enhanced to improve the overall energy efficiency of data centers and the provision of ancillary services for the grid is the Uninterruptible Power Supplies (UPS). This thesis reviews the current state-of-the-art power supply systems and topologies mainly used in data centers and aims to identify ways to increase the overall energy efficiency of data center power supply systems. Moreover, this work presents a detailed analysis of the power supply losses and proposes systems that can improve the con- version efficiency of UPS systems under various loading conditions. The performance metrics in the data center business need to be more accurate. Therefore, the variety of performance metrics, considering energy efficiency, sustainability, reliability and costs, are analysed in the thesis. The conclusion of the thesis wraps up the findings and provides guidelines for planning the power supply infrastructure for various conditions
A Survey of Green Networking Research
Reduction of unnecessary energy consumption is becoming a major concern in
wired networking, because of the potential economical benefits and of its
expected environmental impact. These issues, usually referred to as "green
networking", relate to embedding energy-awareness in the design, in the devices
and in the protocols of networks. In this work, we first formulate a more
precise definition of the "green" attribute. We furthermore identify a few
paradigms that are the key enablers of energy-aware networking research. We
then overview the current state of the art and provide a taxonomy of the
relevant work, with a special focus on wired networking. At a high level, we
identify four branches of green networking research that stem from different
observations on the root causes of energy waste, namely (i) Adaptive Link Rate,
(ii) Interface proxying, (iii) Energy-aware infrastructures and (iv)
Energy-aware applications. In this work, we do not only explore specific
proposals pertaining to each of the above branches, but also offer a
perspective for research.Comment: Index Terms: Green Networking; Wired Networks; Adaptive Link Rate;
Interface Proxying; Energy-aware Infrastructures; Energy-aware Applications.
18 pages, 6 figures, 2 table
Diluting the Scalability Boundaries: Exploring the Use of Disaggregated Architectures for High-Level Network Data Analysis
Traditional data centers are designed with a rigid architecture of
fit-for-purpose servers that provision resources beyond the average workload in
order to deal with occasional peaks of data. Heterogeneous data centers are
pushing towards more cost-efficient architectures with better resource
provisioning. In this paper we study the feasibility of using disaggregated
architectures for intensive data applications, in contrast to the monolithic
approach of server-oriented architectures. Particularly, we have tested a
proactive network analysis system in which the workload demands are highly
variable. In the context of the dReDBox disaggregated architecture, the results
show that the overhead caused by using remote memory resources is significant,
between 66\% and 80\%, but we have also observed that the memory usage is one
order of magnitude higher for the stress case with respect to average
workloads. Therefore, dimensioning memory for the worst case in conventional
systems will result in a notable waste of resources. Finally, we found that,
for the selected use case, parallelism is limited by memory. Therefore, using a
disaggregated architecture will allow for increased parallelism, which, at the
same time, will mitigate the overhead caused by remote memory.Comment: 8 pages, 6 figures, 2 tables, 32 references. Pre-print. The paper
will be presented during the IEEE International Conference on High
Performance Computing and Communications in Bangkok, Thailand. 18 - 20
December, 2017. To be published in the conference proceeding
Performance and Power Analysis of HPC Workloads on Heterogenous Multi-Node Clusters
Performance analysis tools allow application developers to identify and characterize the inefficiencies that cause performance degradation in their codes, allowing for application optimizations. Due to the increasing interest in the High Performance Computing (HPC) community towards energy-efficiency issues, it is of paramount importance to be able to correlate performance and power figures within the same profiling and analysis tools. For this reason, we present a performance and energy-efficiency study aimed at demonstrating how a single tool can be used to collect most of the relevant metrics. In particular, we show how the same analysis techniques can be applicable on different architectures, analyzing the same HPC application on a high-end and a low-power cluster. The former cluster embeds Intel Haswell CPUs and NVIDIA K80 GPUs, while the latter is made up of NVIDIA Jetson TX1 boards, each hosting an Arm Cortex-A57 CPU and an NVIDIA Tegra X1 Maxwell GPU.The research leading to these results has received funding from the European Community’s Seventh Framework Programme [FP7/2007-2013] and Horizon 2020 under the Mont-Blanc projects [17], grant agreements n. 288777, 610402 and 671697. E.C. was partially founded by “Contributo 5 per mille assegnato all’Università degli Studi di Ferrara-dichiarazione dei redditi dell’anno 2014”. We thank the University of Ferrara and INFN Ferrara for the access to the COKA Cluster. We warmly thank the BSC tools group, supporting us for the smooth integration and test of our setup within Extrae and Paraver.Peer ReviewedPostprint (published version
Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions
Traditional power grids are being transformed into Smart Grids (SGs) to
address the issues in existing power system due to uni-directional information
flow, energy wastage, growing energy demand, reliability and security. SGs
offer bi-directional energy flow between service providers and consumers,
involving power generation, transmission, distribution and utilization systems.
SGs employ various devices for the monitoring, analysis and control of the
grid, deployed at power plants, distribution centers and in consumers' premises
in a very large number. Hence, an SG requires connectivity, automation and the
tracking of such devices. This is achieved with the help of Internet of Things
(IoT). IoT helps SG systems to support various network functions throughout the
generation, transmission, distribution and consumption of energy by
incorporating IoT devices (such as sensors, actuators and smart meters), as
well as by providing the connectivity, automation and tracking for such
devices. In this paper, we provide a comprehensive survey on IoT-aided SG
systems, which includes the existing architectures, applications and prototypes
of IoT-aided SG systems. This survey also highlights the open issues,
challenges and future research directions for IoT-aided SG systems
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