Two Reconfigurable NDP Servers: Understanding the Impact of Near-Data Processing on Data Center Applications

2021 ◽  
Vol 17 (4) ◽  
pp. 1-27
Author(s):  
Xiaojia Song ◽  
Tao Xie ◽  
Stephen Fischer
Keyword(s):  
Energy Efficiency ◽  
Data Processing ◽  
Data Center ◽  
Experimental Results ◽  
Full Size ◽  
Data Intensive ◽  
Processing Rate ◽  
New Findings ◽  
Data Intensive Applications ◽  
The Impact

Existing near-data processing (NDP)-powered architectures have demonstrated their strength for some data-intensive applications. Data center servers, however, have to serve not only data-intensive but also compute-intensive applications. An in-depth understanding of the impact of NDP on various data center applications is still needed. For example, can a compute-intensive application also benefit from NDP? In addition, current NDP techniques focus on maximizing the data processing rate by always utilizing all computing resources at all times. Is this “always running in full gear” strategy consistently beneficial for an application? To answer these questions, we first propose two reconfigurable NDP-powered servers called RANS ( R econfigurable A RM-based N DP S erver) and RFNS ( R econfigurable F PGA-based N DP S erver). Next, we implement a single-engine prototype for each of them based on a conventional data center and then evaluate their effectiveness. Experimental results measured from the two prototypes are then extrapolated to estimate the properties of the two full-size reconfigurable NDP servers. Finally, several new findings are presented. For example, we find that while RANS can only benefit data-intensive applications, RFNS can offer benefits for both data-intensive and compute-intensive applications. Moreover, we find that for certain applications the reconfigurability of RANS/RFNS can deliver noticeable energy efficiency without any performance degradation.

NoSQL Databases

2014 ◽  
pp. 186-215 ◽  
Author(s):  
Ganesh Chandra Deka
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Open Source ◽  
Data Storage ◽  
Big Data Processing ◽  
Nosql Databases ◽  
Data Intensive ◽  
Huge Data ◽  
Data Intensive Applications

NoSQL databases are designed to meet the huge data storage requirements of cloud computing and big data processing. NoSQL databases have lots of advanced features in addition to the conventional RDBMS features. Hence, the “NoSQL” databases are popularly known as “Not only SQL” databases. A variety of NoSQL databases having different features to deal with exponentially growing data-intensive applications are available with open source and proprietary option. This chapter discusses some of the popular NoSQL databases and their features on the light of CAP theorem.

From Chip to Cooling Tower Data Center Modeling: Chip Leakage Power and its Impact on Cooling Infrastructure Energy Efficiency

2011 ◽  
Author(s):  
Thomas J. Breen ◽  
Ed J. Walsh ◽  
Jeff Punch ◽  
Amip J. Shah ◽  
Cullen E. Bash ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Power Consumption ◽  
Data Center ◽  
Heat Dissipation ◽  
Linear Models ◽  
Operating Temperature ◽  
Leakage Power ◽  
Operating Conditions ◽  
Operating Temperatures ◽  
The Impact

The power consumption of the chip package is known to vary with operating temperature, independently of the workload processing power. This variation is commonly known as chip leakage power, typically accounting for ∼10% of total chip power consumption. The influence of operating temperature on leakage power consumption is a major concern for the IT industry for design optimization where IT system power densities are steadily increasing and leakage power expected to account for up to ∼50% of chip power in the near future associated with the reducing package size. Much attention has been placed on developing models of the chip leakage power as a function of package temperature, ranging from simple linear models to complex super-linear models. This knowledge is crucial for IT system designers to improve chip level energy efficiency and minimize heat dissipation. However, this work has been focused on the component level with little thought given to the impact of chip leakage power on entire data center efficiency. Studies on data center power consumption quote IT system heat dissipation as a constant value without accounting for the variance of chip power with operating temperature due to leakage power. Previous modeling techniques have also omitted this temperature dependent relationship. In this paper we discuss the need for chip leakage power to be included in the analysis of holistic data center performance. A chip leakage power model is defined and its implementation into an existing multi-scale data center energy model is discussed. Parametric studies are conducted over a range of system and environment operating conditions to evaluate the impact of varying degrees of chip leakage power. Possible strategies for mitigating the impact of leakage power are also illustrated in this study. This work illustrates that when including chip leakage power in the data center model, a compromise exists between increasing operating temperatures to improve cooling infrastructure efficiency and the increase in heat load at higher operating temperatures due to leakage power.

From Chip to Cooling Tower Data Center Modeling: Chip Leakage Power and Its Impact on Cooling Infrastructure Energy Efficiency

2012 ◽  
Vol 134 (4) ◽  
Author(s):  
Thomas J. Breen ◽  
Ed J. Walsh ◽  
Jeff Punch ◽  
Amip J. Shah ◽  
Cullen E. Bash ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Power Consumption ◽  
Data Center ◽  
Heat Dissipation ◽  
Linear Models ◽  
Operating Temperature ◽  
Leakage Power ◽  
Operating Conditions ◽  
Operating Temperatures ◽  
The Impact

The power consumption of the chip package is known to vary with operating temperature, independently of the workload processing power. This variation is commonly known as chip leakage power, typically accounting for ~10% of total chip power consumption. The influence of operating temperature on leakage power consumption is a major concern for the information technology (IT) industry for design optimization where IT system power densities are steadily increasing and leakage power expected to account for up to ~50% of chip power in the near future associated with the reducing package size. Much attention has been placed on developing models of the chip leakage power as a function of package temperature, ranging from simple linear models to complex super-linear models. This knowledge is crucial for IT system designers to improve chip level energy efficiency and minimize heat dissipation. However, this work has been focused on the component level with little thought given to the impact of chip leakage power on entire data center efficiency. Studies on data center power consumption quote IT system heat dissipation as a constant value without accounting for the variance of chip power with operating temperature due to leakage power. Previous modeling techniques have also omitted this temperature dependent relationship. In this paper, we discuss the need for chip leakage power to be included in the analysis of holistic data center performance. A chip leakage power model is defined and its implementation into an existing multiscale data center energy model is discussed. Parametric studies are conducted over a range of system and environment operating conditions to evaluate the impact of varying degrees of chip leakage power. Possible strategies for mitigating the impact of leakage power are also illustrated in this study. This work illustrates that when including chip leakage power in the data center model, a compromise exists between increasing operating temperatures to improve cooling infrastructure efficiency and the increase in heat load at higher operating temperatures due to leakage power.

scholarly journals Energy Efficiency Metrics of University Data Centers

2018 ◽  
Vol 1 (2) ◽  
pp. 64
Author(s):  
Lenonel Hernandez ◽  
Genett Jimenez ◽  
Piedad Marchena
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Large Volume ◽  
Data Center ◽  
Data Centers ◽  
Monitoring And Control ◽  
And Control ◽  
The Impact ◽  
Computing Infrastructure

The data centers are fundamental pieces in the network and computing infrastructure, and evidently today more than ever they are relevant. Since they support the processing, analysis, assurance of the data generated in the network and by the applications in the cloud, which every day increases its volume thanks to technologies such as Internet of Things, Virtualization, and cloud computing, among others. Precisely the management of this large volume of information makes the data centers consume a lot of energy, generating great concern to owners and administrators. Green Data Centers offer a solution to this problem, reducing the impact produced by the data centers in the environment, through the monitoring and control of these. The metrics are the tools that allow us to measure in our case the energy efficiency of the data center and evaluate if it is friendly to the environment. These metrics will be applied to the data centers of the ITSA University Institution, Barranquilla and Soledad campus, and the analysis of these will be carried out. In previous research, the most common metric (PUE) was analyzed to measure the efficiency of the data centers, to verify if the University's data center is friendly to the environment. It is planned to extend this study by carrying out an analysis of several metrics to conclude which is the most efficient and which allows defining the guidelines to update or convert the data center in a friendly environment. 

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