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Energy efficiency for large-scale MapReduce workloads with significant interactive analysis
2012
Proceedings of the 7th ACM european conference on Computer Systems - EuroSys '12
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MapReduce workloads have evolved to include increasing amounts of time-sensitive, interactive data analysis; we refer to such workloads as MapReduce with Interactive Analysis (MIA). ...
Such workloads run on large clusters, whose size and cost make energy efficiency a critical concern. Prior works on MapReduce energy efficiency have not yet considered this workload class. ...
This paper focuses on an alternate use case-what we call MapReduce with Interactive Analysis (MIA) workloads. ...
doi:10.1145/2168836.2168842
dblp:conf/eurosys/ChenABK12
fatcat:teiqmhq7nbdvxn3lxypgqwmqyu
Survey on improved Autoscaling in Hadoop into cloud environments
2013
The 5th Conference on Information and Knowledge Technology
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MapReduce-based systems are suited for performing analysis at this scale since they were designed from the beginning to scale to thousands of nodes in a shared-nothing architecture. ...
So MapReduce is one of these methods in order to overcome the complexity of very large data. ...
Hot zone is always powered and the 2 MapReduce with Interactive Analysis 3
Berkeley Energy Efficient MapReduce frequently accessed data is placed in hot zone. ...
doi:10.1109/ikt.2013.6620031
fatcat:3ra3zakwgrd2zaeelc3h3jtrqe
Energy-Efficient Big Data Analytics in Datacenters
[chapter]
2016
Advances in Computers
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We later discuss the techniques for improving energy efficiency in the cloud-based datacenters for big data analytics. ...
Finally, the current and future trends for datacenters in particular with respect to energy consumption to support big data analytics will be discussed. ...
This approach motivated by an empirical analysis of MapReduce interactive workload at Facebook. ...
doi:10.1016/bs.adcom.2015.10.002
fatcat:xpecmdmje5avvphkdrdnyv4fqi
Towards Efficient Power Management in MapReduce: Investigation of CPU-Frequencies Scaling on Power Efficiency in Hadoop
[chapter]
2014
Lecture Notes in Computer Science
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Most of the large-scale data computations in the cloud heavily rely on the MapReduce paradigm and its Hadoop implementation. ...
In this paper, we focus on MapReduce and we investigate the impact of dynamically scaling the frequency of compute nodes on the performance and energy consumption of a Hadoop cluster. ...
[3] present the Berkeley Energy Efficient MapReduce (BEEMR), an energy efficient MapReduce workload manager motivated by empirical analysis of real-life MapReduce with Interactive Analysis (MIA) traces ...
doi:10.1007/978-3-319-13464-2_11
fatcat:5qfsyye4ynd4llg6f2nubgfwfi
A time–energy performance analysis of MapReduce on heterogeneous systems with GPUs
2015
Performance evaluation (Print)
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To investigate this, we perform a time-energy analysis of MapReduce on intra-node and intra-chip heterogeneous systems. ...
Motivated by the explosion of Big Data analytics, performance improvements in low-power (wimpy) systems and the increasing energy efficiency of GPUs, this paper presents a time-energy performance analysis ...
Acknowledgements We are grateful to Nvidia for providing us with four Jetson TK1 boards. ...
doi:10.1016/j.peva.2015.06.015
fatcat:h2e3dk2dwjawdlgc4t3g7g3lme
On the energy (in)efficiency of Hadoop clusters
2010
ACM SIGOPS Operating Systems Review
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s Hadoop and Google's MapReduce, have been successful at harnessing expansive datacenter resources for large-scale data analysis. ...
However, their effect on datacenter energy efficiency has not been scrutinized. ...
Therefore, it is important to understand the energy efficiency of this emerging workload. ...
doi:10.1145/1740390.1740405
fatcat:fch574xvcvaw3hv7ezaykfeq24
Interactive analytical processing in big data systems
2012
Proceedings of the VLDB Endowment
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Along with these new users, important new workloads have emerged which feature many small, short, and increasingly interactive jobs in addition to the large, long-running batch jobs for which MapReduce ...
Within the past few years, organizations in diverse industries have adopted MapReduce-based systems for large-scale data processing. ...
The authors are grateful for the feedback from our colleagues at UC Berkeley AMP Lab, Cloudera, Facebook, and other industrial partners. ...
doi:10.14778/2367502.2367519
fatcat:yhr5kkjyqvggpcdoahw6ls75jy
Interactive Analytical Processing in Big Data Systems: A Cross-Industry Study of MapReduce Workloads
[article]
2012
arXiv
pre-print
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Along with these new users, important new workloads have emerged which feature many small, short, and increasingly interactive jobs in addition to the large, long-running batch jobs for which MapReduce ...
Within the past few years, organizations in diverse industries have adopted MapReduce-based systems for large-scale data processing. ...
The authors are grateful for the feedback from our colleagues at UC Berkeley AMP Lab, Cloudera, Facebook, and other industrial partners. ...
arXiv:1208.4174v1
fatcat:hz52qhcc3bexbpushz3ujsndea
Governing energy consumption in Hadoop through CPU frequency scaling: An analysis
2016
Future generations computer systems
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Most large-scale data computations in the cloud heavily rely on the MapReduce paradigm and on its Hadoop implementation. ...
In this paper, we focus on MapReduce processing and we investigate the impact of dynamically scaling the frequency of compute nodes on the performance and energy consumption of a Hadoop cluster. ...
[41] present the Berkeley Energy Efficient MapReduce (BEEMR), an energy efficient MapReduce workload manager motivated by empirical analysis of real-life MapReduce with Interactive Analysis (MIA) traces ...
doi:10.1016/j.future.2015.01.005
fatcat:tr7ld7ykkjcypgpt4eyr4accmm
Towards a cost-efficient MapReduce: Mitigating power peaks for Hadoop clusters
2014
Tsinghua Science and Technology
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Deploying and operating such systems require large amount of costs, including hardware costs to build clusters and energy costs to run clusters. ...
In this paper, we take Hadoop as an example to illustrate the power peak problem which causes power inefficiency and provides in-depth analysis to explain issues with existing system designs. ...
These MapReduce • Nan Zhu In order to deliver high performance, large-scale data centers have to deal with power-related problems due to large power consumption and electricity bills. ...
doi:10.1109/tst.2014.6733205
fatcat:fboga6hydba5jkdtfsfs5hn4iy
HybridMR: A Hierarchical MapReduce Scheduler for Hybrid Data Centers
2013
2013 IEEE 33rd International Conference on Distributed Computing Systems
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These results indicate that a hybrid data center with an efficient scheduling mechanism can provide a cost-effective solution for hosting both batch and interactive workloads. ...
of interactive and batch workloads. ...
ACKNOWLEDGMENT We thank the anonymous reviewers, Adwait Jog, Amin Jadidi, Mahshid Sedghi, Nachiappan Chidambaram Nachiappan, and Onur Kayiran for their valuable comments towards improving this paper. ...
doi:10.1109/icdcs.2013.31
dblp:conf/icdcs/SharmaWD13
fatcat:aifrfiqwsbd73ldyzcx54zrreq
Not All Joules are Equal: Towards Energy-Efficient and Green-Aware Data Processing Frameworks
2016
2016 IEEE International Conference on Cloud Engineering (IC2E)
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We develop job/task scheduling algorithms with a particular focus on the factors on joule efficiency in the data center, including the energy efficiency of MapReduce workloads, renewable energy supply ...
In this paper, we investigate how to exploit such joule efficiency to maximize the benefits of renewable energy for MapReduce framework. ...
With the battery, we can delay workload to later slots for higher energy efficiency. ...
doi:10.1109/ic2e.2016.17
dblp:conf/ic2e/NiuHL16
fatcat:yywk4lm56ffoxee4stw6s5vobu
Comparing Implementations of Near-Data Computing with In-Memory MapReduce Workloads
2014
IEEE Micro
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The high bandwidth between the logic and memory dies with through-silicon vias (TSVs) can enable significant speedups for memory-bound applications. ...
MapReduce workloads MapReduce applications typically operate on disk-resident data. Large datasets (often key-value pairs) are partitioned into splits. ...
doi:10.1109/mm.2014.54
fatcat:pt4f4bviprftlj2esbcap4yp2e
Big vs little core for energy-efficient Hadoop computing
2017
Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017
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more cost and energy efficient architecture. ...
Heterogeneous architectures that combine big Xeon cores with little Atom cores have emerged as a promising solution to enhance energy-efficiency by allowing each application to run on an architecture that ...
EDP analysis of Map and Reduce phase of Real World applications (a) NB (b) FP on big and little core with frequency scaling
Figure 8 : 8 Results presented are for energy-efficiency (EDP), real time ...
doi:10.23919/date.2017.7927225
dblp:conf/date/MalikNMSH17
fatcat:vpysnphlcvehfgjtre7g7wakli
An Energy Efficiency Optimization and Control Model for Hadoop Clusters
2019
IEEE Access
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The majority of large-scale data intensive applications designed by MapReduce model are deployed and executed on a large-scale distributed Hadoop system. ...
Therefore improving energy efficiency and minimizing energy consumption when executing each MapReduce job is a critical concern for data centers. ...
MapReduce proposed by Google is a programming model for data intensive computing on Large-scale distributed systems [1] . ...
doi:10.1109/access.2019.2907018
fatcat:bv6jai2yojcbvijztadeotpmcy
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