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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2018
عنوان انگلیسی مقاله:
Big vs little core for energy-efficient Hadoop computing
ترجمه فارسی عنوان مقاله:
بزرگ در مقابل هسته برای محاسبات هادوپ انرژی کارآمد
منبع:
Sciencedirect - Elsevier - J: Parallel Distrib: Comput:, Corrected proof: doi:10:1016/j:jpdc:2018:02:017
نویسنده:
Maria Malik a,*, Katayoun Neshatpour a, Setareh Rafatirad b, Rajiv V. Joshi e, Tinoosh Mohsenin c, Hassan Ghasemzadeh d, Houman Homayoun a
چکیده انگلیسی:
Emerging big data applications require a significant amount of server computational power. However,
the rapid growth in the data yields challenges to process them efficiently using current high-performance
server architectures. Furthermore, physical design constraints, such as power and density, have become
the dominant limiting factor for scaling out servers. 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 matches resource needs more closely than a one
size-fits-all architecture. Therefore, the question of whether to map the application to big Xeon or little
Atom in heterogeneous server architecture becomes important. In this paper, through a comprehensive
system level analysis, we first characterize Hadoop-based MapReduce applications on big Xeon and
little Atom-based server architectures to understand how the choice of big vs little cores is affected
by various parameters at application, system and architecture levels and the interplay among these
parameters. Second, we study how the choice between big and little core changes across various phases
of MapReduce tasks. Furthermore, we show how the choice of most efficient core for a particular
MapReduce phase changes in the presence of accelerators. The characterization analysis helps guiding
scheduling decisions in future cloud-computing environment equipped with heterogeneous multicore
architectures and accelerators. We have also evaluated the operational and the capital cost to understand
how performance, power and area constraints for big data analytics affect the choice of big vs little core
server as a more cost and energy efficient architecture.
Keywords: Heterogeneous architectures ، Hadoop ، MapReduce ، Energy and cost efficiency ، Big and little cores ، Scheduling
قیمت: رایگان
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