An approach for optimization of resource management in Hadoop

Raj, Ravindra; Raju, G Prabhakar

doi:10.1109/iccct2.2014.7066747

Cited by 4 publications

(1 citation statement)

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“…Raj and G. P. Raju addressed plenty of the community developing designs and leading most of the valuable contraptions for Apache Hadoop. In spite of the effectiveness of Hadoop in splintering and restoring huge data, its efficiency can be related to non-stop activities and scattered knowledge [22].Their analysis found that Hadoop follows the computing paradigm of MapReduce that is used for the implementation of endeavours in the decentralized context. C. Yang, C. Yen, C. Tan, S. R. Chafe, [23] based on the need for equivalent bookings, to the degree that they're being done on a distinctive day and generation beyond the boundaries of the client.…”

Section: Related Workmentioning

confidence: 99%

Infrastructure for Time Critical Applications of Big Data Systems Using Light Weight YARN Architecture

Saravanan¹,

Loheswaran

Devi³

et al. 2021

Preprint

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Increasing of humanity and development of Internet resources, storage size is growing with each day, whereby digital records are accessible in clouds of an exploratory format. The immediate future of Big Data is coming shortly for almost all other sectors. Big data can aid in the metamorphosis of significant company operations by offering a recommended and reliable overview of available data. Big data has also figured prominently in the detection of violence. Present framework for designing Big data implementations is capable of processing vast quantities of data through Big data analytics using collections of computing devices together to execute complex processing. Furthermore, existing technologies have not been built to fulfil the specifications of time-critical application areas and are far more oriented on real applications than on time-critical ones. This paper proposes the lightweight architecture called Yet Another Resource Negotiator (YARN), which focuses on the concept of a time-critical big-data system from the perspective of specifications and analyses the essential principles of several common big-data implementations. YARN as the normal computational framework to help MapReduce and another application instances within that Hadoop cluster. YARN requires multiple programs to execute concurrently on a constitutive common server and assent programs to delegate services depending on need. The final evaluation is accompanied by problems stemming from infrastructure and services that serve applications, recommend frameworkand provide preliminary efficiency behaviours that often contribute system impacts to implementation reliability.

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Section: Related Workmentioning

confidence: 99%