Map-Reduce Implementations: Survey and Performance Comparison
نویسندگان
چکیده
منابع مشابه
Map-reduce Implementations: Survey and Performance Comparison
Map Reduce has gained remarkable significance as a prominent parallel data processing tool in the research community, academia and industry with the spurt in volume of data that is to be analyzed. Map Reduce is used in different applications such as data mining, data analytics where massive data analysis is required, but still it is constantly being explored on different parameters such as perf...
متن کاملAlgorithms Using Map Reduce-a Survey
Despite increasing data volumes much faster than compute power. This growth demands new strategies for processing and analyzing information. Organizations are determining that significant forecasting can be through sorting and analyze Big Data. Ever since a large amount of data is "amorphous", it should be structured in a manner which is appropriate for mining and succeeding analysis. Hadoop he...
متن کاملPerformance Comparison of Software Transactional Memory Implementations
KARIATH, RIYA RAJU. Performance Comparison of Software Transactional Memory Implementations. (Under the direction of Dr. Edward F Gehringer.) Software Transactional Memory (STM), an optimistic concurrency control mechanism for controlling accesses to shared memory, is a promising alternative to lockbased mutual exclusion strategies. A transaction in this context is each piece of code that execu...
متن کاملSIP Signaling Implementations and Performance Enhancement over MANET: A Survey
The implementation of the Session Initiation Protocol (SIP)-based Voice over Internet Protocol (VoIP) and multimedia over MANET is still a challenging issue. Many routing factors affect the performance of SIP signaling and the voice Quality of Service (QoS). Node mobility in MANET causes dynamic changes to route calculations, topology, hop numbers, and the connectivity status between the corres...
متن کاملComplexity Measures for Map-Reduce, and Comparison to Parallel Computing
The programming paradigm Map-Reduce [3] and its main open-source implementation, Hadoop [1], have had an enormous impact on large scale data processing. Our goal in this expository writeup is twofold: first, we want to present some complexity measures that allow us to talk about Map-Reduce algorithms formally, and second, we want to point out why this model is actually different from other mode...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Computer Science and Information Technology
سال: 2015
ISSN: 0975-4660,0975-3826
DOI: 10.5121/ijcsit.2015.7410