MapReduce and its applications, challenges, and architecture: a comprehensive review and directions for future research
SN Khezr, NJ Navimipour - Journal of Grid Computing, 2017 - Springer
Profound attention to MapReduce framework has been caught by many different areas. It is
presently a practical model for data-intensive applications due to its simple interface of …
presently a practical model for data-intensive applications due to its simple interface of …
Advances in MapReduce big data processing: platform, tools, and algorithms
L Abualigah, BA Masri - Artificial intelligence and IoT: Smart convergence …, 2021 - Springer
This paper reviews the research progress of big data processing platforms and algorithms
based on MapReduce programming model in recent years. Firstly, 12 typical ones are …
based on MapReduce programming model in recent years. Firstly, 12 typical ones are …
Deep learning techniques for optimizing medical big data
Medical organizations are progressively navigating a highly unstable, complex environment
in which scientific improvements and new medical delivery business models are only …
in which scientific improvements and new medical delivery business models are only …
Improving the performance of Hadoop Hive by sharing scan and computation tasks
MapReduce is a popular programming model for executing time-consuming analytical
queries as a batch of tasks on large scale data clusters. In environments where multiple …
queries as a batch of tasks on large scale data clusters. In environments where multiple …
Pisces: optimizing multi-job application execution in mapreduce
Nowadays, many MapReduce applications consist of groups of jobs with dependencies
among each other, such as iterative machine learning applications and large database …
among each other, such as iterative machine learning applications and large database …
FlowFlex: Malleable Scheduling for Flows of MapReduce Jobs
We introduce FlowFlex, a highly generic and effective scheduler for flows of MapReduce
jobs connected by precedence constraints. Such a flow can result, for example, from a single …
jobs connected by precedence constraints. Such a flow can result, for example, from a single …
Fair multi-agent task allocation for large datasets analysis
MapReduce is a design pattern for processing large datasets distributed on a cluster. Its
performances are linked to the data structure and the runtime environment. Indeed, data …
performances are linked to the data structure and the runtime environment. Indeed, data …
Map reduce overview and functionality
S kour Siledar, B Deogaonkar… - 2021 6th …, 2021 - ieeexplore.ieee.org
In today's modern world the data has become one of the essential entities to be taken care of
properly. New social media platforms that have emerged during last decade, has plethora of …
properly. New social media platforms that have emerged during last decade, has plethora of …
The X-flex cross-platform scheduler: who's the fairest of them all?
We introduce the X-Flex cross-platform scheduler. X-Flex is intended as an alternative to the
Dominant Resource Fairness (DRF) scheduler currently employed by both YARN and …
Dominant Resource Fairness (DRF) scheduler currently employed by both YARN and …
Malleable scheduling for flows of jobs and applications to MapReduce
This paper provides a unified family of algorithms with performance guarantees for
malleable scheduling problems on flows. A flow represents a set of jobs with precedence …
malleable scheduling problems on flows. A flow represents a set of jobs with precedence …