SlimML: Removing Non- critical Input Data in Large-scale Iterative Machine Learning

The core of many large-scale machine learning (ML) applications, such as neural networks (NN), support vector machine (SVM), and convolutional neural network (CNN), is the training algorithm that iteratively updates model parameters by processing massive datasets. From a plethora of studies aiming at accelerating ML, being data...

POSUM: A Generic Portfolio Scheduler for MapReduce Workloads

MapReduce ecosystems are (still) widely popular for big data processing in data centers. To address the diverse non-functional requirements arising from many and increasingly more sophisticated users, the community has developed many scheduling policies for MapReduce workloads. Although some individual policies can dynamically optimize for...

Hadoop in the browser: Simulating MapReduce in the browser

With the current increase of user generated data, the need for tools to process large quantities of data is increasing. One of the courses of the Computer Science BSc curriculum is the Big Data Processing course. The Big Data Processing course teaches students ways of doing so. A popular and teached method is using MapReduce, a programming model...

A workload model for MapReduce

MapReduce is a parallel programming model used by Cloud service providers for data mining. To be able to enhance existing and to develop new MapReduce sys- tems, we need to evaluate the performance of these systems. To this end we intro- duce in this work the Cloud Workloads Archive Toolbox. This toolbox facilitates the analysis of MapReduce...