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As data sets grow in size, analytics applications struggle to get instant insight into large datasets. Modern applications involve heavy batch processing jobs over large volumes of data and at the same time require efficient ad-hoc interactive analytics on temporary data. Existing solutions, however, typically focus on one of these two aspects, largely ignoring the need for synergy between the two...
Many resource management systems and large-scale data processing frameworks use a reservation-based model for managing resources and scheduling tasks. We observe from the reported traces of Facebook and Google that this model leads to resource being wasted because the tasks do not use effectively the allocated resources. We confirm the problem with a trace of our production cluster. We propose an...
Extracting value from data stored in object stores,such as OpenStack Swift and Amazon S3, can be problematicin common scenarios where analytics frameworks and objectstores run in physically disaggregated clusters. One of the mainproblems is that analytics frameworks must ingest large amountsof data from the object store prior to the actual computation;this incurs a significant resources and performance...
In this work we study the I/O performance of long, sequential workloads that mimic those of Big Data applications, to understand the implications of system virtualization on data-intensive frameworks such as Apache Hadoop and Spark, which are frequently run in clusters of Virtual Machines (VMs). We do so through an experimental measurement campaign that collects low-level traces and metrics, to show...
Frequent closed itemset mining is among the most complex exploratory techniques in data mining, and provides the ability to discover hidden correlations in transactional datasets. The explosion of Big Data is leading to new parallel and distributed approaches. Unfortunately, most of them are designed to cope with low-dimensional datasets, whereas no distributed high-dimensional frequent closed itemset...
In this work we investigate the impact of virtualization on the raw network performance attainable by "data-intensive" applications deployed in a private cloud. To this end we developed a new software tool, called OSMeF, to take repeatable measurements on our Open Stack-based platform. We also discuss the implications of our measurement results toward informed deployments of distributed...
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