1,069 research outputs found
MapReduce is Good Enough? If All You Have is a Hammer, Throw Away Everything That's Not a Nail!
Hadoop is currently the large-scale data analysis "hammer" of choice, but
there exist classes of algorithms that aren't "nails", in the sense that they
are not particularly amenable to the MapReduce programming model. To address
this, researchers have proposed MapReduce extensions or alternative programming
models in which these algorithms can be elegantly expressed. This essay
espouses a very different position: that MapReduce is "good enough", and that
instead of trying to invent screwdrivers, we should simply get rid of
everything that's not a nail. To be more specific, much discussion in the
literature surrounds the fact that iterative algorithms are a poor fit for
MapReduce: the simple solution is to find alternative non-iterative algorithms
that solve the same problem. This essay captures my personal experiences as an
academic researcher as well as a software engineer in a "real-world" production
analytics environment. From this combined perspective I reflect on the current
state and future of "big data" research
Fluid Petri Nets for the Performance Evaluation of MapReduce Applications
Big Data applications allow to successfully analyze large amounts of data not necessarily structured, though at the same time they present new challenges. For example, predicting the performance of frameworks such as Hadoop can be a costly task, hence the necessity to provide models that can be a valuable support for designers and developers. This paper provides a new contribution in studying a novel modeling approach based on fluid Petri nets to predict MapReduce jobs execution time.
The experiments we performed at CINECA, the Italian supercomputing center, have shown that the achieved accuracy is within 16% of the actual measurements on average
Modeling performance of Hadoop applications: A journey from queueing networks to stochastic well formed nets
Nowadays, many enterprises commit to the extraction of actionable knowledge from huge datasets as part of their core business activities. Applications belong to very different domains such as fraud detection or one-to-one marketing, and encompass business analytics and support to decision making in both private and public sectors. In these scenarios, a central place is held by the MapReduce framework and in particular its open source implementation, Apache Hadoop. In such environments, new challenges arise in the area of jobs performance prediction, with the needs to provide Service Level Agreement guarantees to the enduser and to avoid waste of computational resources. In this paper we provide performance analysis models to estimate MapReduce job execution times in Hadoop clusters governed by the YARN Capacity Scheduler. We propose models of increasing complexity and accuracy, ranging from queueing networks to stochastic well formed nets, able to estimate job performance under a number of scenarios of interest, including also unreliable resources. The accuracy of our models is evaluated by considering the TPC-DS industry benchmark running experiments on Amazon EC2 and the CINECA Italian supercomputing center. The results have shown that the average accuracy we can achieve is in the range 9–14%
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