3,897 research outputs found
A Big Data Analyzer for Large Trace Logs
Current generation of Internet-based services are typically hosted on large
data centers that take the form of warehouse-size structures housing tens of
thousands of servers. Continued availability of a modern data center is the
result of a complex orchestration among many internal and external actors
including computing hardware, multiple layers of intricate software, networking
and storage devices, electrical power and cooling plants. During the course of
their operation, many of these components produce large amounts of data in the
form of event and error logs that are essential not only for identifying and
resolving problems but also for improving data center efficiency and
management. Most of these activities would benefit significantly from data
analytics techniques to exploit hidden statistical patterns and correlations
that may be present in the data. The sheer volume of data to be analyzed makes
uncovering these correlations and patterns a challenging task. This paper
presents BiDAl, a prototype Java tool for log-data analysis that incorporates
several Big Data technologies in order to simplify the task of extracting
information from data traces produced by large clusters and server farms. BiDAl
provides the user with several analysis languages (SQL, R and Hadoop MapReduce)
and storage backends (HDFS and SQLite) that can be freely mixed and matched so
that a custom tool for a specific task can be easily constructed. BiDAl has a
modular architecture so that it can be extended with other backends and
analysis languages in the future. In this paper we present the design of BiDAl
and describe our experience using it to analyze publicly-available traces from
Google data clusters, with the goal of building a realistic model of a complex
data center.Comment: 26 pages, 10 figure
Efficient Implementation of the Room Simulator for Training Deep Neural Network Acoustic Models
In this paper, we describe how to efficiently implement an acoustic room
simulator to generate large-scale simulated data for training deep neural
networks. Even though Google Room Simulator in [1] was shown to be quite
effective in reducing the Word Error Rates (WERs) for far-field applications by
generating simulated far-field training sets, it requires a very large number
of Fast Fourier Transforms (FFTs) of large size. Room Simulator in [1] used
approximately 80 percent of Central Processing Unit (CPU) usage in our CPU +
Graphics Processing Unit (GPU) training architecture [2]. In this work, we
implement an efficient OverLap Addition (OLA) based filtering using the
open-source FFTW3 library. Further, we investigate the effects of the Room
Impulse Response (RIR) lengths. Experimentally, we conclude that we can cut the
tail portions of RIRs whose power is less than 20 dB below the maximum power
without sacrificing the speech recognition accuracy. However, we observe that
cutting RIR tail more than this threshold harms the speech recognition accuracy
for rerecorded test sets. Using these approaches, we were able to reduce CPU
usage for the room simulator portion down to 9.69 percent in CPU/GPU training
architecture. Profiling result shows that we obtain 22.4 times speed-up on a
single machine and 37.3 times speed up on Google's distributed training
infrastructure.Comment: Published at INTERSPEECH 2018.
(https://www.isca-speech.org/archive/Interspeech_2018/abstracts/2566.html
Towards Data-Driven Autonomics in Data Centers
Continued reliance on human operators for managing data centers is a major
impediment for them from ever reaching extreme dimensions. Large computer
systems in general, and data centers in particular, will ultimately be managed
using predictive computational and executable models obtained through
data-science tools, and at that point, the intervention of humans will be
limited to setting high-level goals and policies rather than performing
low-level operations. Data-driven autonomics, where management and control are
based on holistic predictive models that are built and updated using generated
data, opens one possible path towards limiting the role of operators in data
centers. In this paper, we present a data-science study of a public Google
dataset collected in a 12K-node cluster with the goal of building and
evaluating a predictive model for node failures. We use BigQuery, the big data
SQL platform from the Google Cloud suite, to process massive amounts of data
and generate a rich feature set characterizing machine state over time. We
describe how an ensemble classifier can be built out of many Random Forest
classifiers each trained on these features, to predict if machines will fail in
a future 24-hour window. Our evaluation reveals that if we limit false positive
rates to 5%, we can achieve true positive rates between 27% and 88% with
precision varying between 50% and 72%. We discuss the practicality of including
our predictive model as the central component of a data-driven autonomic
manager and operating it on-line with live data streams (rather than off-line
on data logs). All of the scripts used for BigQuery and classification analyses
are publicly available from the authors' website.Comment: 12 pages, 6 figure
Towards Operator-less Data Centers Through Data-Driven, Predictive, Proactive Autonomics
Continued reliance on human operators for managing data centers is a major
impediment for them from ever reaching extreme dimensions. Large computer
systems in general, and data centers in particular, will ultimately be managed
using predictive computational and executable models obtained through
data-science tools, and at that point, the intervention of humans will be
limited to setting high-level goals and policies rather than performing
low-level operations. Data-driven autonomics, where management and control are
based on holistic predictive models that are built and updated using live data,
opens one possible path towards limiting the role of operators in data centers.
In this paper, we present a data-science study of a public Google dataset
collected in a 12K-node cluster with the goal of building and evaluating
predictive models for node failures. Our results support the practicality of a
data-driven approach by showing the effectiveness of predictive models based on
data found in typical data center logs. We use BigQuery, the big data SQL
platform from the Google Cloud suite, to process massive amounts of data and
generate a rich feature set characterizing node state over time. We describe
how an ensemble classifier can be built out of many Random Forest classifiers
each trained on these features, to predict if nodes will fail in a future
24-hour window. Our evaluation reveals that if we limit false positive rates to
5%, we can achieve true positive rates between 27% and 88% with precision
varying between 50% and 72%.This level of performance allows us to recover
large fraction of jobs' executions (by redirecting them to other nodes when a
failure of the present node is predicted) that would otherwise have been wasted
due to failures. [...
DC-Prophet: Predicting Catastrophic Machine Failures in DataCenters
When will a server fail catastrophically in an industrial datacenter? Is it
possible to forecast these failures so preventive actions can be taken to
increase the reliability of a datacenter? To answer these questions, we have
studied what are probably the largest, publicly available datacenter traces,
containing more than 104 million events from 12,500 machines. Among these
samples, we observe and categorize three types of machine failures, all of
which are catastrophic and may lead to information loss, or even worse,
reliability degradation of a datacenter. We further propose a two-stage
framework-DC-Prophet-based on One-Class Support Vector Machine and Random
Forest. DC-Prophet extracts surprising patterns and accurately predicts the
next failure of a machine. Experimental results show that DC-Prophet achieves
an AUC of 0.93 in predicting the next machine failure, and a F3-score of 0.88
(out of 1). On average, DC-Prophet outperforms other classical machine learning
methods by 39.45% in F3-score.Comment: 13 pages, 5 figures, accepted by 2017 ECML PKD
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