2,496 research outputs found
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. [...
Technical Report: A Trace-Based Performance Study of Autoscaling Workloads of Workflows in Datacenters
To improve customer experience, datacenter operators offer support for
simplifying application and resource management. For example, running workloads
of workflows on behalf of customers is desirable, but requires increasingly
more sophisticated autoscaling policies, that is, policies that dynamically
provision resources for the customer. Although selecting and tuning autoscaling
policies is a challenging task for datacenter operators, so far relatively few
studies investigate the performance of autoscaling for workloads of workflows.
Complementing previous knowledge, in this work we propose the first
comprehensive performance study in the field. Using trace-based simulation, we
compare state-of-the-art autoscaling policies across multiple application
domains, workload arrival patterns (e.g., burstiness), and system utilization
levels. We further investigate the interplay between autoscaling and regular
allocation policies, and the complexity cost of autoscaling. Our quantitative
study focuses not only on traditional performance metrics and on
state-of-the-art elasticity metrics, but also on time- and memory-related
autoscaling-complexity metrics. Our main results give strong and quantitative
evidence about previously unreported operational behavior, for example, that
autoscaling policies perform differently across application domains and by how
much they differ.Comment: Technical Report for the CCGrid 2018 submission "A Trace-Based
Performance Study of Autoscaling Workloads of Workflows in Datacenters
A Deep Dive into the Google Cluster Workload Traces: Analyzing the Application Failure Characteristics and User Behaviors
Large-scale cloud data centers have gained popularity due to their high
availability, rapid elasticity, scalability, and low cost. However, current
data centers continue to have high failure rates due to the lack of proper
resource utilization and early failure detection. To maximize resource
efficiency and reduce failure rates in large-scale cloud data centers, it is
crucial to understand the workload and failure characteristics. In this paper,
we perform a deep analysis of the 2019 Google Cluster Trace Dataset, which
contains 2.4TiB of workload traces from eight different clusters around the
world. We explore the characteristics of failed and killed jobs in Google's
production cloud and attempt to correlate them with key attributes such as
resource usage, job priority, scheduling class, job duration, and the number of
task resubmissions. Our analysis reveals several important characteristics of
failed jobs that contribute to job failure and hence, could be used for
developing an early failure prediction system. Also, we present a novel usage
analysis to identify heterogeneity in jobs and tasks submitted by users. We are
able to identify specific users who control more than half of all collection
events on a single cluster. We contend that these characteristics could be
useful in developing an early job failure prediction system that could be
utilized for dynamic rescheduling of the job scheduler and thus improving
resource utilization in large-scale cloud data centers while reducing failure
rates
Uncertainty-Aware Workload Prediction in Cloud Computing
Predicting future resource demand in Cloud Computing is essential for
managing Cloud data centres and guaranteeing customers a minimum Quality of
Service (QoS) level. Modelling the uncertainty of future demand improves the
quality of the prediction and reduces the waste due to overallocation. In this
paper, we propose univariate and bivariate Bayesian deep learning models to
predict the distribution of future resource demand and its uncertainty. We
design different training scenarios to train these models, where each procedure
is a different combination of pretraining and fine-tuning steps on multiple
datasets configurations. We also compare the bivariate model to its univariate
counterpart training with one or more datasets to investigate how different
components affect the accuracy of the prediction and impact the QoS. Finally,
we investigate whether our models have transfer learning capabilities.
Extensive experiments show that pretraining with multiple datasets boosts
performances while fine-tuning does not. Our models generalise well on related
but unseen time series, proving transfer learning capabilities. Runtime
performance analysis shows that the models are deployable in real-world
applications. For this study, we preprocessed twelve datasets from real-world
traces in a consistent and detailed way and made them available to facilitate
the research in this field
An Efficient Online Prediction of Host Workloads Using Pruned GRU Neural Nets
Host load prediction is essential for dynamic resource scaling and job
scheduling in a cloud computing environment. In this context, workload
prediction is challenging because of several issues. First, it must be accurate
to enable precise scheduling decisions. Second, it must be fast to schedule at
the right time. Third, a model must be able to account for new patterns of
workloads so it can perform well on the latest and old patterns. Not being able
to make an accurate and fast prediction or the inability to predict new usage
patterns can result in severe outcomes such as service level agreement (SLA)
misses. Our research trains a fast model with the ability of online adaptation
based on the gated recurrent unit (GRU) to mitigate the mentioned issues. We
use a multivariate approach using several features, such as memory usage, CPU
usage, disk I/O usage, and disk space, to perform the predictions accurately.
Moreover, we predict multiple steps ahead, which is essential for making
scheduling decisions in advance. Furthermore, we use two pruning methods: L1
norm and random, to produce a sparse model for faster forecasts. Finally,
online learning is used to create a model that can adapt over time to new
workload patterns
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