518 research outputs found
Monitoring and analysis system for performance troubleshooting in data centers
It was not long ago. On Christmas Eve 2012, a war of troubleshooting began in Amazon data centers. It started at 12:24 PM, with an mistaken deletion of the state data of Amazon Elastic Load Balancing Service (ELB for short), which was
not realized at that time. The mistake first led to a local issue that a small number of ELB service APIs were affected. In about six minutes, it evolved into a critical one that EC2 customers were significantly affected. One example was that Netflix, which was using hundreds of Amazon ELB services, was experiencing an extensive streaming service outage when many customers could not watch TV shows or movies on Christmas Eve. It took Amazon engineers 5 hours 42 minutes to find the root cause, the mistaken deletion, and another 15 hours and 32 minutes to fully recover the ELB service. The war ended at 8:15 AM the next day and brought the performance
troubleshooting in data centers to world’s attention. As shown in this Amazon ELB case.Troubleshooting runtime performance issues is crucial in time-sensitive multi-tier cloud services because of their stringent end-to-end timing requirements, but it is also notoriously difficult and time consuming.
To address the troubleshooting challenge, this dissertation proposes VScope, a flexible monitoring and analysis system for online troubleshooting in data centers.
VScope provides primitive operations which data center operators can use to troubleshoot various performance issues. Each operation is essentially a series of monitoring and analysis functions executed on an overlay network. We design a novel
software architecture for VScope so that the overlay networks can be generated, executed and terminated automatically, on-demand. From the troubleshooting side, we design novel anomaly detection algorithms and implement them in VScope. By
running anomaly detection algorithms in VScope, data center operators are notified when performance anomalies happen. We also design a graph-based guidance approach, called VFocus, which tracks the interactions among hardware and software components in data centers. VFocus provides primitive operations by which operators can analyze the interactions to find out which components are relevant to the
performance issue.
VScope’s capabilities and performance are evaluated on a testbed with over 1000 virtual machines (VMs). Experimental results show that the VScope runtime negligibly perturbs system and application performance, and requires mere seconds to deploy monitoring and analytics functions on over 1000 nodes. This demonstrates VScope’s ability to support fast operation and online queries against a comprehensive set of application to system/platform level metrics, and a variety of representative analytics functions. When supporting algorithms with high computation complexity, VScope serves as a ‘thin layer’ that occupies no more than 5% of their total latency. Further, by using VFocus, VScope can locate problematic VMs that cannot be found
via solely application-level monitoring, and in one of the use cases explored in the dissertation, it operates with levels of perturbation of over 400% less than what is seen for brute-force and most sampling-based approaches. We also validate VFocus
with real-world data center traces. The experimental results show that VFocus has troubleshooting accuracy of 83% on average.Ph.D
Data-Driven Methods for Data Center Operations Support
During the last decade, cloud technologies have been evolving at
an impressive pace, such that we are now living in a cloud-native
era where developers can leverage on an unprecedented landscape
of (possibly managed) services for orchestration, compute, storage,
load-balancing, monitoring, etc. The possibility to have on-demand
access to a diverse set of configurable virtualized resources allows
for building more elastic, flexible and highly-resilient distributed
applications. Behind the scenes, cloud providers sustain the heavy
burden of maintaining the underlying infrastructures, consisting in
large-scale distributed systems, partitioned and replicated among
many geographically dislocated data centers to guarantee scalability,
robustness to failures, high availability and low latency. The larger the
scale, the more cloud providers have to deal with complex interactions
among the various components, such that monitoring, diagnosing and
troubleshooting issues become incredibly daunting tasks.
To keep up with these challenges, development and operations
practices have undergone significant transformations, especially in
terms of improving the automations that make releasing new software,
and responding to unforeseen issues, faster and sustainable at scale.
The resulting paradigm is nowadays referred to as DevOps. However,
while such automations can be very sophisticated, traditional DevOps
practices fundamentally rely on reactive mechanisms, that typically
require careful manual tuning and supervision from human experts.
To minimize the risk of outages—and the related costs—it is crucial to
provide DevOps teams with suitable tools that can enable a proactive
approach to data center operations.
This work presents a comprehensive data-driven framework to address
the most relevant problems that can be experienced in large-scale
distributed cloud infrastructures. These environments are indeed characterized
by a very large availability of diverse data, collected at each
level of the stack, such as: time-series (e.g., physical host measurements,
virtual machine or container metrics, networking components
logs, application KPIs); graphs (e.g., network topologies, fault graphs
reporting dependencies among hardware and software components,
performance issues propagation networks); and text (e.g., source code,
system logs, version control system history, code review feedbacks).
Such data are also typically updated with relatively high frequency,
and subject to distribution drifts caused by continuous configuration
changes to the underlying infrastructure. In such a highly dynamic scenario,
traditional model-driven approaches alone may be inadequate
at capturing the complexity of the interactions among system components. DevOps teams would certainly benefit from having robust
data-driven methods to support their decisions based on historical
information. For instance, effective anomaly detection capabilities may
also help in conducting more precise and efficient root-cause analysis.
Also, leveraging on accurate forecasting and intelligent control
strategies would improve resource management.
Given their ability to deal with high-dimensional, complex data,
Deep Learning-based methods are the most straightforward option for
the realization of the aforementioned support tools. On the other hand,
because of their complexity, this kind of models often requires huge
processing power, and suitable hardware, to be operated effectively
at scale. These aspects must be carefully addressed when applying
such methods in the context of data center operations. Automated
operations approaches must be dependable and cost-efficient, not to
degrade the services they are built to improve.
i
Cyber Security
This open access book constitutes the refereed proceedings of the 17th International Annual Conference on Cyber Security, CNCERT 2021, held in Beijing, China, in AJuly 2021. The 14 papers presented were carefully reviewed and selected from 51 submissions. The papers are organized according to the following topical sections: ​data security; privacy protection; anomaly detection; traffic analysis; social network security; vulnerability detection; text classification
Warwick electron microscopy Datasets
Large, carefully partitioned datasets are essential to train neural networks and standardize performance benchmarks. As a result, we have set up new repositories to make our electron microscopy datasets available to the wider community. There are three main datasets containing 19769 scanning transmission electron micrographs, 17266 transmission electron micrographs, and 98340 simulated exit wavefunctions, and multiple variants of each dataset for different applications. To visualize image datasets, we trained variational autoencoders to encode data as 64-dimensional multivariate normal distributions, which we cluster in two dimensions by t-distributed stochastic neighbor embedding. In addition, we have improved dataset visualization with variational autoencoders by introducing encoding normalization and regularization, adding an image gradient loss, and extending t-distributed stochastic neighbor embedding to account for encoded standard deviations. Our datasets, source code, pretrained models, and interactive visualizations are openly available at https://github.com/Jeffrey-Ede/datasets
Landslide Surface Displacement Prediction Based on VSXC-LSTM Algorithm
Landslide is a natural disaster that can easily threaten local ecology,
people's lives and property. In this paper, we conduct modelling research on
real unidirectional surface displacement data of recent landslides in the
research area and propose a time series prediction framework named
VMD-SegSigmoid-XGBoost-ClusterLSTM (VSXC-LSTM) based on variational mode
decomposition, which can predict the landslide surface displacement more
accurately. The model performs well on the test set. Except for the random item
subsequence that is hard to fit, the root mean square error (RMSE) and the mean
absolute percentage error (MAPE) of the trend item subsequence and the periodic
item subsequence are both less than 0.1, and the RMSE is as low as 0.006 for
the periodic item prediction module based on XGBoost\footnote{Accepted in
ICANN2023}
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