The document proposes a failure prediction system for cloud resources that uses hierarchical temporal memory (HTM) for anomaly detection. It evaluates the system's ability to accurately predict failures (effectiveness) and how early it can predict them (timeliness). The system detects anomalies in key performance indicators using HTM and local predictors. A global predictor then analyzes local predictions and alerts for failures. The study tests the system under different workloads and fault injections to answer its research questions about prediction effectiveness and timeliness.
Performance and Success: Key Elements to Consider in the CloudRightScale
Some view the cloud as a silver bullet to solve performance issues. If only it were that simple. The cloud provides a fantastic way to scale hardware on demand, but performance must be optimized at the application level to realize maximum gains. Apica COO, Craig Irwin, will present key strategic elements employed by today's progressive and innovative companies and actionable insights on how they are leveraging technology to proactively identify bottlenecks, improve performance, and optimize their environment. Learn from high profile crashes, common mistakes enterprises make, and how not to become another headline.
Computational Patterns of the Cloud - QCon NYC 2014Ines Sombra
The Cloud has undoubtedly changed the way we think about computing, IT operations, innovation, and entrepreneurship. But what are the computational patterns that have emerged from the pervasiveness of public clouds? What can we leverage to improve our organizations? And what are the challenges that we face going forward?
In this talk, I will introduce you to cloud computing’s paradigms and discuss their applications with practical examples from Engine Yard’s customers, peers, and partners. We will also cover antipatterns and myths. If you are curious about Cloud computing or want to improve your cloud strategy this talk is for you.
NOTE: Open an issue if you want me to explain something in more detail at the accompanying github repo: https://github.com/Randommood/QConNYC2014/
Dependable Operation - Performance Management and Capacity Planning Under Con...Liming Zhu
Talk at http://www.cmga.org.au/ Meet up
Modern large-scale applications experience sporadic changes due to operational activities such as upgrade, redeployment, on-demand scaling and interferences from other simultaneous operations. This poses new challenges in system monitoring, capacity planning, performance management, error detection and diagnosis. For example, the traditional anomaly-detection-based techniques are less effective during the “sporadic” operation period as a wide range of legitimate changes confound the situation and make performance baseline establishment for “normal” operation difficult. The increasing frequency of these sporadic operations (e.g. due to continuous deployment) is exacerbating the problem. In this talk, we will introduce a number of ongoing research activities at NICTA addressing these issues. For example, we propose the Process Oriented Dependability (POD) approach, an approach that explicitly models these sporadic operations as processes and uses the process context to filter logs, traverse fault trees and conduct adaptive monitoring.
Apica - Performance Does Matter: Five Key Elements to Consider in the CloudRightScale
RightScale Conference Santa Clara 2011: We’ve all heard the stories of sites crashing and performing poorly, from major retailers – to iconic technology brands – to multinational airlines. lt’s only a matter of time before another story hits the headlines. Apica CEO, Sven Hammar, will review the importance of employing a strategic load testing and performance monitoring strategy to ensure that your web application doesn’t become another statistic. While outlining the actionable benefits of performance testing and analysis, Sven will touch on the common mistakes, discuss recent outages that hit the headlines, and share best practices to maintain optimal web performance and avoid system crashes.
Performance and Success: Key Elements to Consider in the CloudRightScale
Some view the cloud as a silver bullet to solve performance issues. If only it were that simple. The cloud provides a fantastic way to scale hardware on demand, but performance must be optimized at the application level to realize maximum gains. Apica COO, Craig Irwin, will present key strategic elements employed by today's progressive and innovative companies and actionable insights on how they are leveraging technology to proactively identify bottlenecks, improve performance, and optimize their environment. Learn from high profile crashes, common mistakes enterprises make, and how not to become another headline.
Computational Patterns of the Cloud - QCon NYC 2014Ines Sombra
The Cloud has undoubtedly changed the way we think about computing, IT operations, innovation, and entrepreneurship. But what are the computational patterns that have emerged from the pervasiveness of public clouds? What can we leverage to improve our organizations? And what are the challenges that we face going forward?
In this talk, I will introduce you to cloud computing’s paradigms and discuss their applications with practical examples from Engine Yard’s customers, peers, and partners. We will also cover antipatterns and myths. If you are curious about Cloud computing or want to improve your cloud strategy this talk is for you.
NOTE: Open an issue if you want me to explain something in more detail at the accompanying github repo: https://github.com/Randommood/QConNYC2014/
Dependable Operation - Performance Management and Capacity Planning Under Con...Liming Zhu
Talk at http://www.cmga.org.au/ Meet up
Modern large-scale applications experience sporadic changes due to operational activities such as upgrade, redeployment, on-demand scaling and interferences from other simultaneous operations. This poses new challenges in system monitoring, capacity planning, performance management, error detection and diagnosis. For example, the traditional anomaly-detection-based techniques are less effective during the “sporadic” operation period as a wide range of legitimate changes confound the situation and make performance baseline establishment for “normal” operation difficult. The increasing frequency of these sporadic operations (e.g. due to continuous deployment) is exacerbating the problem. In this talk, we will introduce a number of ongoing research activities at NICTA addressing these issues. For example, we propose the Process Oriented Dependability (POD) approach, an approach that explicitly models these sporadic operations as processes and uses the process context to filter logs, traverse fault trees and conduct adaptive monitoring.
Apica - Performance Does Matter: Five Key Elements to Consider in the CloudRightScale
RightScale Conference Santa Clara 2011: We’ve all heard the stories of sites crashing and performing poorly, from major retailers – to iconic technology brands – to multinational airlines. lt’s only a matter of time before another story hits the headlines. Apica CEO, Sven Hammar, will review the importance of employing a strategic load testing and performance monitoring strategy to ensure that your web application doesn’t become another statistic. While outlining the actionable benefits of performance testing and analysis, Sven will touch on the common mistakes, discuss recent outages that hit the headlines, and share best practices to maintain optimal web performance and avoid system crashes.
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Making Observability Actionable At Scale - DBS DevConnect 2019Squadcast Inc
Many organisations already possess a vast amount of existing data about production systems. As customer expectations evolve, organisations are often challenged to find more proactive ways of dealing with traditionally reactive incident response activity. In this talk, we discuss approaches to unlock value from this data by making it truly actionable. Understanding production failure modes better, enriching technical and business context effectively, decomposing response activity into shared primitives, actions and workflows, and overall, sharing and augmenting this active knowledge repository on a continuous basis are key takeaways. Through case studies, we'll discuss how we can accomplish this by engineering your observability processes and tooling to work for human-in-the-loop interpretation and response rather than a purely human-reliant strategy.
Hope Is Not A Strategy: Automating Efficient Resource Utilization for SREsStormForge .io
On-Demand Video Available Here: https://www.stormforge.io/event/hope-not-strategy-automating-sres/
As a Site Reliability Engineer, you understand the power of software and automation for solving complex problems. So why are your cloud-native applications still running at suboptimal efficiency, despite all your efforts?
The fact is, ensuring peak efficiency of apps running on Kubernetes is a really challenging problem to solve. The complexity of Kubernetes and containers makes it impossible for a human to effectively configure apps for deployment in a way that provides the best trade-off between cost, performance, and reliability. So, you’re left with a time-consuming, and ultimately ineffective, trial-and-error approach.
In this webinar, we’ll show how to apply SRE principles to the optimization of cloud-native apps running on Kubernetes to ensure peak efficiency.
This Webinar is Best for:
• SREs who want to automate the efficiency of their cloud-native applications.
• Anyone looking to apply SRE principles and practices to ensure performance, reliability, and cost-efficiency of their apps running on Kubernetes.
What You'll Learn:
• How machine learning can help to automate yourself out of the painful process of manual, trial-and-error application tuning.
• How to empower your dev team to proactively ensure application performance, reliability, and cost-efficiency.
• How to build continuous optimization into your automated release process to save time and ensure every release performs at peak efficiency.
Next generation alerting and fault detection, SRECon Europe 2016Dieter Plaetinck
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https://www.usenix.org/conference/srecon16europe/program/presentation/plaetinck
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This is truer as workloads move into the cloud and leverage ephemeral technologies, such as microservices and containers. To reach observability, IT and DevOps teams need to correlate different sources from logs, metrics, traces, events and more. This becomes even more challenging when defining the online revenue impact of a failed container—after all, this is what really matters to the business.
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Why it is a bigger challenge in a multicloud and containerized world
How observability results in less firefighting and more fire prevention
How new platforms can help gain observability (on premises and in the cloud) for containers, microservices and even SAP or mainframes
Gartner analyzed data centers for a period of 10 years and found that 47% of all problems were caused by cloud services outages. The duration of outages ranged between 40 minutes and five days. Ponemon Institute studied the financial impact and found that on average outages cost US$ 690.204, with an average downtime cost of US$ 6.828 per minute. These results are important due to the economic impact of unplanned outages on cloud operations which calls for higher platform reliability.
The first part of this talk will present the mechanisms that pioneers, such as Amazon, Google, and Netflix, have already developed to increase the reliability of their cloud platforms. The second part of the talk will describe how Huawei Research is exploring the use of fault-injection mechanisms to effectively increase the reliability of the Open Telekom Cloud platform from Deutsche Telekom.
As the products and organizations grow in terms of scale, developing the applications while keeping performance an integral part of the build process is important. The presentation covers about what is performance aware development, why it is the need of the hour and how we are doing it inside LinkedIn where we run hundreds of services having multiple deployments everyday while making sure the performance of the services is kept in check and probability of introducing performance regressions is kept to a minimum.
What does performance mean in the cloudMichael Kopp
Performance problems are one of the most cited concerns about to the cloud. But is it really the cloud or the application? What does performance mean anyway when you can scale to thousands of servers? This session will discuss why traditional means of performance management and troubleshooting no longer work and how this affects everything. Most importantly we will look at how to identify the root cause of performance problems in such dynamic environments. Finally we will explain how to assess and manage performance when capacity is no longer the issue.
What kind of design patterns are useful for applications adopting the cloud? How can apps achieve the scalability and availability promised by the cloud? Presentation from Interop 2011 Enterprise Cloud Summit.
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
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Many organisations already possess a vast amount of existing data about production systems. As customer expectations evolve, organisations are often challenged to find more proactive ways of dealing with traditionally reactive incident response activity. In this talk, we discuss approaches to unlock value from this data by making it truly actionable. Understanding production failure modes better, enriching technical and business context effectively, decomposing response activity into shared primitives, actions and workflows, and overall, sharing and augmenting this active knowledge repository on a continuous basis are key takeaways. Through case studies, we'll discuss how we can accomplish this by engineering your observability processes and tooling to work for human-in-the-loop interpretation and response rather than a purely human-reliant strategy.
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• Anyone looking to apply SRE principles and practices to ensure performance, reliability, and cost-efficiency of their apps running on Kubernetes.
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Next generation alerting and fault detection, SRECon Europe 2016Dieter Plaetinck
There is a common belief that in order to solve more [advanced] alerting cases and get more complete coverage, we need complex, often math-heavy solutions based on machine learning or stream processing.
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We then explore how we can get dramatically better alerting, as well as make our lives a lot easier by optimizing workflow and machine-human interaction through an alerting IDE (exemplified by bosun), basic logic, basic math and metric metadata, even for solving complicated alerting problems such as detecting faults in seasonal timeseries data.
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For some, observability is just a hollow rebranding of monitoring, for others it’s monitoring on steroids. But what if we told you observability is the new way to find out why—not just if—your distributed system or application isn’t working as expected? Today, we see that traditional monitoring approaches can fall short if a system or application doesn’t adequately externalize its state.
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The differences between observability and monitoring
Why it is a bigger challenge in a multicloud and containerized world
How observability results in less firefighting and more fire prevention
How new platforms can help gain observability (on premises and in the cloud) for containers, microservices and even SAP or mainframes
Gartner analyzed data centers for a period of 10 years and found that 47% of all problems were caused by cloud services outages. The duration of outages ranged between 40 minutes and five days. Ponemon Institute studied the financial impact and found that on average outages cost US$ 690.204, with an average downtime cost of US$ 6.828 per minute. These results are important due to the economic impact of unplanned outages on cloud operations which calls for higher platform reliability.
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What does performance mean in the cloudMichael Kopp
Performance problems are one of the most cited concerns about to the cloud. But is it really the cloud or the application? What does performance mean anyway when you can scale to thousands of servers? This session will discuss why traditional means of performance management and troubleshooting no longer work and how this affects everything. Most importantly we will look at how to identify the root cause of performance problems in such dynamic environments. Finally we will explain how to assess and manage performance when capacity is no longer the issue.
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Cloud Failure Prediction with Hierarchical Temporal Memory An Empirical Assessment
1. joint work with
Alessandro Tundo Leonardo Mariani
Paolo Saltarel
Cloud Failure Prediction with Hierarchical
Temporal Memory
An Empirical Assessment
Oliviero Riganelli
University of Milano - Bicocca
Marco Mobilio
4. Runtime Failures are unavoidable
… and expensive
$1.25 billion to $2.5 billion
cost of unplanned downtime per year [Fortune]
$
Lost revenue Lost productivity Lost brand equity or trust
The top three costs organizations face due to downtime
[Forrester Consulting]
8. Online Anomaly-based Failure Prediction in the Cloud
Cloud
Resource
Cloud
Resource
Anomaly
Detector
Anomaly
Detector
Anomaly
Detector
Anomaly
Detector
KPI values
KPI values
KPI values
KPI values
…
…
Local
Failure
Predictor
anomalies
anomalies
Global
Failure
Predictor
local failure prediction
Local
Failure
Predictor
anomalies
anomalies
local failure prediction
failure alert
…
9. local failure prediction
Online Anomaly-based Failure Prediction in the Cloud
Cloud
Resource
Anomaly
Detector
Anomaly
Detector
… Local
Failure
Predictor
anomalies
anomalies
Global
Failure
Predictor
local failure prediction
failure alert
…
S. Ahmad, A. Lavina, S. Purdy, and Z. Aghaab, “Unsupervised real-time anomaly detection for streaming data”, Neurocomputing 2017
KPI values
KPI values
HTM
xt
a(xt)
π(xt)
St Lt
Anomaly Detection with Hierarchical Temporal Memory (HTM)
an anomaly is reported if Lt >= 1-ε
Prediction
Error
Anomaly
likelihood
10. Anomaly
Detector
…
local failure prediction
Local
Failure
Predictor
Online Anomaly-based Failure Prediction in the Cloud
Cloud
Resource
Anomaly
Detector
Anomaly
Detector
KPI values
KPI values
KPI values
…
…
Local
Failure
Predictor
anomalies
anomalies
Global
Failure
Predictor
local failure prediction
failure alert
normal executions
failure prone executions
Class boundary
(Separating hyperplane)
Local Failure Predictor with one-class SVM
a failure is reported after n consecutive failure predictions
11. Online Anomaly-based Failure Prediction in the Cloud
Cloud
Resource
Cloud
Resource
Anomaly
Detector
Anomaly
Detector
Anomaly
Detector
Anomaly
Detector
KPI values
KPI values
KPI values
KPI values
…
…
Local
Failure
Predictor
anomalies
anomalies
Global
Failure
Predictor
local failure prediction
Local
Failure
Predictor
anomalies
anomalies
local failure prediction
failure alert
…
Failure
No
Yes
Failure
No
Yes
Failure
No
Yes
Vote-based
Single resource
No Failure
Failure alert
Failure
No
Yes
Failure
No
Yes
Failure
No
Yes
x consecutive failure predictions to
raise an alert
y consecutive failure predictions to
raise an alert
12. Experimental Setting
Testbed
1 Cloud-native IP Multimedia SubSystem
6 VMs with 2 vCPUs, 2GB RAM, 20GB HD
150 KPIs
Workload patterns
Daily variations: higher tra
ffi
c on working days
Hourly variations: heavier during the day with peaks
at 9am and 7pm
Fault Injection
CPU Hog
Memory leak
Packet loss
Excessive workload
Types Activation Patterns
Linear
Exponential
Random
Tested Parameters
Anomaly Detector: ε = 0.8, 0.85, 0.9, 0.95
Local Failure Predictor: n=1,2.
Single Resource Global Failure Predictor: x=1,2,3,4,5,6.
Vote-based Global Failure Predictor: y=1,2,3.
Research Questions
Can an HTM-based anomaly detector support a
failure prediction system in accurately predicting
failures?
How early can failures be predicted?
RQ1
RQ2
Prediction Timeliness
Prediction Effectiveness
