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Alerting [HOT]


The alerting feature notifies you when data from one or more Elasticsearch indices meets certain conditions. For example, you might want to notify a Slack channel if your application logs more than five HTTP 503 errors in one hour, or you might want to page a developer if no new documents have been indexed in the past 20 minutes.




alerting



The purpose of this Department of Homeland Security (DHS) Science and Technology Directorate (S&T) Report on Alerting Tactics (Report) is to provide recommendations on effective combinations of alerting tactics for various incident types based upon lessons learned from practitioners.


This document illustrates using the Cloud Monitoring API to create, edit, delete, list,and get metric-based alerting policies programmatically. The examples showhow to use of the Google Cloud CLI and how to use client libraries.This content does not apply to log-based alerting policies. For information about log-based alerting policies, see Monitoring your logs.


An alerting policy is represented by an AlertPolicy object,which describes a set of conditions indicating a potentiallyunhealthy status in your system. Alerting policies referencenotification channels, which let you specify how you want to be informedthat an alerting policy has been triggered.


Alerting policies can be expressed in JSON or YAML, which lets you recordpolicies in files, and use files to back up and restore policies.With Google Cloud CLI, you can create policies from filesin either format. With the REST API, you can create policies from JSON files.See Sample policies for a selection of alerting policiesin JSON format.


The following examples use the gcloud interface and the APIto illustrate these basic use cases. The API samples are excerptedfrom a sample program that uses the API to implement a backup and restoresystem for alerting policies. Fuller samples are shown in Example: backupand restore.


To get the permissions that you need to create and modify alerting policies by using the Cloud Monitoring API, ask your administrator to grant you the Monitoring AlertPolicy Editor (roles/monitoring.alertPolicyEditor) IAM role on your project. For more information about granting roles, see Manage access.


Design your application to single-thread Cloud Monitoring API calls thatmodify the state of an alerting policy in aGoogle Cloud project. For example, single-thread API calls that create, update,or delete an alerting policy.


To create an alerting policy in a project, use thealertPolicies.create method. For information about how to invoke thismethod, its parameters, and the response data, see the reference pagealertPolicies.create.


The following samples illustrate the creation of alerting policies, but theydon't describe how to create a JSON or YAML file that describesan alerting policy. Instead, the samples assume that a JSON-formatted fileexists and they illustrate how to issue the API call. For example JSON files,see Sample policies. For general information aboutmonitoring ratios of metrics, see Ratios of metrics.


To create an alerting policy in a project, use the gcloud alpha monitoringpolicies create command. The following example creates an alerting policy ina-gcp-project from the rising-cpu-usage.json file:


You can also update the notification channels referenced by an alerting policy.Alerting policies refer to notification channels by name. The channelsmust exist before they can be used in an alerting policy.


To modify the notification channels in an alerting policy, use thegcloud alpha monitoring policies update command. There are severalflags related to notification channels, letting you remove notificationchannels, replace notification channels, and add new notification channels.


To display a summary of a single-condition alerting policy on custom dashboard,add an AlertChart widget to the dashboard.You use thedashboards.create method for a new dashboard and thedashboards.patch method for an existing dashboard.


All of the API examples shown are pulled from a larger application that canback up the alerting policies in a project to a file and can restore thepolicies, possibly to another project. If the projects used for backup andrestore are different, the application effectively exports and imports policiesfrom one project to another.


The restoration process is more complex than the original back-up. You canrestore to the project you originally backed up. You can also restore to adifferent project, effectively providing import of alerting policies.


Additionally, when a notification channel is recreated in a different project,it gets a different name, so the restore process has to map the names ofchannels in backed-up alerting policies to their new names, and replace theold names with the new ones.


In Google Cloud CLI, the command group for managing alerting policies andnotification channels is monitoring, which is in alpha release.The monitoring group is available in the alpha component.That is, these commands will all begin with:


Light exerts powerful non-visual effects on a wide range of biological functions and behavior. In humans, light is intuitively linked with an alert or wakeful state. Compared to the effects of light on human circadian rhythms, little attention has been paid to its acute alerting action. Here I summarize studies from the past two decades, which have defined and quantified the dose (illuminance levels), exposure duration, timing and wavelength of light needed to evoke alerting responses in humans, as well as their temporal relationship to light-induced changes in endocrinological and electrophysiological sequelae of alertness. Furthermore, neuroanatomical and neurophysiological findings from animal studies elucidating a potential role of light in the regulation of sleep/wake states are discussed. A brief outlook of promising clinical and non-clinical applications of lights' alerting properties will be given, and its involvement in the design of more effective lighting at home and in the workplace will be considered.


IT alerting automates and streamlines the way IT communicates during major IT incidents to resolve issues faster and minimize their impact on the business. It provides consistent messages to the right IT experts and keeps all stakeholders and impacted customers informed on resolution progress.


New monitoring and alerting capabilities for Microsoft Teams are available in the Teams admin center. Use different sets of rules available under the Notifications & alerts section in the Teams admin center to monitor Teams capabilities and receive alerts. For example, you can actively monitor the health of Teams devices such as IP Phones, Teams Rooms on Android, and others if they unexpectedly go offline.


You must be a global admin in Microsoft 365 or a Teams service admin to configure alerting rules. See Use Teams administrator roles to manage Teams to learn more about Teams admin roles and which reports each admin role can access.


Use of the Integrated Public Alert and Warning System (IPAWS) has increased since its launch in 2012. IPAWS enables authorized federal, state, territorial, tribal, and local alerting authorities to send a Wireless Emergency Alert (WEA) to mobile devices, such as cell phones and an Emergency Alert System (EAS) alert to media platforms, such as radios and television. The Federal Emergency Management Agency (FEMA) operates IPAWS and the Federal Communications Commission (FCC) establishes rules for telecommunications providers to deliver WEA and EAS alerts. A public safety agency must submit an application and receive approval from FEMA to become an IPAWS alerting authority. In September 2019, more than 1,400 alerting authorities had access to IPAWS, up from fewer than 100 authorities in 2013. All states have at least one state alerting authority, but gaps in local authority access remain (see figure) that could limit the timeliness of alerts as emergencies occur at the local level. GAO found 430 pending IPAWS applications as of September 2019, some of which dated back to 2012. FEMA has not established procedures to prioritize and follow up with applicants and FEMA officials acknowledged that doing so would be beneficial.


FEMA and FCC have taken steps to modernize IPAWS and improve alerting. For example, FEMA has made system upgrades and FCC has made various WEA improvements, such as requiring wireless phone carriers to provide more precise geographic targeting of alerts. Prior to these improvements, officials from many alerting authorities said the inability to geographically target alerts with accuracy made the officials reluctant to send WEA messages. FCC intends to partner with certain localities to test geographic targeting and, according to FCC officials, plans to use other tests to learn about how the improvements perform during emergencies. However, FCC has not developed goals and performance measures for these efforts. Doing so would help FCC more clearly assess whether the WEA improvements are working as intended. Furthermore, having specific performance information could increase alerting authorities' confidence in and use of IPAWS.


GAO analyzed relevant data and documentation and assessed FCC's efforts against leading government performance management practices and FEMA and FCC's efforts against internal control standards. GAO interviewed federal officials involved in emergency alerting. GAO also interviewed a non-generalizable selection of IPAWS alerting authorities and applicants, local governments, public safety and industry associations, and communications companies. GAO selected alerting authorities that experienced different types of disasters and threats to public safety from 2017 to 2019.


Early detection of infectious diseases is crucial for reducing transmission and facilitating early intervention. In this study, we built a real-time smartwatch-based alerting system that detects aberrant physiological and activity signals (heart rates and steps) associated with the onset of early infection and implemented this system in a prospective study. In a cohort of 3,318 participants, of whom 84 were infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), this system generated alerts for pre-symptomatic and asymptomatic SARS-CoV-2 infection in 67 (80%) of the infected individuals. Pre-symptomatic signals were observed at a median of 3 days before symptom onset. Examination of detailed survey responses provided by the participants revealed that other respiratory infections as well as events not associated with infection, such as stress, alcohol consumption and travel, could also trigger alerts, albeit at a much lower mean frequency (1.15 alert days per person compared to 3.42 alert days per person for coronavirus disease 2019 cases). Thus, analysis of smartwatch signals by an online detection algorithm provides advance warning of SARS-CoV-2 infection in a high percentage of cases. This study shows that a real-time alerting system can be used for early detection of infection and other stressors and employed on an open-source platform that is scalable to millions of users. 041b061a72


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