Building a strong brand in today’s hyper-competitive business environment takes vision. It also requires a flexible, easily managed approach to digital asset management (DAM), so marketing professionals and other stakeholders can easily share, store, track, and manipulate assets to build the brand.

Many of today’s leading companies, including JetBlue, Slack, TripAdvisor, Lyft, and HealthONE, rely on Brandfolder to deliver consistent, organized, and efficient brand experiences. Brandfolder provides an easy-to-use platform that can scale across an entire company with little end-user training, empowering customers to distribute digital assets wherever they are needed. Customers also gain much greater insight into how those assets are used, and how to use them more effectively in marketing campaigns and brand messaging.

“Google Cloud made it easy to build an ML platform to quickly iterate through different brand intelligence use cases and release data-driven product features into the Brandfolder platform.”

—Ajay Rajasekharan, Head of Data Science, Brandfolder

Brandfolder is constantly advancing its development efforts to introduce new data-driven features without complicating the user experience. Big data, artificial intelligence (AI), and machine learning (ML) are key to meeting customers’ unique business needs, and essential for Brandfolder to compete in the fast-moving DAM industry. To enhance these capabilities, Brandfolder sought a public cloud provider that could help it scale its data pipeline cost effectively while providing access to advanced AI technologies.

After graduating from the Techstars startup accelerator program in 2013, Brandfolder tried two other cloud providers before standardizing on Google Cloud Platform (GCP).

“We saw a difference with Google Cloud from the very beginning because the interactions felt like a strategic relationship,” says Jim Hanifen, Head of Product at Brandfolder. “Google gave us startup credits and a lot of face-to-face support, which we hadn’t experienced with other cloud providers. We decided to move our entire infrastructure to Google Cloud Platform.”

Building an ML platform for brand intelligence

After performing an initial lift-and-shift migration of virtual machines (VMs) onto Compute Engine, Brandfolder built an ML platform using GCP managed services to seamlessly deliver its data products. The platform leverages Cloud SQL, Cloud Storage as the data lake, Cloud Dataproc for cloud-native Apache Spark computing clusters, Cloud Composer as the batch job scheduler, Cloud Pub/Sub as the backbone data pipeline, Container Registry to store Docker images, and Google Kubernetes Engine (GKE) as the application orchestrator. Cloud Dataflow brings data into the data lake and into BigQuery for analysis.

“Google Cloud made it easy to build an ML platform to quickly iterate through different brand intelligence use cases and release data-driven product features into the Brandfolder platform,” says Ajay Rajasekharan, Head of Data Science at Brandfolder, who describes the architecture in a detailed blog. “We simply ingest raw application and event data on one end and output an ML service on the other.”

“Moving to Google Cloud Platform allows us to complete more sophisticated data analysis and ML models much faster, and at a much lower cost. We can create brand-specific ML models 12x faster and get them into production quickly to address our customers’ unique business needs.”

—Brett Nekolny, Head of Engineering, Brandfolder

For many general use cases, Brandfolder does not need to build custom ML models, and instead relies on pre-trained API models from GCP. For example, it uses Vision API and Video Intelligence API to auto-tag creative assets on import to enable fast, intuitive searches across images and videos. When more product- and brand-specific modeling is required to address unique customer use cases, Brandfolder builds and trains custom ML models using its GCP pipeline or Cloud AutoML, a suite of products built on Google transfer learning and neural architecture search technology. For example, if a Brandfolder customer makes different types of grills, Brandfolder can use AutoML Vision to train a model to recognize the different grills.

“Moving to Google Cloud Platform allows us to complete more sophisticated data analysis and ML models much faster, and at a much lower cost,” explains Brett Nekolny, Head of Engineering at Brandfolder. “We can create brand-specific ML models 12x faster and get them into production quickly to address our customers’ unique business needs.”

Industry-leading security and performance

Google Cloud’s security model helps Brandfolder give existing and prospective customers peace of mind that their data will be protected. Cloud Identity & Access Management (Cloud IAM) provides enterprise-grade access control, while Cloud Identity-Aware Proxy (Cloud IAP) enables remote users to work more securely without the hassles of a VPN client. GCP also isolates cloud resources into projects, making it easy to assign permissions and keep data and VMs organized and segregated.

“With Google Cloud, everything begins and ends with security, which makes things very easy for us,” says Jim. “If we’re under a security review, we can submit a Google security white paper. If a potential customer has security concerns, we tell them we are hosted on GCP, and those concerns go away.”

To give customers even better application performance for accessing their brand assets, Brandfolder uses Cloud Memorystore, an in-memory data store service for Redis, to cache data and provide sub-millisecond data access for production applications.

“It was much easier for us to use Cloud Memorystore versus running Redis on our compute instances,” says Brett. “The high availability, replication across zones, and automatic failover with no data loss are big for us.”

Global private network interconnects between Google Cloud and the Fastly content delivery network (CDN) dramatically reduce latency, allowing Brandfolder’s customers to deliver and update even very large creative assets quickly around the world.

“What’s beautiful about the relationship between Google and Fastly is that if one of our customers uploads a new version of an asset, we can propagate that out to Fastly, and the new version will automatically show up in all the places where it’s referenced,” says Brett.

“The ability to quickly solve problems with AI has a substantial impact on our revenue, and that’s more apparent every quarter. Few of our competitors are doing product- or brand-specific modeling because it takes a lot of time and resources. We overcame those hurdles with Google Cloud.”

—Jim Hanifen, Head of Product, Brandfolder

Improving employee and customer productivity

Brandfolder also uses Google solutions for real-time collaboration and productivity, using G Suite to connect employees with intuitive, cloud-based apps. Teams use Gmail, Calendar, Docs, Drive, Sheets, Slides, and Hangouts Meet every day to move the business forward. Many of Brandfolder’s customers are also G Suite users, and Brandfolder offers a plug-in that allows them to view their creative assets inside of Docs and pull images in as needed. Customers can also log into Brandfolder with their G Suite credentials, making the solution even easier to use.

“We’ve been using G Suite since the beginning, and it’s helped us collaborate efficiently to build a successful, growing company,” says Jim. “Our teams expect to have that kind of close collaboration, and everyone here enjoys the G Suite experience.”

Driving 99 percent annual business growth

With automated tagging and other innovative AI-based features, Brandfolder is helping customers locate and distribute assets faster. As a result, Brandfolder is building customer loyalty and increasing sales, growing its business by 99 percent year-over-year. Since moving to GCP, Brandfolder has been able to scale its analytics and data pipeline 50x without a corresponding increase in costs and has not had to expand its development team.

“The ability to quickly solve problems with AI has a substantial impact on our revenue, and that’s more apparent every quarter,” says Jim. “Few of our competitors are doing product- or brand-specific modeling because it takes a lot of time and resources. We overcame those hurdles with Google Cloud.”

Since Aristocrat’s founding in 1953, technology has constantly transformed gaming and the digital demands on our gaming business are a far cry from challenges we faced when we started. As we continue to expand globally, security and compliance are top priorities.

Managing IT security for several gaming subsidiaries and our core business became more complex as we entered into new markets and scaled up our number of users. We needed a centralized platform that could give us full visibility into all of our systems and efficient monitoring capabilities to keep data and applications secure. We also needed the ability to secure our systems without compromising user experiences.

We turned to Google Cloud and Splunk to better manage complexity and support highly efficient, secure, and more dynamic gaming experiences for everyone. We are committed to using today’s modern technologies to give players more optimal experiences.

Bringing our digital footprint into the cloud

When we set out on our digital transformation, we looked to address many business requirements.

These requirements included:

• Regulation: We wanted a platform that could efficiently address our industry’s stringent and global regulatory compliance requirements.
• Player experience: Our IT environment must support smooth gaming experiences to keep users engaged and satisfied.
• Scalability: As we grow and diversify, meeting the changing demands of an increasingly global gaming community, we need an easily scalable platform to align with our current and future needs.

Google Cloud offered us the perfect foundation through solutions such as Compute Engine, Google Kubernetes Engine, BigQuery, and Google Cloud Storage. These acted as the right infrastructure components for us for the following reasons:

• Google Cloud is globally accessible and supports compliance, helping to streamline security and regulatory processes for our team.
• With Google Cloud, we can manage our entire development and delivery processes globally with fast and efficient reconciliation of regional compliance requirements.
• When we need to adjust existing infrastructure or deliver new capabilities, Google Cloud accelerates the process and takes the heavy lifting off of our team.
• Google Cloud allows us to support tens of thousands of players on each of our apps while experiencing minimal downtime and low latency. The importance of this support can’t be underestimated in an industry where players have little to no patience if lags in games occur.

We migrated our back-office IT stack alongside our consumer-facing production applications to Google Cloud given our positive experiences with compliance, security, scalability, and process management. This migration has significantly accelerated our digital transformation while streamlining our infrastructure for faster and more cost-effective performance.

In many ways, Google Cloud has been, with maybe a pun intended, a game-changer for us. For instance, when we suddenly had to support a lot of remote work during the COVID-19 pandemic, native identity and access management tools in Google Cloud allowed us to retire costly VPNs used for backend access and quickly adopt a more easily managed, cost-effective zero-trust security posture.

Accessing vital third-party partners and managed services

Aristocrat has many IT needs best addressed in a multi-cloud environment. Google Cloud is particularly attractive given its strong cloud interoperability, as well as the many products and services available on Google Cloud Marketplace. The marketplace accelerated our deployment of key third-party apps including Splunk and Qualys.

Given the personal information we store and the global regulatory compliance statutes we must oblige, security lies at the heart of our business. Splunk is a critical component of our digital transformation because it offers solutions that provide the enhanced monitoring capabilities and visibility we need. The integration between Splunk and Google Cloud gives us confidence that our data is secure. We know our data can be secure in Google Cloud, while simplified billing through Google Cloud Marketplace makes payments and license tracking easier for our procurement team.

As part of our protected environment, we use the Splunk platform as our security information and event management system, leveraging the InfoSec app for Splunk that provides continuous monitoring and advanced threat detection to significantly improve our security.

We can manipulate and present data in Splunk in a way that provides us with a single pane-of-glass for our hybrid, multi-cloud environment and our third-party apps and systems. Splunk observability tools have likewise helped us to track browser-based applications like our online gaming apps to monitor details related to security and performance.

Splunk and Google Cloud have transformed how we operate. We can now quickly ingest and analyze data at scale within our refined approach to security management by offloading software management to Splunk and Google Cloud. This ability enables us to approach security more strategically, and positions us to integrate more AI/ML capabilities into our products for even greater governance and performance.

This is just the beginning of our journey with Splunk and Google Cloud. We’re excited to see the innovation we can continue bringing to the gaming community worldwide.

As community and business concern over global warming, sustainability, and energy security continues to rise, companies such as Bharat Light & Power (BLP) are working on answers.

Founded in 2010 and located in Bangalore and Delhi, BLP is one of the leading clean energy generation companies in India. According to Tejpreet Singh Chopra, Chief Executive Officer of BLP, the business started with the aim of delivering clean energy from renewable sources such as wind, solar, biomass, and hydro to the 300 million residents of India that did not have reliable access to power at that time.

However, BLP soon began experiencing problems ensuring availability of the wind turbines that powered utility-scale wind power generation across the country.

“While we were investing large amounts building wind farms, the failure of the smallest component could shut down an entire wind turbine and compromise our efficient operation,” says Chopra. “A $5,000 battery could fail and shut down a $2.5 million piece of equipment!”

BLP transformed its legacy thinking about man, machine, technology, and process, and implemented an AI and Internet of Things (IoT) project. The business used hundreds of tags from its wind turbines to capture and send machine behavior and performance data to a control center. This data triggered an 18-month project to create AI and machine learning algorithms that would enable engineers to predict component failures in wind turbines, improve generation, and adjust maintenance and replacement schedules accordingly.

This activity provided the foundation of a technology business—one of the three companies that comprise BLP—that delivers enterprise AI for industrial uses over a platform, branded Orion.

“We provide an end-to-end solution that enables businesses in industries such as transportation, logistics, ports, infrastructure, oil and gas, supply chain manufacturing, steel, and automotive to improve productivity,” says Chopra.

As the business expanded, it began to extend beyond its core “AI for industry” mission. It recruited an IoT team to help factory owners and operators enable the programmable logic controllers (PLC) and supervisory control and data acquisition (SCADA) systems that run disparate equipment and machines—such as production line machines of different ages and countries of origin—to talk to each other and provide usable data and insights.

Agility and adaptability key

BLP’s management team realized early that the business needed to be agile and adaptable to keep pace with changes in technology.

“We knew we would have to effectively destroy and remake the business every 18–24 months to remain relevant, so we needed a service that could support our dynamic infrastructure, data, and AI needs,” says Chopra.

The business started operations on a cloud service but quickly ran into problems. “We found in the world of industry—the vast amounts of data, the variety of sources of data, and the complexity of insights required—created a very different set of challenges relative to the consumer technology environment,” says Chopra. “Our cloud provider could not provide an architecture that made sense for an industry-focused solution.”

Google Cloud to power AI and visual analytics

Within two years, BLP advanced its strategy and focused on using open source to reduce the cost of its architecture. As Orion matured and its take-up grew, the business began looking at multinational cloud services to run the forthcoming version 3.0 of the platform.

BLP found Google Cloud provided the best fit for its needs for a range of reasons, including the ease of use of Google Cloud services—likened by the BLP technology team as “the equivalent of a consumer app experience”—and its high quality database services, high availability, fast response times, and the power to run the platform’s AI and visual analytics.

BLP established as its key objective a 20% reduction in costs over its previous cloud service and availability levels exceeding 99.9%. The business completed the deployment in June 2019 following discussions and input from Google Cloud’s engineering and architecture experts prior to and during the first stage of the project.

Cloud IoT Core—a managed service that allows organizations to connect, manage, and ingest data from dispersed devices—is the cornerstone of the architecture supporting the latest latest version of the Orion platform, Orion 4.0. BLP has also created a data pipeline based on Cloud Pub/Sub event ingestion and delivery and Cloud Dataflow data processing.

Cloud Bigtable provides a high-performance NoSQL database service for the platform’s analytical workloads and BigQuery delivers a powerful analytics data warehouse. Cloud Functions enables the business’s developers to build event-driven serverless applications.

This architecture currently captures, processes, analyzes, and reports on 8 million data points in 578 turbines around the world per day and processes data from 200,000 data points in 4,000 sensors per day.

The Google Cloud architecture supports the data visualization and reporting and the AI and machine learning-powered products created by BLP. These reports and products enable users to monitor remote assets and use AI-based analytics to predict machine failures before they occur, sequence failure events, and maximize equipment uptime.

A range of benefits to customers

With Google Cloud, BLP is delivering projects with a range of benefits to customers, including increasing manufacturing productivity by at least 5% and reducing costs by about 10%.

“We’ve enabled one of the largest electrical companies in India to compare production line performance by capturing data from PLC and SCADA systems and extracting it to Google Cloud for processing, analysis, and reporting,” says Chopra. “For another customer, we’ve deployed an IoT system that has saved them about $200,000 in energy costs over six months.”

The business is also providing monitoring, reporting, and analysis to predict likely failures of gearboxes, bearings, generators, and blades in 2,000 wind turbines—that provide close over 2 GW of wind power—in countries such as France, Germany, Italy, India, Portugal, Spain, the United Kingdom, and the United States.

Furthermore, BLP is providing visual analytics to help one of the largest ports in the world detect when workers are not wearing helmets or safety equipment.

“We also do a lot of inventory track-and-trace work to help companies improve supply chains and help factories keep track of tooling through Bluetooth low-energy technologies,” says Chopra.

With Google Cloud, BLP is now ideally placed to execute its business strategy of helping customers improve productivity and increase growth, control and reduce costs, and enhance quality and safety.

“We are realizing this strategy by working to become the best company in the world at using AI to predict machine failure,” says Chopra. “Our underlying technology strategy entails enabling the most advanced IoT hardware used by industry to talk to software and the cloud, using the most powerful AI cloud around—which is why we chose to marry our AI algorithms with Google Cloud—and delivering insights through visualization.”

Testing Edge TPU

The business is now testing Edge TPU to run AI at the edge in high-performance, small-footprint, and low-power ASIC environments.

“We think that will be the next big revolution as TPU costs come down,” says Chopra. “It will be a considerable benefit performing AI at the edge rather than involving the full infrastructure of the cloud.”

BLP is keen to build on its existing relationship with and use of Google Cloud to further transform manufacturing worldwide.

“Manufacturers are pushing the boundaries of quality and cost through initiatives such as Lean Six Sigma, Zero Defects, poka-yoke, just in time, and other methodologies and approaches,” says Chopra.

“The next wave of manufacturing improvements are coming through industry 4.0, AI, IoT, and Google Cloud’s analytics, databases and other services. These are ideal to power this change in the industrial world.”

When critical services fail, businesses risk losing revenue, productivity, and trust. That’s why Google Cloud customers running SAP applications choose to deploy high availability (HA) systems on Google Cloud.

In these deployments Linux operating system clustering provides application and guest awareness for the application state and automates recovery actions in case of failure — including cluster node, resource or node failover or failed action. 

Pacemaker is the most popular software Linux administrators use to manage their HA clusters, which includes automating notifications about events — including failover fencing and node, attribute, and resource events — and reporting on events. With automated alerts and reports, Linux administrators can not only learn about events as they happen, but they can also make sure other stakeholders are alerted to take action when critical events occur. They can even discover past events to assess the overall health of their HA systems. 

Here, we break down the steps to setting up automated alerts for HA cluster events and alert  reporting.

How to Deploy the Alert Script

To set up event-based alerts, you’ll need to take the following steps to execute the script. 

1. Download the script file ‘gcp_crm_alert.sh’ from

https://github.com/GoogleCloudPlatform/pacemaker-alerts-cloud-logging

2. Under root user, add exec flag for the script and execute deployment with:

  chmod +x ./gcp_crm_alert.sh
./gcp_crm_alert.sh -d

3. Confirm that the deployment runs successfully. If it does, you will see the following INFO log messages:

In the Red Hat Enterprise Linux (RHEL) system:

gcp_crm_alert.sh:2022-01-24T23:48:30+0000:INFO:'pcs alert recipient add gcp_cluster_alert value=gcp_cluster_alerts id=gcp_cluster_alert_recepient options value=/var/log/crm_alerts_log' rc=0

In the SUSE Linux Enterprise Server (SLES):

gcp_crm_alert.sh:2022-01-25T00:13:27+00:00:INFO:'crm configure alert gcp_cluster_alert /usr/share/pacemaker/alerts/gcp_crm_alert.sh meta timeout=10s timestamp-format=%Y-%m-%dT%H:%M:%S.%06NZ to { /var/log/crm_alerts_log attributes gcloud_timeout=5 gcloud_cmd=/usr/bin/gcloud }' rc=0

Now, in the event of a cluster node, resource, node failover, or failed action, Pacemaker will start the alert mechanism. For further details on the alerting agent, check out the Pacemaker Explained documentation.

How to Use Cloud Logging for Alert Reporting

Alerted events are published in Cloud Logging. Below is an example of the log record payload, where the cluster alert key-value pairs get recorded in the jsonPayload node.

{

 "insertId": "ktildwg1o3fbim",   "jsonPayload": {     "CRM_alert_recipient": "/var/log/crm_alerts_log",     "CRM_alert_attribute_name": "",     "CRM_alert_kind": "resource",     "CRM_alert_status": "0",     "CRM_alert_rsc": "STONITH-sapecc-scs",     "CRM_alert_rc": "0",     "CRM_alert_timestamp_usec": "",     "CRM_alert_interval": "0",     "CRM_alert_node_sequence": "21",     "CRM_alert_task": "start",     "CRM_alert_nodeid": "",     "CRM_alert_timestamp": "2022-01-25T00:17:06.515313Z",     "CRM_alert_timestamp_epoch": "",     "CRM_alert_desc": "ok",     "CRM_alert_target_rc": "0",     "CRM_alert_version": "1.1.15",     "CRM_alert_attribute_value": "",     "CRM_alert_node": "sapecc-ers",     "CRM_alert_exec_time": ""   },   "resource": {     "type": "global",     "labels": {       "project_id": "gcp-tse-sap-on-gcp-lab"     }   },   "timestamp": "2022-01-25T00:17:09.662557309Z",   "severity": "INFO",   "logName": "projects/gcp-tse-sap-on-gcp-lab/logs/sapecc-ers%2F%2Fvar%2Flog%2Fcrm_alerts_log", "receiveTimestamp": "2022-01-25T00:17:09.662557309Z" 

}

To get notified of a resource event — for example, when the HANA topology resource monitor fails — you can use the following filter for the alerting definition:

jsonPayload.CRM_alert_node=("hana-venus" OR "hana-mercury") -jsonPayload.CRM_alert_status="0" jsonPayload.CRM_alert_rsc="rsc_SAPHanaTopology_SBX_HDB00" jsonPayload.CRM_alert_task="monitor" To define an alert for a fencing event, your can apply this filter: jsonPayload.CRM_alert_node=("hana-venus" OR "hana-mercury") jsonPayload.CRM_alert_kind="fencing" The fencing log entry gets recorded with warning severity to give you deeper insight, and this additional information is also helpful for more specific filtering criteria: {   "insertId": "1plznskfjsxt82",   "jsonPayload": {     "CRM_alert_attribute_value": "",     "CRM_alert_recipient": "/var/log/crm_alerts_log",     "CRM_alert_rsc": "",     "CRM_alert_rc": "0",     "CRM_alert_timestamp_usec": "529261",     "CRM_alert_desc": "Operation reboot of hana-mercury by hana-venus for crmd.2361@hana-venus: OK (ref=2a9bf814-9adf-4247-af3f-94ac254fc3ca)", "CRM_alert_target_rc": "",     "CRM_alert_nodeid": "",     "CRM_alert_kind": "fencing",     "CRM_alert_node_sequence": "33",     "CRM_alert_task": "st_notify_fence",     "CRM_alert_status": "",     "CRM_alert_exec_time": "",     "CRM_alert_attribute_name": "",     "CRM_alert_timestamp_epoch": "1643072786",     "CRM_alert_version": "1.1.19",     "CRM_alert_timestamp": "2022-01-25T01:06:26.529261Z",     "CRM_alert_interval": "",     "CRM_alert_node": "hana-mercury"   },   "resource": {     "type": "global",     "labels": {       "project_id": "gcp-tse-sap-on-gcp-lab"     }   },   "timestamp": "2022-01-25T01:06:27.267017052Z",   "severity": "WARNING",   "logName": "projects/gcp-tse-sap-on-gcp-lab/logs/hana-venus%2F%2Fvar%2Flog%2Fcrm_alerts_log", "receiveTimestamp": "2022-01-25T01:06:27.267017052Z" 
} 

Alerts can be delivered through multiple channels, including text and email. Below is an example of an email notification for our earlier example, when we defined an alert for a HANA topology resource monitor failure:

You can write and apply filters to your log-based alerts to isolate certain types of incidents and analyze events over time. For example, the following script will surface a resource event occurring within a two-hour window on a specific date:

timestamp>="2022-01-25T00:00:00Z" timestamp<="2022-01-25T02:00:00Z"

jsonPayload.CRM_alert_kind="resource"

With the ability to analyze these logged alerts over time, determine whether event patterns warrant any action.

[SIDEBAR]

The alert script prints details in the standard output and in the log file /var/log/crm_alerts_log, and this can grow over time. We recommend that the log file is set with the Linux logrotate service in order to limit the file system space. Use the following command to create the necessary logrotate setting for the alerting log file:

cat > /etc/logrotate.d/crm_alerts_log << END-OF-FILE  /var/log/crm_alerts_log {   create 0660 root root   rotate 7   size 10M   missingok   compress   delaycompress   copytruncate   dateext   dateformat -%Y%m%d-%s   notifempty } END-OF-FILE 

[END SIDEBAR]

Tips for Troubleshooting When you first deploy your alert script, how can you tell for certain that you’ve done it correctly? Use the following commands to test it out:

In RHEL:

pcs alert show 

In SLES:

sudo crm config show | grep -A3 gcp_cluster_alert 

You should see the following if the script is correct:

In RHEL:

Alerts:  Alert: gcp_cluster_alert (path=/usr/share/pacemaker/alerts/gcp_crm_alert.sh)   Description: "Cluster alerting for hana-node-X"   Options: gcloud_cmd=/usr/bin/gcloud gcloud_timeout=5   Meta options: timeout=10s timestamp-format=%Y-%m-%dT%H:%M:%S.%06NZ   Recipients:    Recipient: gcp_cluster_alert_recepient (value=gcp_cluster_alerts)     Options: value=/var/log/crm_alerts_log In SLES:

alert gcp_cluster_alert "/usr/share/pacemaker/alerts/gcp_crm_alert.sh" \ meta timeout=10s timestamp-format="%Y-%m-%dT%H:%M:%S.%06NZ" \ to "/var/log/crm_alerts_log" attributes gcloud_timeout=5 gcloud_cmd="/usr/bin/gcloud" 

If the commands do not display the alerts properly, re-deploy the script.

In case there is an issue with the script, or if the Cloud Logging records are not presenting as expected, examine the script log file /var/log/crm_alerts_log. The errors and warning can be filtered with:

egrep '(ERROR|WARN)' /var/log/crm_alerts_log 

Any Pacemaker alert failures will be recorded in the messages and/or Pacemaker log. To examine recent alert failures, use the following command:

egrep '(gcp_crm_alert.sh|gcp_cluster_alert)' \   /var/log/messages /var/log/pacemaker.log 

Keep in mind, though, that the Pacemaker log location may be different in your system from the one in the example above.

From reactive to proactive

Your SAP applications are too critical to risk outages. The most effective way to manage high availability clusters for your SAP systems on Google Cloud is to take full advantage of Pacemaker’s alerting capabilities, so you can be proactive in ensuring your systems are healthy and available.

Learn more about running SAP on Google Cloud.