Chaos engineering is a method of testing distributed software that deliberately introduces failure and faulty scenarios to verify its resilience in the face of random disruptions.
Gremlin is a simple, safe, and secure way to improve the resilience of your systems by using Chaos Engineering to identify and fix failure modes.
Gremlin is a cloud-native platform that runs in any environment. Gremlin supports all public cloud environments — AWS, Azure, and GCP — and runs on Linux, Windows, and containerized environments like Kubernetes, and bare metal.
This Article is based on how to implement Chaos Engineering Experiments Using Gremlin on Google Cloud.
See more at: https://medium.com/google-cloud/gremlin-chaos-engineering-on-google-cloud-2568f9fc70c9
A multi-cloud approach is nothing but leveraging two or more cloud platforms for meeting the various business requirements of an enterprise. The multi-cloud IT environment incorporates different clouds from multiple vendors and negates the dependence on a single public cloud service provider. Thus enterprises can choose specific services from multiple public clouds and reap the benefits of each.
Given its affordability and agility, most enterprises opt for a multi-cloud approach in cloud computing now. A 2018 survey on the public cloud services market points out that 81% of the respondents use services from two or more providers. Subsequently, the cloud computing services market has reported incredible growth in recent times. The worldwide public cloud services market is all set to reach $500 billion in the next four years, according to IDC.
By choosing multi-cloud solutions strategically, enterprises can optimize the benefits of cloud computing and aim for some key competitive advantages. They can avoid the lengthy and cumbersome processes involved in buying, installing and testing high-priced systems. The IaaS and PaaS solutions have become a windfall for the enterprise’s budget as it does not incur huge up-front capital expenditure.
However, cost optimization is still a challenge while facilitating a multi-cloud environment and a large number of enterprises end up overpaying with or without realizing it. The below-mentioned tips would help you ensure the money is spent wisely on cloud computing services.
Most organizations tend to get wrong with simple things which turn out to be the root cause for needless spending and resource wastage. The first step to cost optimization in your cloud strategy is to identify underutilized resources that you have been paying for.
Enterprises often continue to pay for resources that have been purchased earlier but are no longer useful. Identifying such unused and unattached resources and deactivating it on a regular basis brings you one step closer to cost optimization. If needed, you can deploy automated cloud management tools that are largely helpful in providing the analytics needed to optimize the cloud spending and cut costs on an ongoing basis.
Another key cost optimization strategy is to identify the idle computing instances and consolidate them into fewer instances. An idle computing instance may require a CPU utilization level of 1-5%, but you may be billed by the service provider for 100% for the same instance.
Every enterprise will have such non-production instances that constitute unnecessary storage space and lead to overpaying. Re-evaluating your resource allocations regularly and removing unnecessary storage may help you save money significantly. Resource allocation is not only a matter of CPU and memory but also it is linked to the storage, network, and various other factors.
The key to efficient cost reduction in cloud computing technology lies in proactive monitoring. A comprehensive view of the cloud usage helps enterprises to monitor and minimize unnecessary spending. You can make use of various mechanisms for monitoring computing demand.
For instance, you can use a heatmap to understand the highs and lows in computing visually. This heat map indicates the start and stop times which in turn lead to reduced costs. You can also deploy automated tools that help organizations to schedule instances to start and stop. By following a heatmap, you can understand whether it is safe to shut down servers on holidays or weekends.
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The Google computer engine exchanges a large number of scalable virtual machines to serve as clusters used for that purpose. GCE can be managed through a RESTful API, command line interface, or web console. The computing engine is serviced for a minimum of 10-minutes per use. There is no up or front fee or time commitment. GCE competes with Amazon’s Elastic Compute Cloud (EC2) and Microsoft Azure.
#google compute engine #google compute engine tutorial #google app engine #google cloud console #google cloud storage #google compute engine documentation
If you looking to learn about Google Cloud in depth or in general with or without any prior knowledge in cloud computing, then you should definitely check this quest out, Link.
Google Could Essentials is an introductory level Quest which is useful to learn about the basic fundamentals of Google Cloud. From writing Cloud Shell commands and deploying my first virtual machine, to running applications on Kubernetes Engine or with load balancing, Google Cloud Essentials is a prime introduction to the platform’s basic features.
Let’s see what was the Quest Outline:
A Tour of Qwiklabs and Google Cloud was the first hands-on lab which basically gives an overview about Google Cloud. There were few questions to answers that will check your understanding about the topic and the rest was about accessing Google cloud console, projects in cloud console, roles and permissions, Cloud Shell and so on.
**Creating a Virtual Machine **was the second lab to create virtual machine and also connect NGINX web server to it. Compute Engine lets one create virtual machine whose resources live in certain regions or zones. NGINX web server is used as load balancer. The job of a load balancer is to distribute workloads across multiple computing resources. Creating these two along with a question would mark the end of the second lab.
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Cloud Technology is growing at an exceptional speed; Cloud Engineers have long agreed to accept the challenge of AWS and Azure certifications.
Now the cloud platform that has consistent demand is Google Cloud. Google Cloud is a niche play still, but with that comes some great salary demand.
In the meantime, Google Cloud Platform has some of the highest salaries out there for certified professionals and consistently beating out AWS the last few years.
“For the second straight year, the GCP Cloud Architect certification is associated with the highest salaries in IT. This newer credential-it launched in 2017-allows IT professionals to certify as a cloud architect on the GCP platform. It demonstrates the ability to design, develop and manage a secure, scalable and reliable cloud architecture.” -Global Knowledge Salary Survey dated Feb 2020
#google-cloud #cloud-computing #cloud-services #cloud-engineering #google-cloud-platform
Ever since the advent of Google Cloud, there has been an increased amount of services to facilitate customers and business requirements no matter what the enterprise domain is.
Google has put its efforts in coming up with solutions and products that not only fit the current user needs but also cater for future business needs.
That’s precisely why companies opt for Google Cloud Products as their prime cloud services for their business operations.
Nevertheless, another thing that is of much interest is the amount of “Security” baked into these Google products. There are certainly some significant considerations when deploying anything in the cloud.
#google-cloud #google-cloud-platform #cloud-computing #cloud-security #cloud