Share your experience using Oracle Compute Cloud Service

Creating a target group is essential when setting up Amazon EC2 Auto Scaling. You can specify the target capacity, indicating the desired number of instances within the same instance family. Additionally, you define CPU thresholds to trigger scaling actions, specifying when to increase or decrease instances. Moreover, you configure the load balancer to communicate with the front end.

Amazon EC2 Auto Scaling operates at a different level, working in parallel to efficiently manage workload distribution. Primarily, it focuses on orchestration rather than directly managing EC2 instances for deployment and configuration. It uses automated processes to deploy and manage ports, leveraging Application Load Balancers to effectively handle data communication and management.

It is essential to architect your system depending on the data type and workload requirements. For large datasets, consider storing them in cloud storage solutions like Amazon S3 and utilizing services like Amazon Athena or AWS Glue for querying and processing. Using Amazon RDS for traditional databases like Oracle or SQL Server could benefit structured data or transactional workloads. Additionally, AWS provides various managed services tailored to specific use cases, which can be leveraged to optimize performance and scalability.

Amazon EC2 Auto Scaling offers various benefits but lacks certain features for fine-grained customization compared to other cloud providers like GCP. Users are constrained by predefined instance families in EC2 when selecting instance types for scaling. Unlike GCP, where users can independently scale resources such as memory or CPU, EC2 doesn't offer this flexibility. 

This limitation can be problematic for users who require specific resource configurations, such as needing more CPU without increasing memory or vice versa. Customizing instance families based on specific resource needs would benefit users by providing more tailored scaling options to optimize their applications' performance and cost.

The product is stable. It is the leading service offered by AWS. It provides reliable scaling capabilities for managing resources efficiently. I've encountered minimal downtime and excellent support from AWS.  AWS support promptly notifies us if any resource encounters issues and ensures that it's properly addressed or moved to a healthier resource for optimal performance.

The product is scalable. We use vertical scaling. It can leverage horizontal scaling, involving clustering instances to handle workload demands. This process is swift and doesn't require manual adjustments of instance types. It simplifies scaling operations significantly.

I have the 45 dollar support pack for Amazon EC2 Auto Scaling. With this support package, if there are any issues or questions about the service, I can email the support team for assistance.

I used to deploy using cloud automation templates rather than directly provisioning instances from HP. Therefore, I have different files for deploying the resources. Generally, I use configurations and predefined images to create instances automatically. This process is automated through pipeline processes rather than manual intervention.

Amazon EC2 Auto Scaling is a vital component with real-time data. While some data remains in traditional offline formats, much of it is stored dynamically in the cloud. This setup facilitates efficient responses and operations. The effectiveness of EC2 Auto Scaling depends on the nature of the data, including its source and storage location. For instance, data lakes play a significant role in organizing and analyzing data as per domain requirements, aiding in the creation of models and other processes.

Amazon EC2 Auto Scaling is versatile in adjusting resources like memory and CPU according to different application needs. For instance, in a memory-intensive application where multiple computing instances are used, the requirement may not be for high memory, but rather for a significant number of vCPUs to process data efficiently. The four gigabytes memory size may enough, the computing processes may demand eight vCPUs to handle data effectively. Scaling options can be adjusted accordingly, perhaps opting for instances with higher vCPU counts while maintaining memory at a lower size.

The integration is easier because of the VPC and unavailable endpoint.

We use open-source Python automation tools for deploying our applications on Amazon EC2 Auto Scaling. These tools help us manage instances dynamically based on code commits. As we scale our infrastructure, we rely on these tools to efficiently handle the increasing workload.

You can use the available endpoints to facilitate communication between the service and the instances within the private subnet for security purposes. This ensures a private and secure environment. It's essential to adhere to these practices for optimal security. 

Overall, I rate the solution an eight out of ten.

We use the solution to create virtual private networks for our customers. We use it for connectivity and traffic control. We also use subnets.

The solution enables us to provide a private cloud for our customers. The solution is secure. We use it for the security purposes. We can do networking and create routes. Subnetting is good for security purposes. We can add inbound and outbound groups. We can allow the traffic. We can decide whether to allow inbound or outbound traffic according to the customers’ needs.

If something needs to be highlighted, peering must be maintained.

I have been using the solution for the past two years.

I haven’t had any bugs or breakdown issues. I rate the stability an eight out of ten.

The support is good.

The deployment is easy. The UI is user-friendly.

I will recommend the product to others. People can use the tool as a secure private network for their systems. Overall, I rate the product an eight out of ten.