Shulou(Shulou.com)06/03 Report--

Over the past decade, the public cloud has gradually evolved into a place for companies to develop, test, and run their applications. Whether large enterprises or small and medium-sized enterprises, most of them have begun to run workloads on cloud servers, or plan to go to the cloud in the near future, because the high investment and high maintenance costs of traditional servers will lead to their gradual elimination.

For the engineering team, with the rapid expansion of cloud service offerings, the transition from an on-premises data center to a cloud server is essential.

Here, we take bidding examples as an example to show how to help enterprises reduce cloud computing costs, and why cloud scaling group solutions can automate the process of using bidding instances to run workloads.

One of the main challenges in managing cloud resource costs is to predict cloud computing usage for application workloads. To avoid deviating from budget estimates, we need to predict computing resource consumption during routine and peak traffic periods. In addition, the permissions of its application need to be adjusted to the most appropriate instance type to avoid being "too large", which may lead to underutilization of the instance.

During peak traffic, the infrastructure should automatically expand the instance to support the upcoming application load, and when the peak traffic decreases, the cluster should reduce the instance back to normal. Running production workloads with on-demand instances is costly and significantly increases cloud charges.

In addition, sufficient capacity is not always guaranteed when instance availability is full-customers disrupt services when they try to quickly switch to another instance, and data migration, data loss, active sessions, and HTTP requests are also challenges.

To address the challenge of cloud workload automation on competitive instances, Cloudam has developed cloud scaling groups on which users can manage, configure, and extend cloud computing infrastructure.

1. Bidding instance with SLA

Cloud scaling groups use bidding instances to provide cost-effective computing clusters to their users and reduce costs by up to 80%. Based on historical and statistical data, cloud scaling groups predict outages 15 minutes in advance and automatically migrate loads to different computer types and regions.

In cases where specific instance types are unstable or unavailable, cloud scaling groups fall back to on-demand instances to ensure high availability and consistency. The cloud scaling group will also ensure gradual preemption to ensure the proper operation of the service.

2. Perfect integration of different types of instances

The cost-effective strategy of cloud scaling groups not only depends on the configuration of competitive instances, but also depends on competitive instances. If you have purchased prepaid instances in advance, you can select them in the cloud scaling group, and then configure bidding instances and on-demand instances.

At any given time, the cloud scaling group automatically scales the application based on the best combination of instance types, while ensuring a 99.99% SLA.

3. Automatic and predictive scaling

In terms of infrastructure scaling, cloud scaling groups can automatically scale clusters based on metrics or events, and provide predictive auto-scaling capabilities.

The cloud scaling group constantly analyzes your application and optimizes the underlying infrastructure in real time. By analyzing the results, evaluate your workload and determine the best combination that includes server performance, type, and lifecycle. The combination is a well-optimized and fully utilized collection of cloud resources, which dynamically provides on-demand and excess capacity of cloud servers, meeting demand and reducing costs.

4. Stateful applications

As mentioned earlier, using bidding instances to reduce cloud computing costs applies only to stateless applications that can handle interruptions.

The concepts of data integrity and consistency are critical when managing workloads. This may be trivial when running on on-demand instances, but it's not that simple when using bidding instances. To meet this challenge, cloud scaling groups have built-in support for stateful applications, so extend competitive instances to other use cases.

The use of competitive instances is considered the most influential strategy for running cost-effective workloads in the public cloud. However, the management and coordination of competitive instances and the automatic failover process after application interruption are still pain points for users. Today, cloud scaling groups optimize the cost of cloud resources by simplifying and automating the entire process to solve problems.

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