Using Google Cloud Tools in Load Capacity Optimization
A high load capacity is not a negative attribute in IT. It is simply a characteristic of system’s operational conditions, which determines how well it can handle a certain level of demand. In fact, for some types of businesses, the ability of a system to handle a high load is a critical requirement.
In this article, we will dive deeper into the definition of high-load projects, determine what causes the high load, and open up about the opportunities and benefits of Google Cloud Platform tools over hardware-based solutions in an organization’s load capacity optimization efforts. We will also look at three practical cases of Google Cloud tools implementation from Kanda’s recent experience.
What causes high load?
The high load can occur due to several reasons, including:
- A high number of system requests, which is common in systems such as CRM and ERP with a large number of users, or, for instance, in call centers that handle a high volume of calls.
- Processing a large amount of data, which can be the case in monitoring systems with many connected devices, business intelligence systems with numerous indicators. This is overall relevant for CRM and ERP systems that process a significant amount of data.
- Incorrect machine code and lack of optimization, which can lead to high-load situations where the server cannot handle the tasks.
3 characteristics of high-load projects
Here are three common characteristics of high-load projects.
1. The project has a massive user base
Web applications, which are often classified as high-load, may have thousands or even hundreds of thousands of users. While it’s difficult to pinpoint a specific number, it’s evident that processing ten requests per day, such as an online store, doesn’t qualify as a high load.
2. The project system is distribute
When a system needs to handle gradually increasing amounts of data, one server is not enough. Extensive high-load systems, such as Google or Facebook, operate on hundreds of servers.
However, the necessity for numerous servers is triggered not only by a high load but mostly because servers tend to fail quickly at high rates. Therefore, having more servers increases the likelihood of the system quickly recovering from a failure.
3. The project is high-performing
To provide an immediate response, the system within a high-load project requires significant resources such as CPU (Central Processing Unit), RAM (Random-access Memory), disk space, and more. These resources need to be available and operate fast.
This factor reflects the paradox of high-load systems: the faster they grow, the more critical it becomes to monitor and manage resources. As the system’s users grow, the number of requests also naturally increases, and along with them, the resources are required to be highly interactive.
Therefore, a high-load system needs constant scaling, which can be quite challenging to set up.
Benefits of using cloud tools in load capacity optimization
When it comes to choosing between cloud or hardware solutions for high-load projects, it should be noted that the cloud tools offers several benefits over hardware:
Cloud tools allow for easy scaling of resources to meet demand. This means that as traffic to your high-load project increases, you can easily add more resources to handle the load.
Cloud tools offer redundancy and failover capabilities, which ensure that your high-load project stays up and running even if one or more servers fail.
Cloud tools offer a pay-as-you-go model, which means you only pay for the resources you use. It helps to reduce costs and ensure that you are not over-provisioning resources that are not being utilized.
Cloud tools offer a wide range of configuration options, which allow you to customize your infrastructure to meet the specific needs of your high load project.
Load capacity optimization techniques
In today’s fast-paced digital environment, DevOps engineers skillfully optimize high-load projects through Google Cloud tools by using the following techniques:
This technique allows for the automatic provisioning and de-provisioning of resources in response to changes in demand, which ensures the project always has the resources it needs to handle the load.
Load Balancing distributes traffic across multiple servers to ensure that no single server is overloaded and to improve the performance and availability of a high-load project.
It is a technique used to store frequently accessed data in memory, reducing the need for repeated database queries. The technique can reduce the load on your database and thus improve the performance of the system.
Monitoring and Optimization
This involves using Google Cloud monitoring tools to track resource usage and identify areas for optimization. DevOps professionals can then make changes to their infrastructure to improve performance and reduce costs.
Using Google Cloud Tools in load capacity optimization: Kanda’s experience
Below, we will provide details on the recent Kanda’s case where Kanda’s team successfully implemented Google Cloud tools as a part of collaborative efforts with the customer to redesign its system architecture and optimize load capacity.
Kanda has been working closely with the customer, a big cyber security provider, on one of its projects. As a result of the collaborative work, the tech team made a number of advancements to optimize the load capacity.
In the three examples below, we showcase the benefits of implementing cloud-based load capacity optimization solutions instead of hardware-based tools, which significantly enhance the customer’s system performance.
1. Using Google VPC Traffic mirroring instead of Netfilter mirroring
Google VPC Traffic Mirroring is a cloud-native feature provided by Google Cloud Platform that allows users to copy network traffic from a source virtual machine to a destination virtual machine. At the same time, Linux/Netfilter mirroring involves configuring the Linux kernel to mirror network traffic.
One advantage of Google VPC Traffic Mirroring is its ease of use, with a simple point-and-click interface, as opposed to Linux/Netfilter mirroring, which requires significant technical expertise and may involve writing custom scripts.
Additionally, Google VPC Traffic Mirroring is highly configurable, allowing users to filter and mirror specific types of traffic, while Linux/Netfilter mirroring is less flexible and may not offer the same level of customization.
2. Using Google external Load Balancer instead of one big Compute instance
Google External Load Balancer and a single big Compute instance are two approaches to managing high-load projects. A Google External Load Balancer allows you to handle large amounts of traffic by distributing it across multiple instances. It can also distribute traffic to healthy instances, even if one or more of them are down, improving availability.
Additionally, it can distribute traffic evenly across multiple instances, improving response times and minimizing the impact of traffic spikes.
Moreover, it can be more cost-effective than a single big Compute instance, which can be expensive to run and maintain.
In contrast, a single big Compute instance may struggle to handle high traffic, making it difficult to scale the system. It also represents a single point of failure, which can lead to downtime in the event of hardware or software failure.
3. Google Certificates Manager and GCP external Load Balancer instead of HAProxy SSL offload component
Google Certificates Manager and GCP External Load Balancer are two cloud-native features provided by Google Cloud Platform that offer a simple and efficient way to manage SSL certificates and offload SSL processing. They are cost-effective solutions and can be easily managed through the GCP console or API, making them an ideal solution for those using the Google Cloud Platform, while HAProxy may struggle to handle high traffic volumes.
To sum up
Google Cloud Tools offer a range of tools and services that can assist with optimizing system load in high-load projects.
Their focus on performance and efficiency means that websites and applications running on them are able to maintain high levels of performance, even during peak usage periods. By leveraging the tools and services offered by Google Cloud Platform, organizations can ensure that their high-load projects are able to meet the demands of their users and remain highly available and responsive.
With more than 25 years of experience in modernizing legacy solutions and developing applications for businesses ranging from startups to large corporations, Kanda offers comprehensive expertise in cloud engineering. Our skills extend to private, hybrid, and public cloud environments, enabling our clients to build secure, high-performance, and scalable solutions that boost return on cloud investments, enhance business agility, reduce costs, and mitigate risks.