Nowadays, the development and maintenance of most services and applications require a reliable and scalable infrastructure that can handle a large number of concurrent requests. However, traditional approaches to IT infrastructure management are often no longer able to meet the demands that high-load systems place on it.
In this article, we'll discuss the tasks a high-load infrastructure must perform and what current approaches to its development exist.
High-load infrastructure: when do you need it?
High-load systems process large volumes of data and thus generate value for the business. The flip side, however, is that any system failures and service downtime result in huge financial losses for companies. According to Gartner, the losses of large online services reach an average of $300,000 per hour in the event of downtime.
Therefore, the IT infrastructure must first and foremost provide uninterrupted operation of high-load systems and their resilience to peak loads. As a consequence, one of the main requirements for such an infrastructure is the ability to scale it and redistribute the load quickly.
The primary issues in high-load projects are the large volume of data, complexity, and speed of changes. Thus, when designing a high-load infrastructure, it is important to consider the following aspects of its functionality:
- Scalability: A high-load IT infrastructure must be able to handle a large number of concurrent requests without compromising performance. This requires a scalable architecture that can effectively handle sharp increases in traffic and resource consumption.
- Availability: A high-load IT infrastructure must remain available and respond quickly, even during unexpected traffic spikes, and automatically recover from failures.
- Latency: A high-load infrastructure must be able to respond to user requests with low latency, even in high-traffic environments. This can be achieved by a variety of methods, such as caching, the use of faster storage systems, and distributed computing.
Modern approaches to developing a high-load infrastructure
When developing high-load projects, it is important to consider that there are no standard solutions that would be suitable for any high-load system. However, to ensure the reliability of the system, the following general principles can be applied:
- Separate the parts of the system that affect its performance from those most susceptible to human error.
- Implement a testing system (unit testing, system integration testing, or manual testing).
- Establish an action plan and tools for quick system restoration in the event of a failure to minimize the consequences.
- Implement metrics, monitoring, and logging systems for diagnosing errors and the causes of failures.
Let's consider a few up-to-date approaches to designing a high-load IT infrastructure.
Cloud & Edge computing
Cloud solutions have already become a common element of an IT infrastructure in many companies, and demand will only grow in 2023. According to Gartner predictions, more than half of corporate infrastructure costs will be redistributed from traditional solutions in favor of cloud ones.
One of the upcoming trends is the use of edge computing to improve system stability and accelerate its work. Data processing is carried out not in data centers or the cloud, but on peripheral devices and local servers, that is, in the immediate vicinity of where the data is collected or produced.
This significantly reduces network signal latency and the load on main servers. Gartner predicts that by 2025, about 75% of all corporate traffic will be generated by edge computing. Examples of popular edge computing platforms include AWS Greengrass and Azure IoT Edge.
Containerization continues to be one of the prevailing approaches to managing a high-load IT infrastructure. It involves “packaging” an application and all its dependencies into a container, which can then be easily deployed and run on any infrastructure.
This allows for scaling and managing resources efficiently and increases the overall reliability of the system. You can run as many containers as your current workload requires, and quickly ramp them up as needed. Another key benefit of containerization is that it helps you to isolate different applications and services, preventing conflicts and reducing the risk of system-wide failures.
The most popular containerization tools are Docker and Kubernetes. Docker allows developers to package an application and its dependencies into a container and easily deploy it to different environments. Kubernetes is an open-source container orchestration system that can automatically manage container scaling, deployment, and replication, making it a great solution for high-load infrastructure.
This is a relatively new approach to high-load infrastructure management that allows developers to create and run applications without providing servers. Instead, the infrastructure is managed by the cloud provider, and the application runs in response to specific events or triggers instead of running in the background. This significantly reduces overall costs, as you only pay for the time you use server capacity.
Serverless computing works well with high-load, event-driven operations and services such as image processing, data streaming, and IoT applications. As the load increases, a provider enables automatic scaling and allocates additional resources.
Popular serverless platforms include AWS Lambda, Azure Functions, and Google Cloud Functions. They automatically handle scaling, provisioning, and resource allocation, and offer a pay-as-you-go pricing model.
This approach is not new, but it does not lose its relevance. Distributed computing involves splitting a large task into smaller ones, which are distributed among several machines.
Distributed computing is often used for large-scale data processing, machine learning, and other resource-intensive tasks. If necessary, you can add or remove computing devices to or from the network to balance the load.
Distributed computing improves system performance and scalability, as well as increases fault tolerance since a failure on one of the servers is less likely to make the entire task impossible. Popular modern distributed computing platforms include Apache Hadoop and Apache Spark.
Building a high-load IT infrastructure can be a non-trivial task, so it's helpful to have an understanding of current approaches to high-load project management. Technologies such as containerization, serverless computing, cloud platforms, edge computing, and distributed computing can help develop an efficient and fault-tolerant infrastructure. Each approach has its benefits and drawbacks, so the choice depends on the specific requirements and limitations of your high-load system.
It's also worth noting that these approaches are not mutually exclusive and can be combined to create a more robust infrastructure. With the right combination of hardware and software, organizations can create a high-load IT infrastructure that can handle peak loads and meet ever-growing business requirements.