When it’s time for a new system, the capital outlay is huge, and it takes many months to investigate and plan. Which system will be the best fit for the business, and will it offer flexibility and scalability in the long term?
Possibly the most important decision is that of system architecture: the design of a system, its components and their relationships, user needs and user interactions.
Without a sound architecture that is suitable for the application and scalable, businesses run into trouble when they experience growth a few years later.
Below, we look at the different types of architecture, with examples of suitable applications.
Monolithic and service-oriented architectures
In the early days of computing, business applications were hosted on one platform that did everything. The operating system, application software and databases were all handled in one monolithic structure. Communications with user terminals happened via cables.
Scalability was very limited. It could be expanded vertically by adding memory and CPU resources. However, expanding it horizontally, such as adding new servers, was not possible. In time, integration between systems became a requirement.
Enter service-oriented architecture (SOA). SOA architecture was designed to use common interfaces that made software components interoperable.
The interfaces between the different business models provide loose couplings that don’t require any knowledge of the business functions.
The applications behind the interfaces can be written in any programming language, supplied by a software vendor, or obtained as open-source applications.
The service interface may be written in web service definition language based on XML. Exposure to the various modules happens through standard network protocols such as HTTP.
Integration between applications is usually handled by a centralized enterprise service bus (ESB). This is software that manages the composition of the different components as well as the connectivity, communication and routing.
Many legacy systems today still run on monolithic-type systems, with modern communications taking place via networks and protocols. Setup and maintenance of these systems is simple and cost-effective.
However, there is a need today for large volumes of data as well as interaction between various business modules that run on a diverse range of platforms. The monolithic-type infrastructure is thus limited in terms of features and capacity. Limited scalability means that it is no longer viable for large organizations.
Microservices architecture
Microservice technology differs from SOAs. Microservices comprise separate business applications that are independent of each other, while SOAs standardize the way that services within an organization integrate with one another.
Microservices have their own technology that includes database and data management. They can be defined by their separate business functions, and they interact with each other using network protocols.
This gives more scope for horizontal scalability as the capacity of the individual units can be increased individually as needed. Individual upgrades are also less costly.
Microservices also enable the use of diverse technologies in terms of business modules.
APIs empower communication between the various business modules, facilitating the integration of data.
Development is easier and faster when limited to single business applications, and upgrades are also easier to implement.
Event-driven architecture
Event-driven architecture (EDA) consists of event notifications, producers and consumers. When an event is triggered, an event producer sends a notification to the event consumer that activates the necessary routine to complete the task.
When used in a multiservice environment, event triggers are capable of handling a multitude of instructions simultaneously. This technology is suitable for large organizations such as banks and online shopping platforms where there is a need for large volumes and fast, real-time processing.
EDA architecture decouples the various system components, promoting asynchronous processing and optimum scalability of the individual components.
Serverless architecture
Serverless architecture is a cloud-based option that allows you to focus on writing your software while the cloud service providers take care of the infrastructure. Maintenance, capacity, configuration and scaling are all managed by the service provider. There is still a server involved, so it’s wise to cover the scalability features in the contract.
Microkernel architecture
The kernel of a system is the basic operating system (OS) that is required to manage functions such as file systems, networking protocols, device drivers and address space.
Microkernel architecture moves most of this functionality out to the user space, keeping only the bare necessities in the kernel space for the OS to run. The rest of the system is handled as plugin modules. Plugins are independent of one another and thus easier to modify.
An example of a microkernel application would be having a financial system at the core with multiple plugins to handle various modules required in an ERP environment.
AI can help you choose the right software architecture for your business
Each of the above architectures has advantages and disadvantages. How do you make the right choice?
Software engineers are well-placed to manage the various AI tools available on the market. With their in-depth knowledge of software architectures and communication protocols, a thorough system evaluation is within their reach.
If you’re considering making a career out of AI software development and system architecture, then an online Master’s in Computer Science with Baylor University would be an ideal starting point. Through the program, students develop skills in AI and machine learning, data science, programming, problem-solving, ethical awareness and more.
Managing your software evaluation
- Do a thorough needs analysis, including input from key stakeholders.
- Collect and analyze your current and historical data.
- Evaluate similar businesses and their systems.
Many AI tests can be run on your data, and simulations can be done that are relevant to your business environment.
Requirements analysis
With the use of natural language processing tools and decision support systems, AI will analyze your requirements.
Architecture suggestions
Using machine learning models, you can train AI to predict the performance of the various architectures using your data.
Expert systems simulate the reasoning and decision-making abilities of human experts to determine the suitability of the different architectures.
Simulation
Simulate the various architectures under different conditions and create prototypes using AI-based tools.
Cost analysis
Use predictive analytics to do a cost-benefit exercise on development and operational costs.
A thorough evaluation is essential
Gather your data and select the AI tools that you think will suit your needs.
Train AI to analyze your requirements and predict outcomes based on the different architectures. Simulate the different options, and then tweak them where necessary and try again.
By the time you have come up with the perfect solution, you will be proficient in software architecture and will also be knowledgeable about AI.