Exploring Distributed System Architecture: From Basic to Advanced

As the technological world becomes increasingly dependent on big data analysis, the effectiveness of distributed architectures makes it easier to process large amounts of data – without relying on too many computing resources.

Big data frameworks like Hadoop, web servers, and blockchain make the most of distributed systems. The transition away from monolithic systems has helped modern technology companies unlock the enormous potential seeded in modularity, service decoupling, and distributed systems.

Here we will discuss the fundamental and advanced concept of distributed systems.

Basics of distributed systems

Let's start by exploring the fundamentals of distributed systems. including definitions, advantages and challenges.

What is a distributed system?

A distributed system is essentially a network of autonomous computing systems that, although physically distant, are connected to a centralized network of computers driven by distributed system software. Autonomous computers are responsible for sharing requested resources and files over a communication network and performing tasks assigned by the centralized computer network.

The main components of a distributed system are:

• Primary System Controller: This is the controller that keeps track of everything in a distributed system and makes it easy to send and manage server requests across the system.
• Secondary Controller: The secondary controller acts as a process or communication controller that regulates and manages the server request flow and system translation load.
• UI Client: Managing the end user of the distributed system, a UI client provides important system information related to control and maintenance.
• System Data Store: Every distributed system comes with a data store which is used to share data across the system. Data can be stored on one machine or distributed across devices.
• Relational database: A relational database stores all data and allows multiple system users to use the same information simultaneously.

Why use distributed computing systems?

Distributed computing systems find their applications in the following sectors across the world.

There are several advantages to using distributed computing systems. Here are some of the most important professionals you should know:

Scalability

Distributed computing systems are highly scalable, allowing horizontal scalability. You can add more computers to the network and operate the system through multiple nodes. In other words, scalability makes it easier to meet growing computing workloads, consumer demands and expectations.

Redundancy

In distributed systems, we often come across the concept of redundancy, which allows the system to duplicate critical components, resulting in a significant increase in reliability and resilience. With redundancy, distributed systems can make backups and operate when some computing nodes stop working.

Fault tolerance

Distributed systems are fault tolerant by design. This is because these scaled-out systems typically still function even if one of the nodes goes down. The computational workload is distributed equally among the remaining functional nodes.

Load balancing

Adding a load balancing device or load balancing algorithm to the distributed system makes it easier to prevent system overload. The load balancing algorithm looks for the least busy machine and distributes the workload accordingly.

Challenges of Distributed Systems

What are some of the main challenges of distributed systems? Here are the problems you may encounter when working with these systems.

• Network latency: There may be significant latency in communication. This is because the system is distributed and involves multiple components working together to manage different requests. This can cause system-wide performance issues.

• Distributed coordination: A distributed system needs to coordinate between nodes. Such extensive coordination can be quite challenging given the distribution of the entire system.

• Security: The distributed nature of the system makes it vulnerable to data breaches and external security threats. This is one of the reasons why centralized systems are sometimes preferred over distributed systems.

• Openness: As a distributed system uses components with various data models, standards, protocols and formats, it is quite challenging to achieve effective and continuous communication and data exchange between components without manual intervention. This is especially true if we consider the enormous amount of data processed through the system.

Other challenges you may encounter when using a distributed computing system are heterogeneity, concurrency, transparency, fault handling, and more.

Key Concepts in Distributed Architecture

Here are some of the concepts that are important for the smooth functioning of a distributed architecture:

Nodes and clusters

A node is a single or multithreaded network that has memory and I/O functions controlled by an operating system. A cluster, on the other hand, is a group of two or more nodes or computers that work simultaneously or in parallel to complete the assigned task.

A computer cluster makes it possible to process a large workload by distributing the individual tasks among the cluster nodes, leveraging the combined processing power to increase performance. Cluster computing ensures high availability, load balancing, scalability and high performance.

Data replication and fragmentation

Data replication and sharding are two ways in which data is distributed among multiple nodes. Data replication essentially consists of maintaining a copy of the same data on multiple servers to significantly minimize data loss. Sharding, also called horizontal partitioning, distributes large database management systems into smaller components to facilitate faster data management.

With these data distribution tactics, it is more viable to solve scalability issues, ensure high availability, speed up query response time, create more write bandwidth, and achieve horizontal scalability. Data replication allows for a reduction in latency, increases availability and helps increase the number of servers.

Load balancing

An effective distributed system relies heavily on load balancing. This key concept of distributed architecture facilitates optimal distribution of traffic across nodes in a cluster, resulting in optimized performance without causing any system overhead.

With load balancing, the system can simply eliminate the need to assign a disproportionate amount of work to a single node. Load balancing is enabled by adding a load balancer and a load balancing algorithm that periodically checks the health of each node in the cluster.

If a node fails, the load balancer immediately redirects incoming traffic to the working nodes.

Fault tolerance and failover strategies

As a distributed system requires several components to function properly, it must be highly fault tolerant. After all, multiple components in a system can result in multiple failures, causing significant performance degradation. A fault-tolerant distributed system is readily available, reliable, secure, and maintainable.

Fault tolerance in distributed systems is guaranteed through phases such as fault detection, fault diagnosis, evidence generation, evaluation and recovery. High system availability in a distributed computing architecture is maintained through failover strategies.

Failover clustering, for example, ensures high availability by creating a cluster of servers. This allows the system to function even if a server fails.

Advanced Topics in Distributed Architecture

Now let's look at some more advanced topics in distributed architecture.

CAP Theorem

The CAP theorem or CAP principle is used to explain the capabilities of a distributed system related to replication. Through CAP, system designers resolve potential tradeoffs when designing distributed networks. CAP stands for Consistency, Availability, and Partition Tolerance – three desirable properties of a distributed system.

The CAP theorem states that a distributed system cannot have all three desirable properties at the same time. A shared data system may have only two of these desirable properties.

Service Oriented Architecture (SOA)

Service-oriented architecture (SOA) is a design pattern for distributed systems that allows the extension of services to other applications through the defined service communication protocol. Services in SOA are loosely coupled, location-transparent, and independent and support interoperability,

Service-oriented architecture contains two aspects: functional aspect and quality of service. The functional aspect of SOA involves the transport of the service request, the service description, the actual service, the service communication protocol, the business process, and the service registration.

The quality of service aspect of SOA contains transaction, management, and a policy or set of protocols for identification, authorization, and service extension. SOA is easy to integrate, platform independent, loosely coupled, highly available and reliable, and enables parallel development in a layer-based architecture.

Distributed databases

Primarily used for scale-out, distributed database systems are designed to perform assigned tasks and meet computational requirements without the need to stop or change the database application.

A well-designed distributed database system can effectively make the system more available and fault tolerant while also solving issues related to throughput, latency, scalability, and more. It facilitates location independence, distributed query processing, seamless integration, network linking, transaction processing, and distributed transaction management.

Case studies

Now let's take a look at some case studies related to implementing high-level distributed systems.

Netflix: A real-world example

Netflix is ​​a classic high-level distributed system architecture use case that runs on AWS and Open Connect clouds. Netflix's backend enables new content integration, video processing, and efficient data distribution to servers located around the world. These processes are supported by Amazon Web Services.

Netflix uses an elastic load balancer (two-tier load balancing scheme) to route traffic to front-end services. Netflix's microservices architecture shows how the application runs on a collection of services that power application APIs and web pages. These microservices fulfill data requests coming into the endpoint and can communicate with other microservices to request the data.

Google Distributed Systems

Google's search engine works on a distributed system because it has to support tens of thousands of requests every second. Requests trigger databases that need to read and serve hundreds of megabytes while using billions of processing cycles.

Google starts load balancing the moment an Internet user types a query, looking for the closest active cluster to the user's location. The load balancer transfers requests to the Google web server while GWS creates a response in HTML format.

The entire distributed system is driven by three components: a Googlebot or web crawler, an indexer, and a document server.

Conclusion

A distributed system, regardless of its complexities, is quite popular because it enhances high availability, fault tolerance and scalability. Although there are several significant challenges associated with them, the future of distributed systems and their application is quite promising as the technology advances.

Emerging technologies such as cluster computing, client-server architectures, and grid computing are revolutionizing distributed systems right now. Furthermore, the emergence of pervasive technology, ubiquitous computing, mobile computing, and the use of distributed systems as utilities will certainly change the existing distributed systems architecture.

Common questions

How do distributed systems handle failures?

Distributed systems handle failures through data replication, replacement of nodes using automated scripts or manual intervention, retry policy to reduce recovery time from intermittent failures, use of caches as a substitute for storing data for repeated requests, balancing effective charge and much more.

Why is data consistency a challenge in distributed systems?

Data consistency is a major challenge in distributed systems because issues such as network delays, failures, and others can disrupt data synchronization and updates. Data consistency suffers in distributed systems due to concurrency and conflicts that increase when multiple nodes request modification access to the same data. Additionally, fault tolerance, data replication, and data partitioning can also inhibit data consistency.

Is maintaining distributed systems more expensive?

A distributed system takes full advantage of a scalable architecture that comprises multiple components such as servers, storage, networks, and more. The more parts a system has, the more likely it is to break. Distributed systems are complex in nature, and building and maintaining them can be quite laborious and expensive.

How does load balancing improve the performance of distributed systems?

Load balancing plays a crucial role in ensuring the continuous functioning of distributed system architectures and is particularly vital in the parallel computing domain. In a parallel computing environment, multiple processors or nodes work simultaneously to solve a problem, which requires an effective mechanism to evenly distribute the computational load.

Load balancing, in conjunction with a robust load balancing algorithm, meets this requirement by ensuring that traffic and computational tasks are distributed equally among available nodes. This not only helps prevent any single node from becoming a bottleneck due to system overload, but also optimizes overall performance, leading to more efficient and faster execution of parallel processes.