Distributed Computing: Unraveling the Power of Parallelism & Cloud Systems

Did you know you’re participating in a distributed computing system simply by reading this article? That’s right, the massive network that is the internet is an example of distributed computing, as is every application that uses the world wide web.

Distributed computing involves getting multiple computing units to work together to solve a single problem or perform a single task. Distributing the workload across multiple interconnected units leads to the formation of a super-computer that has the resources to deal with virtually any challenge.

Without this approach, large-scale operations involving computers would be all but impossible. Sure, this has significant implications for scientific research and big data processing. But it also hits close to home for an average internet user. No distributed computing means no massively multiplayer online games, e-commerce websites, or social media networks.

With all this in mind, let’s look at this valuable system in more detail and discuss its advantages, disadvantages, and applications.

Basics of Distributed Computing

Distributed computing aims to make an entire computer network operate as a single unit. Read on to find out how this is possible.

Components of a Distributed System

A distributed system has three primary components: nodes, communication channels, and middleware.

Nodes

The entire premise of distributed computing is breaking down one giant task into several smaller subtasks. And who deals with these subtasks? The answer is nodes. Each node (independent computing unit within a network) gets a subtask.

Communication Channels

For nodes to work together, they must be able to communicate. That’s where communication channels come into play.

Middleware

Middleware is the middleman between the underlying infrastructure of a distributed computing system and its applications. Both sides benefit from it, as it facilitates their communication and coordination.

Types of Distributed Systems

Coordinating the essential components of a distributed computing system in different ways results in different distributed system types.

Client-Server Systems

A client-server system consists of two endpoints: clients and servers. Clients are there to make requests. Armed with all the necessary data, servers are the ones that respond to these requests.

The internet, as a whole, is a client-server system. If you’d like a more specific example, think of how streaming platforms (Netflix, Disney+, Max) operate.

Peer-to-Peer Systems

Peer-to-peer systems take a more democratic approach than their client-server counterparts: they allocate equal responsibilities to each unit in the network. So, no unit holds all the power and each unit can act as a server or a client.

Content sharing through clients like BitTorrent, file streaming through apps like Popcorn Time, and blockchain networks like Bitcoin are some well-known examples of peer-to-peer systems.

Grid Computing

Coordinate a grid of geographically distributed resources (computers, networks, servers, etc.) that work together to complete a common task, and you get grid computing.

Whether belonging to multiple organizations or far away from each other, nothing will stop these resources from acting as a uniform computing system.

Cloud Computing

In cloud computing, centralized data centers store data that organizations can access on demand. These centers might be centralized, but each has a different function. That’s where the distributed system in cloud computing comes into play.

Thanks to the role of distributed computing in cloud computing, there’s no limit to the number of resources that can be shared and accessed.

Key Concepts in Distributed Computing

For a distributed computing system to operate efficiently, it must have specific qualities.

Scalability

If workload growth is an option, scalability is a necessity. Amp up the demand in a distributed computing system, and it responds by adding more nodes and consuming more resources.

Fault Tolerance

In a distributed computing system, nodes must rely on each other to complete the task at hand. But what happens if there’s a faulty node? Will the entire system crash? Fortunately, it won’t, and it has fault tolerance to thank.

Instead of crashing, a distributed computing system responds to a faulty node by switching to its working copy and continuing to operate as if nothing happened.

Consistency

A distributed computing system will go through many ups and downs. But through them all, it must uphold consistency across all nodes. Without consistency, a unified and up-to-date system is simply not possible.

Concurrency

Concurrency refers to the ability of a distributed computing system to execute numerous processes simultaneously.

Parallel computing and distributed computing have this quality in common, leading many to mix up these two models. But there’s a key difference between parallel and distributed computing in this regard. With the former, multiple processors or cores of a single computing unit perform the simultaneous processes. As for distributed computing, it relies on interconnected nodes that only act as a single unit for the same task.

Despite their differences, both parallel and distributed computing systems have a common enemy to concurrency: deadlocks (blocking of two or more processes). When a deadlock occurs, concurrency goes out of the window.

Advantages of Distributed Computing

There are numerous reasons why using distributed computing is a good idea:

• Improved performance. Access to multiple resources means performing at peak capacity, regardless of the workload.
• Resource sharing. Sharing resources between several workstations is your one-way ticket to efficiently completing computation tasks.
• Increased reliability and availability. Unlike single-system computing, distributed computing has no single point of failure. This means welcoming reliability, consistency, and availability and bidding farewell to hardware vulnerabilities and software failures.
• Scalability and flexibility. When it comes to distributed computing, there’s no such thing as too much workload. The system will simply add new nodes and carry on. No centralized system can match this level of scalability and flexibility.
• Cost-effectiveness. Delegating a task to several lower-end computing units is much more cost-effective than purchasing a single high-end unit.

Challenges in Distributed Computing

Although this offers numerous advantages, it’s not always smooth sailing with distributed systems. All involved parties are still trying to address the following challenges:

• Network latency and bandwidth limitations. Not all distributed systems can handle a massive amount of data on time. Even the slightest delay (latency) can affect the system’s overall performance. The same goes for bandwidth limitations (the amount of data that can be transmitted simultaneously).
• Security and privacy concerns. While sharing resources has numerous benefits, it also has a significant flaw: data security. If a system as open as a distributed computing system doesn’t prioritize security and privacy, it will be plagued by data breaches and similar cybersecurity threats.
• Data consistency and synchronization. A distributed computing system derives all its power from its numerous nodes. But coordinating all these nodes (various hardware, software, and network configurations) is no easy task. That’s why issues with data consistency and synchronization (concurrency) come as no surprise.
• System complexity and management. The bigger the distributed computing system, the more challenging it gets to manage it efficiently. It calls for more knowledge, skills, and money.
• Interoperability and standardization. Due to the heterogeneous nature of a distributed computing system, maintaining interoperability and standardization between the nodes is challenging, to say the least.

Applications of Distributed Computing

Nowadays, distributed computing is everywhere. Take a look at some of its most common applications, and you’ll know exactly what we mean:

• Scientific research and simulations. Distributed computing systems model and simulate complex scientific data in fields like healthcare and life sciences. (For example, accelerating patient diagnosis with the help of a large volume of complex images (CT scans, X-rays, and MRIs).
• Big data processing and analytics. Big data sets call for ample storage, memory, and computational power. And that’s precisely what distributed computing brings to the table.
• Content delivery networks. Delivering content on a global scale (social media, websites, e-commerce stores, etc.) is only possible with distributed computing.
• Online gaming and virtual environments. Are you fond of massively multiplayer online games (MMOs) and virtual reality (VR) avatars? Well, you have distributed computing to thank for them.
• Internet of Things (IoT) and smart devices. At its very core, IoT is a distributed system. It relies on a mixture of physical access points and internet services to transform any devices into smart devices that can communicate with each other.

Future Trends in Distributed Computing

Given the flexibility and usability of distributed computing, data scientists and programmers are constantly trying to advance this revolutionary technology. Check out some of the most promising trends in distributed computing:

• Edge computing and fog computing – Overcoming latency challenges
• Serverless computing and Function-as-a-Service (FaaS) – Providing only the necessary amount of service on demand
• Blockchain – Connecting computing resources of cryptocurrency miners worldwide
• Artificial intelligence and machine learning – Improving the speed and accuracy in training models and processing data
• Quantum computing and distributed systems – Scaling up quantum computers

Distributed Computing Is Paving the Way Forward

The ability to scale up computational processes opens up a world of possibilities for data scientists, programmers, and entrepreneurs worldwide. That’s why current challenges and obstacles to distributed computing aren’t particularly worrisome. With a little more research, the trustworthiness of distributed systems won’t be questioned anymore.