What Is Distributed Computing?
Distributed computing is the method of making multiple computers work together to solve a common problem. It makes a computer network appear as a powerful single computer that provides large-scale resources to deal with complex challenges.
For example, distributed computing can encrypt large volumes of data; solve physics and chemical equations with many variables; and render high-quality, three-dimensional video animation. Distributed systems, distributed programming, and distributed algorithms are some other terms that all refer to distributed computing.
What are the advantages of distributed computing?
Distributed systems bring many advantages over single system computing. The following are some of them.
Distributed systems can grow with your workload and requirements. You can add new nodes, that is, more computing devices, to the distributed computing network when they are needed.
Your distributed computing system will not crash if one of the computers goes down. The design shows fault tolerance because it can continue to operate even if individual computers fail.
Computers in a distributed system share information and duplicate data between them, but the system automatically manages data consistency across all the different computers. Thus, you get the benefit of fault tolerance without compromising data consistency.
Distributed computing systems provide logical separation between the user and the physical devices. You can interact with the system as if it is a single computer without worrying about the setup and configuration of individual machines. You can have different hardware, middleware, software, and operating systems that work together to make your system function smoothly.
Distributed systems offer faster performance with optimum resource use of the underlying hardware. As a result, you can manage any workload without worrying about system failure due to volume spikes or underuse of expensive hardware.
What are some distributed computing use cases?
Distributed computing is everywhere today. Mobile and web applications are examples of distributed computing because several machines work together in the backend for the application to give you the correct information. However, when distributed systems are scaled up, they can solve more complex challenges. Let’s explore some ways in which different industries use high-performing distributed applications.
Healthcare and life sciences
Healthcare and life sciences use distributed computing to model and simulate complex life science data. Image analysis, medical drug research, and gene structure analysis all become faster with distributed systems. These are some examples:
- Accelerate structure-based drug design by visualizing molecular models in three dimensions.
- Reduce genomic data processing times to get early insights into cancer, cystic fibrosis, and Alzheimer’s.
- Develop intelligent systems that help doctors diagnose patients by processing a large volume of complex images like MRIs, X-rays, and CT scans.
Engineers can simulate complex physics and mechanics concepts on distributed systems. They use this research to improve product design, build complex structures, and design faster vehicles. Here are some examples:
- Computation fluid dynamics research studies the behavior of liquids and implements those concepts in aircraft design and car racing.
- Computer-aided engineering requires compute-intensive simulation tools to test new plant engineering, electronics, and consumer goods.
Financial services firms use distributed systems to perform high-speed economic simulations that assess portfolio risks, predict market movements, and support financial decision-making. They can create web applications that use the power of distributed systems to do the following:
- Deliver low-cost, personalized premiums
- Use distributed databases to securely support a very high volume of financial transactions.
- Authenticate users and protect customers from fraud
Energy and environment
Energy companies need to analyze large volumes of data to improve operations and transition to sustainable and climate-friendly solutions. They use distributed systems to analyze high-volume data streams from a vast network of sensors and other intelligent devices. These are some tasks they might do:
- Streaming and consolidating seismic data for the structural design of power plants
- Real-time oil well monitoring for proactive risk management
What are the types of distributed computing architecture?
In distributed computing, you design applications that can run on several computers instead of on just one computer. You achieve this by designing the software so that different computers perform different functions and communicate to develop the final solution. There are four main types of distributed architecture.
Client-server is the most common method of software organization on a distributed system. The functions are separated into two categories: clients and servers.
Clients have limited information and processing ability. Instead, they make requests to the servers, which manage most of the data and other resources. You can make requests to the client, and it communicates with the server on your behalf.
Server computers synchronize and manage access to resources. They respond to client requests with data or status information. Typically, one server can handle requests from several machines.
Benefits and limitations
Client-server architecture gives the benefits of security and ease of ongoing management. You have only to focus on securing the server computers. Similarly, any changes to the database systems require changes to the server only.
The limitation of client-server architecture is that servers can cause communication bottlenecks, especially when several machines make requests simultaneously.
In three-tier distributed systems, client machines remain as the first tier you access. Server machines, on the other hand, are further divided into two categories:
Application servers act as the middle tier for communication. They contain the application logic or the core functions that you design the distributed system for.
Database servers act as the third tier to store and manage the data. They are responsible for data retrieval and data consistency.
By dividing server responsibility, three-tier distributed systems reduce communication bottlenecks and improve distributed computing performance.
N-tier models include several different client-server systems communicating with each other to solve the same problem. Most modern distributed systems use an n-tier architecture with different enterprise applications working together as one system behind the scenes.
Peer-to-peer distributed systems assign equal responsibilities to all networked computers. There is no separation between client and server computers, and any computer can perform all responsibilities. Peer-to-peer architecture has become popular for content sharing, file streaming, and blockchain networks.
How does distributed computing work?
Distributed computing works by computers passing messages to each other within the distributed systems architecture. Communication protocols or rules create a dependency between the components of the distributed system. This interdependence is called coupling, and there are two main types of coupling.
In loose coupling, components are weakly connected so that changes to one component do not affect the other. For example, client and server computers can be loosely coupled by time. Messages from the client are added to a server queue, and the client can continue to perform other functions until the server responds to its message.
High-performing distributed systems often use tight coupling. Fast local area networks typically connect several computers, which creates a cluster. In cluster computing, each computer is set to perform the same task. Central control systems, called clustering middleware, control and schedule the tasks and coordinate communication between the different computers.
What is parallel computing?
Parallel computing is a type of computing in which one computer or multiple computers in a network carry out many calculations or processes simultaneously. Although the terms parallel computing and distributed computing are often used interchangeably, they have some differences.
Parallel computing vs. distributed computing
Parallel computing is a particularly tightly coupled form of distributed computing. In parallel processing, all processors have access to shared memory for exchanging information between them. On the other hand, in distributed processing, each processor has private memory (distributed memory). Processors use message passing to exchange information.
What is grid computing?
In grid computing, geographically distributed computer networks work together to perform common tasks. One feature of distributed grids is that you can form them from computing resources that belong to multiple individuals or organizations.
Grid computing vs. distributed computing
Grid computing is highly scaled distributed computing that emphasizes performance and coordination between several networks. Internally, each grid acts like a tightly coupled computing system. However, externally, grids are more loosely coupled. Each grid network performs individual functions and communicates the results to other grids.
What is AWS High-Performance Computing?
With AWS High-Performance Computing (HPC), you can accelerate innovation with fast networking and virtually unlimited distributed computing infrastructure. For example, you can use these services:
- Amazon Elastic Cloud Compute (EC2) to support virtually any workload with secure, resizable compute capacity.
- AWS Batch to scale hundreds of thousands of computing jobs across AWS compute services.
- AWS ParallelCluster to quickly build HPC compute environments and HPC clusters.
Get started with distributed computing on AWS by creating a free account today.