Distributed Computing

What is distributed computing?

The critical difference between distributed computing and cloud computing lies in the location and management of computing resources. In distributed computing, the resources are often local but interconnected through a network to share workloads. In contrast, cloud computing centralizes all resources—hardware, software, and infrastructure—which are provided and managed by a cloud service provider and delivered over the Internet or a cloud network. This allows for on-demand scalability, resource elasticity, and pay-as-you-go models, making cloud computing highly flexible and cost-effective.

Vertical scaling, also known as scaling up, is the process of enhancing the processing power of an existing system without increasing its physical footprint. This involves adding more RAM, increasing CPU speed, or expanding storage capacity within an existing computer or server.

Horizontal scaling, also known as scaling out, involves boosting computing power by expanding the overall infrastructure footprint. This is achieved by adding additional servers or node clusters to a network, thereby distributing workloads across multiple systems. This approach is commonly used in cloud environments and distributed systems to achieve high availability, fault tolerance, and scalability.

A variety of complex architectures are used in distributed computing, based on resources, and required tasks. Because distributed computing is scalable, there can be nuanced differences in large networks, but many will fall into one of the following basic categories:

Client-server

A client-server network consists of a central server, handling processing and storage duties, with clients functioning as terminals that send and receive messages to/from the server. The most common example of a client-server network is email.

Three-tier

In this type of distributed computing network, the first tier is called the presentation tier and is the interface through which an end user sends and receives messages. The middle section is called the application tier, middle tier, or logic tier and controls the application’s functionality. The final tier is the database servers or file shares, which house the required data used to complete tasks. The most common example of a three-tier system is an e-commerce site. Note that there is some degree of crossover between “multitier” or “n-tier” distributed systems and “three-tier” systems, since multitier and n-tier systems are variations on three-tier architecture. The main distinction here is that each of the tiers is in a separate physical space and responsible for specialized, localized tasks within the larger computing architecture.

Peer-to-peer

In this distribution architecture model, peers are equally privileged and equally powerful to handle workloads. In this environment, peers, users, or machines are called nodes and do not require centralized coordination between the parties. The most famous usage of peer-to-peer networking was the file-sharing application, Napster, which launched in 1999 as a means of sharing music between Internet-capable listeners.

Distributed computing has a wide variety of benefits, which explains why just about every modern computing process above simple calculations utilizes a distributed computing architecture.

Scalability

For starters, the network can not only be architected to meet the needs of the tasks, but it can also scale dynamically in real time to onboard nodes to meet demands, and then return them to inactive states when the demands reduce.

Reliability

Because of the nature of a distributed system, natural redundancies are inherent in the architecture. As nodes might jump in to support computing tasks, those same nodes can contribute to a zero-downtime process by covering for a failed or malfunctioning node. In an e-commerce scenario, a healthy server could step in and complete the sale if a shopping cart server failed mid-transaction.

Speed

The single most important benefit of distributed computing systems is the speed at which complex tasks are handled. Where otherwise a server might get bogged down in heavy traffic, a distributed system can scale in real time to handle the same tasks with more computing power. Essentially, the distributed system can be architected to make workloads standardized by matching needs with resources dynamically.

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