Distributed System vs Distributed Computing?

Distributed system and distributed computing are two terms that are often used interchangeably, but they have different meanings and scopes.

A distributed system is a collection of independent entities that communicate and cooperate to achieve a common goal, such as a network of computers, sensors, or agents. A distributed system may or may not involve distributed computing, depending on the nature and complexity of the tasks that the entities perform. For example, a distributed system can be a peer-to-peer network that simply shares files or messages, without performing any computation.

Distributed computing, on the other hand, is a subfield of computer science that studies the design, analysis, and implementation of algorithms and protocols that enable distributed systems to perform computation. Distributed computing focuses on solving problems that require coordination and collaboration among multiple processors, such as load balancing, synchronization, consensus, distributed databases, or distributed machine learning. Distributed computing can be seen as a specific application of distributed system concepts and techniques. For example, a distributed computing system can be a cluster of servers that run a parallel algorithm to process large amounts of data.

It is the process of planning and creating a distributed system that meets the requirements and goals of a given problem or application. Distributed system design involves the following steps:

• Problem definition: The first step is to identify and analyze the problem or application domain, such as the functionality, performance, scalability, reliability, availability, security, or cost of the system.
• System model: The second step is to define and abstract the system model, such as the entities, components, resources, communication, coordination, failure, or fault-tolerance mechanisms of the system.
• Algorithm design: The third step is to design and specify the algorithms and protocols that enable the system to achieve the desired functionality and properties, such as the data structures, messages, message passing, synchronization, consensus, replication, or consistency models of the system.
• Implementation and evaluation: The fourth step is to implement and evaluate the system, such as the programming languages, frameworks, libraries, tools, platforms, testing, debugging, or benchmarking methods of the system.

It is a challenging and complex task that requires a deep understanding of the theoretical and practical aspects of distributed systems, as well as the trade-offs and limitations that arise from the inherent distributed nature of the system. Distributed system design also requires creativity and innovation to devise novel and effective solutions for different problems and applications. Some examples of distributed system design are the design of the Internet, the World Wide Web, cloud computing, peer-to-peer networks, distributed databases, or distributed machine learning systems