Concurrency (computer science) in the context of Process (computing)

In computing, time-sharing is the concurrent sharing of a computing resource among many tasks or users by giving each task or user a small slice of processing time. This quick switch between tasks or users gives the illusion of simultaneous execution. It enables multi-tasking by a single user or enables multiple-user sessions.

Developed during the 1960s, its emergence as the prominent model of computing in the 1970s represented a major technological shift in the history of computing. By allowing many users to interact concurrently with a single computer, time-sharing dramatically lowered the cost of providing computing capability, made it possible for individuals and organizations to use a computer without owning one, and promoted the interactive use of computers and the development of new interactive applications.

In computer programming, a thread pool is a software design pattern for achieving concurrency of execution in a computer program. Often also called a replicated workers or worker-crew model, a thread pool maintains multiple threads waiting for tasks to be allocated for concurrent execution by the supervising program. By maintaining a pool of threads, the model increases performance and avoids latency in execution due to frequent creation and destruction of threads for short-lived tasks. Another good property - the ability to limit system load, when we use fewer threads than available. The number of available threads is tuned to the computing resources available to the program, such as a parallel task queue after completion of execution.

Concurrent computing is a form of computing in which several computations are executed concurrently—during overlapping time periods—instead of sequentially—with one completing before the next starts.

This is a property of a system—whether a program, computer, or a network—where there is a separate execution point or "thread of control" for each process. A concurrent system is one where a computation can advance without waiting for all other computations to complete.

Distributed computing is a field of computer science that studies distributed systems, defined as computer systems whose inter-communicating components are located on different networked computers.

The components of a distributed system communicate and coordinate their actions by passing messages to one another in order to achieve a common goal. Three challenges of distributed systems are: maintaining concurrency of components, overcoming the lack of a global clock, and managing the independent failure of components. When a component of one system fails, the entire system does not fail. Examples of distributed systems vary from SOA-based systems to microservices to massively multiplayer online games to peer-to-peer applications. Distributed systems cost more than monolithic architectures, primarily due to increased needs for additional hardware, servers, gateways, firewalls, new subnets, proxies, and so on. Distributed systems can also suffer from fallacies of distributed computing. Conversely, a well-designed distributed system is more scalable, more durable, more changeable, and more fine-tuned than a monolithic application deployed on a single machine. According to Marc Brooker: "a system is scalable in the range where marginal cost of additional workload is nearly constant." Serverless technologies fit this definition but the total cost of ownership, and not just the infra cost must be considered.