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A computer operating system (OS) is one of the most important aspects of a computer. It is responsible for the underlying task management and orchestration of the computer's programs. A distributed operating system is a system that spreads the load over multiple computer hardware servers. This type of OS provides better performance and availability because it is distributed across multiple components.
Most operating systems are available in distributed versions. Some examples include UNIX®, Linux®, and the Windows® operating system. When the OS is distributed, it must be installed on multiple servers, which requires special configuration and management processes. This typically requires system engineers to manage the project.
A distributed OS is configured as a cluster of servers that share memory and tasks. These servers act in unison and provide more power than a single large computer server. This typically generates better performance because the load is distributed over multiple servers.
Grid computing is good example of distributed computing. This system uses computers connected to the Internet to complete complex tasks that require extensive processing power. Using a distributed model makes use of idle computer capacity because it shares multiple servers.
Computer capacity is known as the maximum available processing power of a computer system. It is typically calculated based on the available memory and computer processing units of the hardware platform. A distributed system provides additional capacity because it includes multiple servers.
There are specific algorithms used for a distributed operating system to handle task management. This arrangement is designed to process individual tasks on multiple servers based on priority and expected processing time. These algorithms vary in complexity, but are designed to make the best use of the available processing power from the shared servers.
A round-robin algorithm is an example of a simple algorithm that is used in a distributed operating system. This technique distributes incoming computer tasks to multiple servers based on a simple counting algorithm. Each task is assigned a specific number that corresponds to a specific server within the chain of available servers.
Some distributed operating system models monitor the available capacity of each server within the chain. This typically provides better performance than simple round-robin techniques because the server load is based on the actual available processing power. Advanced algorithms are more common in sophisticated multiprocessing operating systems.
From what I understand, these are a pain to set up but can be very, very effective. Take an Internet service provider, for example. Is it more cost effective to buy larger and larger servers or to just add to capacity in a distributed environment?
The answer is obvious.
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