Distributed processing in computer networks, the concept of distributed processing in networks is consistent with Moore's Law, which assumes that the number of transistors in a single integrated circuit (IC) doubles every two years. In distributed processing in computer networks, many computer servers are linked together across a network to enable large workloads that make use of all resources available.
What has distributed processing in computer networks?
Distributed processing in computer networks is a technique for linking multiple computer servers together across a network in a group, in order to share data and coordinate processing power. Such a pool is referred to as a “distributed system.” Distributed computing offers advantages in scalability, through a hierarchical structure performance through parallelism, flexibility by redundancy, and cost-effectiveness through the use of low-cost commodity hardware.
Distributed processing can also be used as an approximate synonym for parallel processing, where programs work faster with multiple processors, through the strategy of including more than one processor on a microprocessor chip, and device users can also group multiple computers together, in order to implement parallel processing With applications known as distributed processing software.
Distributed processing in computer networks is used to refer to a variety of computer systems that use more than one computer or processor to run an application. This includes parallel processing in which one computer uses more than one central processing unit to execute programs. Distributed processing in computer networks often refers to local area networks (LANs) designed.
So that one program can run at a time in different locations, where most distributed processing in computer networks contains sophisticated programs that detect idle CPUs on the network and compile programs to take advantage of them, as distributed computing has become very common in the design of databases and applications, and this is The reason they are especially valuable for scaling is that this extra overhead can be handled as data volumes increase simply by adding more devices to the system.
General information about distributed processing in computer networks
Distributed processing in computer networks makes all the computers in the group work together as if it were one computer. Although there is some complexity, there are benefits, including scalability, as it is easy to measure distributed computing groups, through a “scalable architecture” where Higher loads can be handled simply by adding new hardware in exchange for replacing existing hardware.
and performance through parallelism where each computer in the cluster simultaneously deals with a subset of an overarching task, the cluster can achieve high levels of performance through an approach, flexibility Distributed computing clusters typically replicate data across all computer servers, To ensure there is no single point of failure, and if a computer fails, copies of the data on that computer are stored elsewhere so that no data is lost. Cost-effectiveness: Distributed computing typically takes advantage of low-cost, commodity hardware, making Initial deployments to group expansions very economical.
Advantages of distributed processing in computer networks
1. Easy to replace remote computers
Microsoft Windows Server has a feature called failover aggregation, which helps remove faulty computers, and if any computer on the network fails or becomes damaged by some means, that computer is automatically replaced by other computers.
2. Optimum processing
Managing data on the server online solves the slow processing on the PC, we can do additional tasks too, it consumes processor power but the online computer is for one type of processing and is likely to increase the processing powers, as only the database server can process The database queries and the file server stores files so data handling is improved.
3. Easy to expand
Suppose your company needs to process more data than expected, you can easily connect more computers to the distributed network.
4. Parallel processing
Adding and removing computers from the network cannot disturb the flow of data, as all data from different computers are processed in parallel, parallel processing means that data is updated simultaneously from all nodes.
5. Local data synchronization
All computers on the network can have local storage of important data, as to suppose that there are different office branches interconnected with each other, and all the sub computers are interconnected with the main branch office. All office branch computers contain a local copy of the data, and here Office users edit and update the data and then upload it to the main server, so the data is synchronized and made available to all computers, where working locally with the data is easy and fast, and when the user thinks that His work is complete, at the end of the day he can sync that data with the master server.
6. Backup data
The data can be backed up from any computer connected to the network, so the user can back up the data at a different time and work with that data locally, then upload the data to the server.
7. Data recovery
If some data such as the database is lost in any computer, it can be restored by another connected computer such as the master database server.
8. Better performance
Overall company performance improves and data is filtered and processed more quickly in a distributed environment.
Disadvantages of distributed processing in computer networks
1. Complexity
Computers attached to distributed processing in computer networks are difficult to troubleshoot, design, and manage.
2. Data synchronization planning is difficult
It is difficult to develop the correct data synchronization procedure, and sometimes the data is updated in the wrong order, so administrators need to maintain focus on it before creating a distributed network.
3. Data security
If the unauthorized computer is connected to a distributed network, it can affect the performance of the other computer and data can be lost as well.
Examples of distributed processing in computer networks
- Hosting a website on the electronic server.
- Online photo editing tools.
- Air ticket reservation system.
- Processing of User Data by the mobile carriers “Dropbox”, “Google drive”, “MSN drive”, and “Google Images”.
- Satellite generation report.
- weather forecasting system.
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