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Large-scale distributed systems are the core software infrastructure underlying cloud computing. These systems consist of tens of thousands of networked computers working together to provide unprecedented performance and fault-tolerance.
As with so many complex things, computer system complexity means different things to different people. In this article, we identify five aspects of distributed com- puting system complexity: task-structure complexity, unpre- dictability, size complexity, chaotic complexity, and algorith- mic complexity.
Network Issues: Distributed systems rely on network communication, so network stability and bandwidth problems can occur. Network delays and packet loss can impact system performance. 3. Fault Management: Handling node or service failures and implementing recovery strategies is essential.
Designing and implementing reliable and high-performance distributed systems can be challenging due to various factors, including network latency, network partitioning, load balancing, and fault tolerance.
Issues related to data synchronization, replication, and version control can arise. 2. Network Issues: Distributed systems rely on network communication, so network stability and bandwidth problems can occur. Network delays and packet loss can impact system performance.
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These are explained as following below. Method failure : In this type of failure, the distributed system is generally halted and unable to perform the execution. System failure : Secondary storage device failure : Communication medium failure : Failure Models: Timing failure: Response failure: Omission failure:
In the distributed systems replication is mainly used to provide fault tolerance. Two replication protocols have been used in distributed systems: Active and Passive replication [10]. In active replication each client request is processed by all the servers. It is also known as state machine replication [4].
Then as now, challenges with distributed systems involved latency, scaling, understanding networking APIs, marshalling and unmarshalling data, and the complexity of algorithms such as Paxos. As the systems quickly grew larger and more distributed, what had been theoretical edge cases turned into regular occurrences.

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