1. Scalability: Distributed systems offer the ability to scale horizontally by adding more nodes to the system. This allows for increased processing power, storage capacity, and network bandwidth to handle growing demands.
2. Fault Tolerance: Distributed systems are designed to handle failures and ensure system reliability. By replicating data and services across multiple nodes, the system can continue to operate even if some nodes fail. Fault tolerance mechanisms, such as redundancy, replication, and fault detection and recovery techniques, are employed to ensure system availability.
3. Consistency and Replication: Replication is commonly used in distributed systems to improve performance and fault tolerance. However, maintaining consistency among replicated data copies across multiple nodes can be a challenge. Consistency models, such as strong consistency, eventual consistency, and eventual linearizability, are used to define the level of consistency in distributed systems.
4. Communication and Message Passing: Communication between nodes in a distributed system is typically achieved through message passing. Nodes exchange messages to share information, request resources, or coordinate their actions. Communication protocols and middleware are used to facilitate reliable and efficient message passing.
5. Distributed File Systems: Distributed file systems enable the storage and access of files across multiple nodes in a distributed system. Examples include Hadoop Distributed File System (HDFS) and Google File System (GFS). These systems provide fault tolerance, high throughput, and parallel processing capabilities.
6. Distributed Computing: Distributed systems allow for distributed computing, where computational tasks are divided among multiple nodes for parallel execution. Distributed computing frameworks, such as Apache Spark and Hadoop MapReduce, enable efficient data processing across distributed nodes.
7. Consensus and Coordination: Distributed systems often require mechanisms for achieving consensus among nodes to make collective decisions. Consensus protocols, such as the Raft algorithm or the Paxos algorithm, are used to ensure agreement among distributed nodes.
8. Distributed Database Systems: Distributed databases store data across multiple nodes to provide scalability and fault tolerance. They employ techniques such as sharding, replication, and data partitioning to distribute and manage data across the system.
9. Security and Trust: Distributed systems face challenges related to security and trust. Authentication, encryption, access control, and secure communication protocols are implemented to protect data and ensure the integrity and confidentiality of interactions among nodes.
10. Monitoring and Management: Distributed systems require monitoring and management tools to track the performance, health, and status of individual nodes and the overall system. These tools help in identifying bottlenecks, diagnosing issues, and optimizing the system's performance.

Distributed systems are utilized in various domains, including cloud computing, big data processing, content delivery networks (CDNs), Internet of Things (IoT), and blockchain networks. They provide the foundation for building scalable, resilient, and highly available systems that can handle complex computational tasks and support the needs of modern applications and services.