"A distributed system is a system whose components are located on different networked computers, which communicate and coordinate their actions by passing messages to one another."
An introductory training to the basic principles of distributed systems.
Network Protocols: Understanding network protocols like TCP/IP, HTTP, and UDP is essential when learning about distributed systems. This knowledge is necessary to begin building distributed software that is capable of communicating with a variety of devices over a network.
Distributed Computing Models: This includes architectural models such as client-server architecture, service-oriented architecture, and peer-to-peer architecture. These models provide the foundation for distributed systems and help designers understand the different aspects of distributed systems that are critical to their successful implementation.
Consistency and Replication: These topics deal with ensuring data consistency across nodes in a distributed system, and how to replicate data in order to make it available to users in different parts of the world.
Distributed Algorithms: Distributed algorithms help manage distributed systems and ensure that they remain operational even in the face of failures or other issues.
Security: Security is an important aspect of distributed systems, as sensitive data is often transmitted across different nodes in a network. Understanding security protocols and best practices can help design distributed systems that are secure and resilient to security threats.
Fault Tolerance: Fault tolerance is an important consideration when designing distributed systems, as failures in any one node can have significant impact on the entire system. Understanding how to build fault-tolerant systems that can detect and recover from errors is critical to the success of a distributed system.
Distributed Systems Tools: There are a number of tools available for building and deploying distributed systems, including Docker, Kubernetes, and Apache ZooKeeper. Understanding how these tools work and integrating them into a distributed system can make it more robust and scalable.
Cloud Computing: Cloud computing has made it easier to deploy and scale distributed systems, and offers a number of benefits compared to traditional on-premise deployments. Understanding best practices for deploying distributed systems in the cloud can help designers build systems that are highly scalable and reliable.
Machine Learning in Distributed Systems: Machine learning techniques can be applied to distributed systems to improve performance and enable automated decision-making. Understanding how machine learning can be used in distributed systems can improve their efficiency and effectiveness.
Big Data and Distributed Computing: Distributed systems are often used to process large amounts of data, such as in the case of big data processing. Understanding best practices for managing and processing large amounts of data in a distributed environment can help designers build systems that are optimized for this use case.
Client-server architecture: A distributed system that separates client applications from server applications, with client applications requesting services from server applications.
Peer-to-peer architecture: A distributed system in which every node in the network acts as both a client and a server, allowing nodes to share resources and services.
Hybrid architecture: A distributed system that combines both client-server and peer-to-peer architectures.
Cloud computing: A distributed system that enables users to access a network of remote servers over the internet, providing the ability to store, process, and manage data remotely.
Cluster computing: A distributed system that connects multiple computers together to work as a single system, sharing processing and storage resources.
Grid computing: A distributed system that connects geographically dispersed computing resources, providing access to computing power and storage on-demand.
Mobile computing: A distributed system that enables mobile devices to connect to a network and access resources remotely, allowing for mobility and flexibility in accessing services.
Sensor networks: A distributed system in which multiple sensors are deployed and connected to perform a specific task or set of tasks, such as monitoring a particular environment.
Multi-agent systems: A distributed system in which multiple intelligent agents work together to solve complex problems, such as decision-making or resource allocation.
Internet of Things (IoT): A distributed system in which devices are connected to the internet and can communicate and share data with each other, allowing for efficient monitoring and control of physical devices and systems.
"Distributed computing is a field of computer science that studies distributed systems."
"The components of a distributed system interact with one another in order to achieve a common goal."
"Three significant 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 massively multiplayer online games to peer-to-peer applications."
"A computer program that runs within a distributed system is called a distributed program."
"Distributed programming is the process of writing such programs."
"There are many different types of implementations for the message passing mechanism, including pure HTTP, RPC-like connectors, and message queues."
"Distributed computing also refers to the use of distributed systems to solve computational problems."
"In distributed computing, a problem is divided into many tasks."
"Each task is solved by one or more computers, which communicate with each other via message passing."
"The components of a distributed system... communicate and coordinate their actions by passing messages to one another."
"Maintaining concurrency of components" is a significant challenge in distributed systems.
"Overcoming the lack of a global clock" is a significant challenge in distributed systems.
"Managing the independent failure of components" is a significant challenge in distributed systems.
"When a component of one system fails, the entire system does not fail."
"Examples of distributed systems vary from SOA-based systems to massively multiplayer online games to peer-to-peer applications."
"A computer program that runs within a distributed system is called a distributed program."
"Computers in distributed computing... communicate with each other via message passing."