"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."
The use of multiple computers connected via a network to solve a single computing task, distributing the workload and improving performance.
Distributed systems architecture: This is the design and organization of a system composed of multiple nodes that communicate and coordinate to achieve a common goal.
Parallel processing: This involves breaking down a large task into small parts that can be executed concurrently across multiple processors.
Message passing: This involves sending messages between nodes of a distributed system to coordinate tasks, exchange information, and synchronize activities.
Load balancing: This refers to the practice of distributing workloads across multiple nodes in a distributed system to optimize resource utilization and improve overall system performance.
Fault tolerance: This is the ability of a system to continue working in the event of failures or errors, usually through redundancy and other measures.
Distributed algorithms: These are algorithms designed to run on distributed systems, often taking into account their unique characteristics and challenges.
Consensus protocols: These are methods for achieving agreement or coordination among multiple nodes in a distributed system, despite potential failures or discrepancies.
Grid computing: This is a type of distributed computing where geographically dispersed resources are used to work together as a single system, often used in scientific and research settings.
Cloud computing: This involves the use of remote servers to store, manage, and process data or software applications, often using virtualization and other technologies.
Big data processing: This involves distributed processing and storage of large volumes of data that cannot be easily handled by a single system or server.
Machine learning: This involves the use of algorithms and statistical models to enable computers to learn and make predictions based on data, often using distributed computing to enable scalability and high performance.
High-performance computing: This involves the use of powerful computing systems and technologies to enable faster processing and analysis of data, often used in scientific and research settings.
Grid computing: Grid computing is a distributed computing architecture that involves a group of geographically dispersed and loosely coupled computers working together to perform a task. Grid computing is typically used for scientific and engineering applications that require large amounts of computational power.
Cluster computing: Cluster computing is a type of distributed computing architecture that involves a group of computers that are tightly coupled and work together to perform tasks. The computers in a cluster are typically located in the same physical location and are interconnected by a high-speed network.
Cloud computing: Cloud computing is a type of distributed computing architecture that involves the use of remote servers to store, manage, and process data. Cloud computing offers scalable and on-demand computing resources that can be accessed over the internet.
Peer-to-peer computing: Peer-to-peer computing is a type of distributed computing architecture that involves a network of computers that communicate and share resources directly with each other, without the need for a centralized server.
Volunteer computing: Volunteer computing is a type of distributed computing architecture that involves the use of idle computing resources from volunteers to perform computational tasks.
Fog computing: Fog computing is a type of distributed computing architecture that involves the use of edge devices such as routers, switches, and gateways to handle computing tasks and data storage, rather than relying solely on centralized servers.
Heterogeneous computing: Heterogeneous computing is a type of distributed computing architecture that involves the use of different types of computers with varying processing capabilities to work together to perform a task.
Mobile computing: Mobile computing is a type of distributed computing architecture that involves the use of mobile devices such as smartphones and tablets to perform computational tasks.
Quantum computing: Quantum computing is a type of distributed computing architecture that involves the use of quantum bits, also known as qubits, to perform computational tasks. Quantum computing is still in its early stages of development and is expected to revolutionize how computing tasks are performed in the future.
"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."