Distributed computing in the context of Interprocess communication

A search engine is a software system that provides hyperlinks to web pages, and other relevant information on the Web in response to a user's query. The user enters a query in a web browser or a mobile app, and the search results are typically presented as a list of hyperlinks accompanied by textual summaries and images. Users also have the option of limiting a search to specific types of results, such as images, videos, or news.

For a search provider, its engine is part of a distributed computing system that can encompass many data centers throughout the world. The speed and accuracy of an engine's response to a query are based on a complex system of indexing that is continuously updated by automated web crawlers. This can include data mining the files and databases stored on web servers, although some content is not accessible to crawlers.

A smart device is an electronic device, generally connected to other devices or networks via different wireless protocols (such as Bluetooth, Zigbee, near-field communication, Wi-Fi, NearLink, Li-Fi, or 5G) that can operate to some extent interactively and autonomously. Several notable types of smart devices are smartphones, smart speakers, smart cars, smart cards, smart thermostats, smart doorbells, smart locks, smart refrigerators, phablets and tablets, smartwatches, smart bands, smart keychains, smart glasses, smart TV, and many others. The term can also refer to a device that exhibits some properties of ubiquitous computing, including—although not necessarily—machine learning.

Smart devices can be designed to support a variety of form factors, a range of properties pertaining to ubiquitous computing and to be used in three main system environments: physical world, human-centered environments, and distributed computing environments. Smart homes indicate the presence of sensors and some detection devices, appliances, and a database to control them.

In computer networking, a proxy server is a server application that acts as an intermediary between a client requesting a resource and the server then providing that resource.

Instead of connecting directly to a server that can fulfill a request for a resource, such as a file or web page, the client directs the request to the proxy server, which evaluates the request and performs the required network transactions. This serves as a method to simplify or control the complexity of the request, or provide additional benefits such as load balancing, privacy, or security. Proxies were devised to add structure and encapsulation to distributed systems. A proxy server thus functions on behalf of the client when requesting service, potentially masking the true origin of the request to the resource server.

Computational science, also known as scientific computing, technical computing or scientific computation (SC), is a division of science, and more specifically the computer sciences, which uses advanced computing capabilities to understand and solve complex physical problems in science. While this typically extends into computational specializations, this field of study includes:

• Algorithms (numerical and non-numerical): mathematical models, computational models, and computer simulations developed to solve sciences (e.g, physical, biological, and social), engineering, and humanities problems
• Computer hardware that develops and optimizes the advanced system hardware, firmware, networking, and data management components needed to solve computationally demanding problems
• The computing infrastructure that supports both the science and engineering problem solving and the developmental computer and information science

In practical use, it is typically the application of computer simulation and other forms of computation from numerical analysis and theoretical computer science to solve problems in various scientific disciplines. The field is different from theory and laboratory experiments, which are the traditional forms of science and engineering. The scientific computing approach is to gain understanding through the analysis of mathematical models implemented on computers. Scientists and engineers develop computer programs and application software that model systems being studied and run these programs with various sets of input parameters. The essence of computational science is the application of numerical algorithms and computational mathematics. In some cases, these models require massive amounts of calculations (usually floating-point) and are often executed on supercomputers or distributed computing platforms.

Uploading refers to transmitting data from one computer system to another through means of a network. Common methods of uploading include: uploading via web browsers, FTP clients, and terminals (SCP/SFTP). Uploading can be used in the context of (potentially many) clients that send files to a central server. While uploading can also be defined in the context of sending files between distributed clients, such as with a peer-to-peer (P2P) file-sharing protocol like BitTorrent, the term file sharing is more often used in this case. Moving files within a computer system, as opposed to over a network, is called file copying.

Uploading directly contrasts with downloading, where data is received over a network. In the case of users uploading files over the internet, uploading is often slower than downloading as many internet service providers (ISPs) offer asymmetric connections, which offer more network bandwidth for downloading than uploading.

In computer science, a consistency model specifies a contract between the programmer and a system, wherein the system guarantees that if the programmer follows the rules for operations on memory, memory will be consistent and the results of reading, writing, or updating memory will be predictable. Consistency models are used in distributed systems like distributed shared memory systems or distributed data stores (such as filesystems, databases, optimistic replication systems or web caching). Consistency is different from coherence, which occurs in systems that are cached or cache-less, and is consistency of data with respect to all processors. Coherence deals with maintaining a global order in which writes to a single location or single variable are seen by all processors. Consistency deals with the ordering of operations to multiple locations with respect to all processors.

High level languages, such as C++ and Java, maintain the consistency contract by translating memory operations into low-level operations in a way that preserves memory semantics, reordering some memory instructions, and encapsulating required synchronization with library calls such as pthread_mutex_lock().

In computer science, interprocess communication (IPC) is the sharing of data between running processes in a computer system, or between multiple such systems. Mechanisms for IPC may be provided by an operating system. Applications which use IPC are often categorized as clients and servers, where the client requests data and the server responds to client requests. Many applications are both clients and servers, as commonly seen in distributed computing.

IPC is very important to the design process for microkernels and nanokernels, which reduce the number of functionalities provided by the kernel. Those functionalities are then obtained by communicating with servers via IPC, leading to a large increase in communication when compared to a regular monolithic kernel. IPC interfaces generally encompass variable analytic framework structures. These processes ensure compatibility between the multi-vector protocols upon which IPC models rely.

In computer science, concurrency refers to the ability of a system to execute multiple tasks through simultaneous execution or time-sharing (context switching), sharing resources and managing interactions. Concurrency improves responsiveness, throughput, and scalability in modern computing, including:

• Operating systems and embedded systems
• Distributed systems, parallel computing, and high-performance computing
• Database systems, web applications, and cloud computing

Leslie Barry Lamport (born February 7, 1941) is an American computer scientist and mathematician. Lamport is best known for his seminal work in distributed systems, and as the initial developer of the document preparation system LaTeX and the author of its first manual.

Lamport was the winner of the 2013 Turing Award for imposing clear, well-defined coherence on the seemingly chaotic behavior of distributed computing systems, in which several autonomous computers communicate with each other by passing messages. He devised important algorithms and developed formal modeling and verification protocols that improve the quality of real distributed systems. These contributions have resulted in improved correctness, performance, and reliability of computer systems.

In concurrent computing, deadlock is any situation in which no member of some group of entities can proceed because each waits for another member, including itself, to take action, such as sending a message or, more commonly, releasing a lock. Deadlocks are a common problem in multiprocessing systems, parallel computing, and distributed systems, because in these contexts systems often use software or hardware locks to arbitrate shared resources and implement process synchronization.

In an operating system, a deadlock occurs when a process or thread enters a waiting state because a requested system resource is held by another waiting process, which in turn is waiting for another resource held by another waiting process. If a process remains indefinitely unable to change its state because resources requested by it are being used by another process that itself is waiting, then the system is said to be in a deadlock.

Grid computing is the use of widely distributed computer resources to reach a common goal. A computing grid can be thought of as a distributed system with non-interactive workloads that involve many files. Grid computing is distinguished from conventional high-performance computing systems such as cluster computing in that grid computers have each node set to perform a different task/application. Grid computers also tend to be more heterogeneous and geographically dispersed (thus not physically coupled) than cluster computers. Although a single grid can be dedicated to a particular application, commonly a grid is used for a variety of purposes. Grids are often constructed with general-purpose grid middleware software libraries. Grid sizes can be quite large.

Grids are a form of distributed computing composed of many networked loosely coupled computers acting together to perform large tasks. For certain applications, distributed or grid computing can be seen as a special type of parallel computing that relies on complete computers (with onboard CPUs, storage, power supplies, network interfaces, etc.) connected to a computer network (private or public) by a conventional network interface, such as Ethernet. This is in contrast to the traditional notion of a supercomputer, which has many processors connected by a local high-speed computer bus. This technology has been applied to computationally intensive scientific, mathematical, and academic problems through volunteer computing, and it is used in commercial enterprises for such diverse applications as drug discovery, economic forecasting, seismic analysis, and back office data processing in support for e-commerce and Web services.

Replication in computing refers to maintaining multiple copies of data, processes, or resources to ensure consistency across redundant components. This fundamental technique spans databases, file systems, and distributed systems, serving to improve availability, fault-tolerance, accessibility, and performance. Through replication, systems can continue operating when components fail (failover), serve requests from geographically distributed locations, and balance load across multiple machines. The challenge lies in maintaining consistency between replicas while managing the fundamental tradeoffs between data consistency, system availability, and network partition tolerance – constraints known as the CAP theorem.

Access time is the time delay or latency between a request to an electronic system, and the access being initiated or the requested data returned.

In computer and software systems, it is the time interval between the point where an instruction control unit initiates a call to retrieve data or a request to store data, and the point at which delivery of the data is completed or the storage is started. Note that in distributed software systems or other systems with stochastic processes, access time or latency should be measured at the 99th percentile.

In computer science and data management, a commit is a behavior that marks the end of a transaction and provides Atomicity, Consistency, Isolation, and Durability (ACID) in transactions. The submission records are stored in the submission log for recovery and consistency in case of failure. In terms of transactions, the opposite of committing is giving up tentative changes to the transaction, which is rolled back.

Due to the rise of distributed computing and the need to ensure data consistency across multiple systems, commit protocols have been evolving since their emergence in the 1970s. The main developments include the Two-Phase Commit (2PC) first proposed by Jim Gray, which is the fundamental core of distributed transaction management. Subsequently, the Three-phase Commit (3PC), Hypothesis Commit (PC), Hypothesis Abort (PA), and Optimistic Commit protocols gradually emerged, solving the problems of blocking and fault recovery.

In computer science, a data buffer (or just buffer) is a region of memory used to store data temporarily while it is being moved from one place to another. Typically, the data is stored in a buffer as it is retrieved from an input device (such as a microphone) or just before it is sent to an output device (such as speakers); however, a buffer may be used when data is moved between processes within a computer, comparable to buffers in telecommunication. Buffers can be implemented in a fixed memory location in hardware or by using a virtual data buffer in software that points at a location in the physical memory.

In all cases, the data stored in a data buffer is stored on a physical storage medium. The majority of buffers are implemented in software, which typically use RAM to store temporary data because of its much faster access time when compared with hard disk drives. Buffers are typically used when there is a difference between the rate at which data is received and the rate at which it can be processed, or in the case that these rates are variable, for example in a printer spooler or in online video streaming. In a distributed computing environment, data buffers are often implemented in the form of burst buffers, which provides distributed buffering services.