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Distributed computing is a field of computer science that studies distributed systems, defined as computer systems whose inter-communicating components are located on different networked computers.
In computer science, ACID is a set of properties of database transactions intended to guarantee data validity despite errors, power failures, and other mishaps. In the context of databases, a sequence of database operations that satisfies the ACID properties is called a transaction. For example, a transfer of funds from one bank account to another, even involving multiple changes such as debiting one account and crediting another, is a single transaction.
Self-stabilization is a concept of fault-tolerance in distributed systems. Given any initial state, a self-stabilizing distributed system will end up in a correct state in a finite number of execution steps.
In transaction processing, databases, and computer networking, the two-phase commit protocol is a type of atomic commitment protocol (ACP). It is a distributed algorithm that coordinates all the processes that participate in a distributed atomic transaction on whether to commit or abort the transaction. This protocol achieves its goal even in many cases of temporary system failure, and is thus widely used. However, it is not resilient to all possible failure configurations, and in rare cases, manual intervention is needed to remedy an outcome. To accommodate recovery from failure the protocol's participants use logging of the protocol's states. Log records, which are typically slow to generate but survive failures, are used by the protocol's recovery procedures. Many protocol variants exist that primarily differ in logging strategies and recovery mechanisms. Though usually intended to be used infrequently, recovery procedures compose a substantial portion of the protocol, due to many possible failure scenarios to be considered and supported by the protocol.
A Byzantine fault is a condition of a system, particularly a distributed computing system, where a fault occurs such that different symptoms are presented to different observers, including imperfect information on whether a system component has failed. The term takes its name from an allegory, the "Byzantine generals problem", developed to describe a situation in which, to avoid catastrophic failure of a system, the system's actors must agree on a strategy, but some of these actors are unreliable in such a way as to cause other (good) actors to disagree on the strategy and they may be unaware of the disagreement.
In the field of computer science, an atomic commit is an operation that applies a set of distinct changes as a single operation. If the changes are applied, then the atomic commit is said to have succeeded. If there is a failure before the atomic commit can be completed, then all of the changes completed in the atomic commit are reversed. This ensures that the system is always left in a consistent state. The other key property of isolation comes from their nature as atomic operations. Isolation ensures that only one atomic commit is processed at a time. The most common uses of atomic commits are in database systems and version control systems.
In computer science, software transactional memory (STM) is a concurrency control mechanism analogous to database transactions for controlling access to shared memory in concurrent computing. It is an alternative to lock-based synchronization. STM is a strategy implemented in software, rather than as a hardware component. A transaction in this context occurs when a piece of code executes a series of reads and writes to shared memory. These reads and writes logically occur at a single instant in time; intermediate states are not visible to other (successful) transactions. The idea of providing hardware support for transactions originated in a 1986 paper by Tom Knight. The idea was popularized by Maurice Herlihy and J. Eliot B. Moss. In 1995, Nir Shavit and Dan Touitou extended this idea to software-only transactional memory (STM). Since 2005, STM has been the focus of intense research and support for practical implementations is growing.
High-availability clusters are groups of computers that support server applications that can be reliably utilized with a minimum amount of down-time. They operate by using high availability software to harness redundant computers in groups or clusters that provide continued service when system components fail. Without clustering, if a server running a particular application crashes, the application will be unavailable until the crashed server is fixed. HA clustering remedies this situation by detecting hardware/software faults, and immediately restarting the application on another system without requiring administrative intervention, a process known as failover. As part of this process, clustering software may configure the node before starting the application on it. For example, appropriate file systems may need to be imported and mounted, network hardware may have to be configured, and some supporting applications may need to be running as well.
For transaction processing in computing, the X/Open XA standard is a specification released in 1991 by X/Open for distributed transaction processing (DTP).
Replication in computing involves sharing information so as to ensure consistency between redundant resources, such as software or hardware components, to improve reliability, fault-tolerance, or accessibility.
A distributed algorithm is an algorithm designed to run on computer hardware constructed from interconnected processors. Distributed algorithms are used in different application areas of distributed computing, such as telecommunications, scientific computing, distributed information processing, and real-time process control. Standard problems solved by distributed algorithms include leader election, consensus, distributed search, spanning tree generation, mutual exclusion, and resource allocation.
Commitment ordering (CO) is a class of interoperable serializability techniques in concurrency control of databases, transaction processing, and related applications. It allows optimistic (non-blocking) implementations. With the proliferation of multi-core processors, CO has also been increasingly utilized in concurrent programming, transactional memory, and software transactional memory (STM) to achieve serializability optimistically. CO is also the name of the resulting transaction schedule (history) property, defined in 1988 with the name dynamic atomicity. In a CO compliant schedule, the chronological order of commitment events of transactions is compatible with the precedence order of the respective transactions. CO is a broad special case of conflict serializability and effective means to achieve global serializability across any collection of database systems that possibly use different concurrency control mechanisms.
M. Dale Skeen is an American computer scientist. He specializes in designing and implementing large-scale computing systems, distributed computing and database management systems.
A fundamental problem in distributed computing and multi-agent systems is to achieve overall system reliability in the presence of a number of faulty processes. This often requires coordinating processes to reach consensus, or agree on some data value that is needed during computation. Example applications of consensus include agreeing on what transactions to commit to a database in which order, state machine replication, and atomic broadcasts. Real-world applications often requiring consensus include cloud computing, clock synchronization, PageRank, opinion formation, smart power grids, state estimation, control of UAVs, load balancing, blockchain, and others.
Paxos is a family of protocols for solving consensus in a network of unreliable or fallible processors. Consensus is the process of agreeing on one result among a group of participants. This problem becomes difficult when the participants or their communications may experience failures.
Fault Tolerant Messaging in the context of computer systems and networks, refers to a design approach and set of techniques aimed at ensuring reliable and continuous communication between components or nodes even in the presence of errors or failures. This concept is especially critical in distributed systems, where components may be geographically dispersed and interconnected through networks, making them susceptible to various potential points of failure.
Keith Marzullo is the inventor of Marzullo's algorithm, which is part of the basis of the Network Time Protocol and the Windows Time Service. On August 1, 2016 he became the Dean of the University of Maryland College of Information Studies after serving as the Director of the NITRD National Coordination Office. Prior to this he was a Professor in the Department of Computer Science and Engineering at University of California, San Diego. In 2011 he was inducted as a Fellow of the Association for Computing Machinery.
A quorum is the minimum number of votes that a distributed transaction has to obtain in order to be allowed to perform an operation in a distributed system. A quorum-based technique is implemented to enforce consistent operation in a distributed system.
Gbcast is a reliable multicast protocol that provides ordered, fault-tolerant (all-or-none) message delivery in a group of receivers within a network of machines that experience crash failure. The protocol is capable of solving Consensus in a network of unreliable processors, and can be used to implement state machine replication. Gbcast can be used in a standalone manner, or can support the virtual synchrony execution model, in which case Gbcast is normally used for group membership management while other, faster, protocols are often favored for routine communication tasks.
Avalanche is a decentralized, open-source proof-of-stake blockchain with smart contract functionality. AVAX is the native cryptocurrency of the platform.
References
- ↑ Skeen, Dale (February 1982). A Quorum-Based Commit Protocol (Technical report). Department of Computer Science, Cornell University.
- ↑ Keidar, Idit; Danny Dolev (December 1998). "Increasing the Resilience of Distributed and Replicated Database Systems". Journal of Computer and System Sciences. 57 (3): 309–324. doi: 10.1006/jcss.1998.1566 .