- ~300 lines of C code - scalable - fast - fair (starvation-free) In computer science, the Actor model is a mathematical model of concurrent computation that treats "actors" as the universal primitives of concurrent digital computation: in response to a message that it receives, an actor can make local decisions, create more actors, send more messages, and determine how to respond to the next message received. The Actor model originated in 1973.[1] It has been used both as a framework for a theoretical understanding of computation, and as the theoretical basis for several practical implementations of concurrent systems. The relationship of the model to other work is discussed in Indeterminacy in concurrent computation and Actor model and process calculi. HistoryMain article: History of the Actor model
Unlike previous models of computation, the Actor model was inspired by physics including general relativity and quantum mechanics. It was also influenced by the programming languages Lisp, Simula and early versions of Smalltalk, as well as capability-based systems and packet switching. Its development was "motivated by the prospect of highly parallel computing machines consisting of dozens, hundreds or even thousands of independent microprocessors, each with its own local memory and communications processor, communicating via a high-performance communications network."[2] Since that time, the advent of massive concurrency through multi-core computer architectures has rekindled interest in the Actor model. Following Hewitt, Bishop, and Steiger's 1973 publication, Irene Greif developed an operational semantics for the Actors model as part of her doctoral research.[3] Two years later, Henry Baker and Hewitt published a set of axiomatic laws for Actor systems.[4] Other major milestones include William Clinger's dissertation, in 1981, introducing a denotational semantics based on power domains,[2] and Gul Agha's 1985 dissertation which further developed a transition-based semantic model complementary to Clinger's.[5] This resulted in the full development of actor model theory. Major software implementation work was done by Russ Atkinson, Beppe Attardi, Henry Baker, Gerry Barber, Peter Bishop, Peter de Jong, Ken Kahn, Henry Lieberman, Carl Manning, Tom Reinhardt, Richard Steiger, and Dan Theriault, in the Message Passing Semantics Group at Massachusetts Institute of Technology (MIT). Research groups led by Chuck Seitz at California Institute of Technology (Caltech) and Bill Dally at MIT constructed computer architectures that further developed the message passing in the model. See Actor model implementation. Research on the Actor model has been carried out at Caltech Computer Science, Kyoto University Tokoro Laboratory, MCC, MIT Artificial Intelligence Laboratory, SRI, Stanford University, University of Illinois at Urbana-Champaign Open Systems Laboratory, Pierre and Marie Curie University (University of Paris 6), University of Pisa, University of Tokyo Yonezawa Laboratory and elsewhere. Fundamental conceptsThe Actor model adopts the philosophy that everything is an actor. This is similar to the everything is an object philosophy used by some object-oriented programming languages, but differs in that object-oriented software is typically executed sequentially, while the Actor model is inherently concurrent. An actor is a computational entity that, in response to a message it receives, can concurrently:
There is no assumed sequence to the above actions and they could be carried out in parallel. Decoupling the sender from communications sent was a fundamental advance of the Actor model enabling asynchronous communication and control structures as patterns of passing messages.[6] Recipients of messages are identified by address, sometimes called "mailing address". Thus an actor can only communicate with actors whose addresses it has. It can obtain those from a message it receives, or if the address is for an actor it has itself created. The Actor model is characterized by inherent concurrency of computation within and among actors, dynamic creation of actors, inclusion of actor addresses in messages, and interaction only through direct asynchronous message passing with no restriction on message arrival order. Formal systemsOver the years, several different formal systems have been developed which permit reasoning about systems in the Actor model. These include:
There are also formalisms that are not fully faithful to the Actor model in that they do not formalize the guaranteed delivery of messages including the following (See Attempts to relate Actor semantics to algebra and linear logic): Applications
The Actors model can be used as a framework for modelling, understanding, and reasoning about, a wide range of concurrent systems. For example:
Models prior to the Actor modelThe Actor model builds on previous models of computation. Lambda calculusThe lambda calculus of Alonzo Church can be viewed as the earliest message passing programming language (see Hewitt, Bishop, and Steiger 1973; Abelson and Sussman 1985). For example, the lambda expression below implements a tree data structure when supplied with parameters for a leftSubTree and rightSubTree. When such a tree is given a parameter message "getLeft", it returns leftSubTree and likewise when given the message "getRight" it returns rightSubTree. λ(leftSubTree,rightSubTree) However, the semantics of the lambda calculus were expressed using variable substitution in which the values of parameters were substituted into the body of an invoked lambda expression. The substitution model is unsuitable for concurrency because it does not allow the capability of sharing of changing resources. Inspired by the lambda calculus, the interpreter for the programming language Lisp made use of a data structure called an environment so that the values of parameters did not have to be substituted into the body of an invoked lambda expression. This allowed for sharing of the effects of updating shared data structures but did not provide for concurrency. SimulaSimula 67 pioneered using message passing for computation, motivated by discrete event simulation applications. These applications had become large and unmodular in previous simulation languages. At each time step, a large central program would have to go through and update the state of each simulation object that changed depending on the state of whichever simulation objects that it interacted with on that step. Kristen Nygaard and Ole-Johan Dahl developed the idea (first described in an IFIP workshop in 1967) of having methods on each object that would update its own local state based on messages from other objects. In addition they introduced a class structure for objects with inheritance. Their innovations considerably improved the modularity of programs. However, Simula used coroutine control structure instead of true concurrency. SmalltalkAlan Kay was influenced by message passing in the pattern-directed invocation of Planner in developing Smalltalk-71. Hewitt was intrigued by Smalltalk-71 but was put off by the complexity of communication that included invocations with many fields including global, sender, receiver, reply-style, status, reply, operator selector, etc. In 1972 Kay visited MIT and discussed some of his ideas for Smalltalk-72 building on the Logo work of Seymour Papert and the "little person" model of computation used for teaching children to program. However, the message passing of Smalltalk-72 was quite complex. Code in the language was viewed by the interpreter as simply a stream of tokens. As Dan Ingalls later described it:
Thus the message-passing model in Smalltalk-72 was closely tied to a particular machine model and programming-language syntax that did not lend itself to concurrency. Also, although the system was bootstrapped on itself, the language constructs were not formally defined as objects that respond to Eval messages (see discussion below). This led some to believe that a new mathematical model of concurrent computation based on message passing should be simpler than Smalltalk-72. Subsequent versions of the Smalltalk language largely followed the path of using the virtual methods of Simula in the message-passing structure of programs. However Smalltalk-72 made primitives such as integers, floating point numbers, etc. into objects. The authors of Simula had considered making such primitives into objects but refrained largely for efficiency reasons. Java at first used the expedient of having both primitive and object versions of integers, floating point numbers, etc. The C# programming language (and later versions of Java, starting with Java 1.5) adopted the less elegant solution of using boxing and unboxing, a variant of which had been used earlier in some Lisp implementations. The Smalltalk system went on to become very influential, innovating in bitmap displays, personal computing, the class browser interface, and many other ways. For details see Kay's The Early History of Smalltalk[13]. Meanwhile the Actor efforts at MIT remained focused on developing the science and engineering of higher level concurrency. (See the paper by Jean-Pierre Briot for ideas that were developed later on how to incorporate some kinds of Actor concurrency into later versions of Smalltalk.) Petri netsPrior to the development of the Actor model, Petri nets were widely used to model nondeterministic computation. However, they were widely acknowledged to have an important limitation: they modeled control flow but not data flow. Consequently they were not readily composable, thereby limiting their modularity. Hewitt pointed out another difficulty with Petri nets: simultaneous action. I.e., the atomic step of computation in Petri nets is a transition in which tokens simultaneously disappear from the input places of a transition and appear in the output places. The physical basis of using a primitive with this kind of simultaneity seemed questionable to him. Despite these apparent difficulties, Petri nets continue to be a popular approach to modelling concurrency, and are still the subject of active research. Threads, locks, and buffers (channels)Prior to the Actor model, concurrency was defined in low-level machine terms of threads, locks and buffers(channels). It certainly is the case that implementations of the Actor model typically make use of these hardware capabilities. However, there is no reason that the model could not be implemented directly in hardware without exposing any hardware threads and locks. Also, there is no necessary relationship between the number of Actors, threads, and locks that might be involved in a computation. Implementations of the Actor model are free to make use of threads and locks in any way that is compatible with the laws for Actors. Message-passing semanticsThe Actor model is about the semantics of message passing. Unbounded nondeterminism controversyArguably, the first concurrent programs were interrupt handlers. During the course of its normal operation, a computer needed to be able to receive information from outside (characters from a keyboard, packets from a network, etc.). So when the information arrived, execution of the computer was "interrupted" and special code called an interrupt handler was called to put the information in a buffer where it could be subsequently retrieved. In the early 1960s, interrupts began to be used to simulate the concurrent execution of several programs on a single processor.[14] Having concurrency with shared memory gave rise to the problem of concurrency control. Originally, this problem was conceived as being one of mutual exclusion on a single computer. Edsger Dijkstra developed semaphores and later, between 1971 and 1973[15], Tony Hoare[16] and Per Brinch Hansen[17] developed monitors to solve the mutual exclusion problem. However, neither of these solutions provided a programming-language construct that encapsulated access to shared resources. This encapsulation was later accomplished by the serializer construct ([Hewitt and Atkinson 1977, 1979] and [Atkinson 1980]). The first models of computation (e.g. Turing machines, Post productions, the lambda calculus, etc.) were based on mathematics and made use of a global state to represent a computational step (later generalized in [McCarthy and Hayes 1969] and [Dijkstra 1976] see Event orderings versus global state). Each computational step was from one global state of the computation to the next global state. The global state approach was continued in automata theory for finite state machines and push down stack machines, including their nondeterministic versions. Such nondeterministic automata have the property of bounded nondeterminism; that is, if a machine always halts when started in its initial state, then there is a bound on the number of states in which it halts. Edsger Dijkstra further developed the nondeterministic global state approach. Dijkstra's model gave rise to a controversy concerning unbounded nondeterminism. Unbounded nondeterminism (also called unbounded indeterminacy), is a property of concurrency by which the amount of delay in servicing a request can become unbounded as a result of arbitration of contention for shared resources while still guaranteeing that the request will eventually be serviced. Hewitt argued that the Actor model should provide the guarantee of service. In Dijkstra's model, although there could be an unbounded amount of time between the execution of sequential instructions on a computer, a (parallel) program that started out in a well defined state could terminate in only a bounded number of states [Dijkstra 1976]. Consequently, his model could not provide the guarantee of service. Dijkstra argued that it was impossible to implement unbounded nondeterminism. Hewitt argued otherwise: there is no bound that can be placed on how long it takes a computational circuit called an arbiter to settle (see metastability in electronics). Arbiters are used in computers to deal with the circumstance that computer clocks operate asynchronously with input from outside, e.g. keyboard input, disk access, network input, etc. So it could take an unbounded time for a message sent to a computer to be received and in the meantime the computer could traverse an unbounded number of states. The Actor Model features unbounded nondeterminism which was captured in a mathematical model by Will Clinger using domain theory.[2] There is no global state in the Actor model. Direct communication and asynchronyMessages in the Actor model are not necessarily buffered. This was a sharp break with previous approaches to models of concurrent computation. The lack of buffering caused a great deal of misunderstanding at the time of the development of the Actor model and is still a controversial issue. Some researchers argued that the messages are buffered in the "ether" or the "environment". Also, messages in the Actor model are simply sent (like packets in IP); there is no requirement for a synchronous handshake with the recipient. Actor creation plus addresses in messages means variable topologyA natural development of the Actor model was to allow addresses in messages. Influenced by packet switched networks [1961 and 1964], Hewitt proposed the development of a new model of concurrent computation in which communications would not have any required fields at all: they could be empty. Of course, if the sender of a communication desired a recipient to have access to addresses which the recipient did not already have, the address would have to be sent in the communication. A computation might need to send a message to a recipient from which it would later receive a response. The way to do this is to send a communication which has the message along with the address of another actor called the resumption (sometimes also called continuation or stack frame) along with the message. The recipient could then cause a response message to be sent to the resumption. Actor creation plus the inclusion of the addresses of actors in messages means that Actors have a potentially variable topology in their relationship to one another much as the objects in Simula also had a variable topology in their relationship to one another. Inherently concurrentAs opposed to the previous approach based on composing sequential processes, the Actor model was developed as an inherently concurrent model. In the Actor model sequentiality was a special case that derived from concurrent computation as explained in Actor model theory. No requirement on order of message arrivalHewitt argued against adding the requirement that messages must arrive in the order in which they are sent to the Actor. If output message ordering is desired, then it can be modeled by a queue Actor that provides this functionality. Such a queue Actor would queue the messages that arrived so that they could be retrieved in FIFO order. So if an Actor X sent a message M1 to an Actor Y, and later X sent another message M2 to Y, there is no requirement that M1 arrives at Y before M2. In this respect the Actor model mirrors packet switching systems which do not guarantee that packets must be received in the order sent. Not providing the order of delivery guarantee allows packet switching to buffer packets, use multiple paths to send packets, resend damaged packets, and to provide other optimizations. For example, Actors are allowed to pipeline the processing of messages. What this means is that in the course of processing a message M1, an Actor can designate the behavior to be used to process the next message, and then in fact begin processing another message M2 before it has finished processing M1. Just because an Actor is allowed to pipeline the processing of messages does not mean that it must pipeline the processing. Whether a message is pipelined is an engineering tradeoff. How would an external observer know whether the processing of a message by an Actor has been pipelined? There is no ambiguity in the definition of an Actor created by the possibility of pipelining. Of course, it is possible to perform the pipeline optimization incorrectly in some implementations, in which case unexpected behavior may occur. LocalityAnother important characteristic of the Actor model is locality. Locality means that in processing a message an Actor can send messages only to addresses that it receives in the message, addresses that it already had before it received the message and addresses for Actors that it creates while processing the message. (But see Synthesizing Addresses of Actors.) Also locality means that there is no simultaneous change in multiple locations. In this way it differs from some other models of concurrency, e.g., the Petri net model in which tokens are simultaneously removed from multiple locations and placed in other locations. Composing Actor SystemsThe idea of composing Actor systems into larger ones is an important aspect of modularity that was developed in Gul Agha's doctoral dissertation,[5], developed later by Gul Agha, Ian Mason, Scott Smith, and Carolyn Talcott.[7] BehaviorsA key innovation was the introduction of behavior specified as a mathematical function to express what an Actor does when it processes a message including specifying a new behavior to process the next message that arrives. Behaviors provided a mechanism to mathematically model the sharing in concurrency. Behaviors also freed the Actor model from implementation details, e.g., the Smalltalk-72 token stream interpreter. However, it is critical to understand that the efficient implementation of systems described by the Actor model require extensive optimization. See Actor model implementation for details. Modeling other concurrency systemsOther concurrency systems (e.g. process calculi) can be modeled in the Actor model using a two-phase commit protocol.[18] Computational Representation TheoremThere is a Computational Representation Theorem in the Actor model for systems which are closed in the sense that they do not receive communications from outside. The mathematical denotation denoted by a closed system S. is constructed increasingly better approximations from an initial behavior called ⊥S using a behavior-approximating function progressionS to construct a denotation (meaning ) for S as follows [Hewitt 2008; Clinger 1981]:
In this way, S can be mathematically characterized in terms of all its possible behaviors (including those involving unbounded nondeterminism). Although DenoteS is not an implementation of S, it can be used to prove a generalization of the Church-Turing-Rosser-Kleene thesis [Kleene 1943]:
Relationship to mathematical logicThe development of the Actor model has an interesting relationship to mathematical logic. One of the key motivations for its development was to understand and deal with the control structure issues that arose in development of the Planner programming language. Once the Actor model was initially defined, an important challenge was to understand the power of the model relative to Robert Kowalski's thesis that "computation can be subsumed by deduction". Kowalski's thesis turned out to be false for the concurrent computation in the Actor model (see Indeterminacy in concurrent computation). This result is still somewhat controversial and it reversed previous expectations because Kowalski's thesis is true for sequential computation and even some kinds of parallel computation, e.g. the lambda calculus. Nevertheless attempts were made to extend logic programming to concurrent computation. However, Hewitt and Agha [1991] claimed that the resulting systems were not deductive in the following sense: computational steps of the concurrent logic programming systems do not follow deductively from previous steps (see Indeterminacy in concurrent computation). MigrationMigration in the Actor model is the ability of Actors to change locations. E.g., in his dissertation, Aki Yonezawa modeled a post office that customer Actors could enter, change locations within while operating, and exit. An Actor that can migrate can be modeled by having a location Actor that changes when the Actor migrates. However the faithfulness of this modeling is controversial and the subject of research. SecurityThe security of Actors can be protected in the following ways:
Synthesizing addresses of actorsA delicate point in the Actor model is the ability to synthesize the address of an Actor. In some cases security can be used to prevent the synthesis of addresses (see Security). However, if an Actor address is simply a bit string then clearly it can be synthesized although it may be difficult or even infeasible to guess the address of an Actor if the bit strings are long enough. SOAP uses a URL for the address of an endpoint where an Actor can be reached. Since a URL is a character string, it can clearly be synthesized although encryption can make it virtually impossible to guess. Synthesizing the addresses of Actors is usually modeled using mapping. The idea is to use an Actor system to perform the mapping to the actual Actor addresses. For example, on a computer the memory structure of the computer can be modeled as an Actor system that does the mapping. In the case of SOAP addresses, it's modeling the DNS and rest of the URL mapping. Contrast with other models of message-passing concurrencyRobin Milner's initial published work on concurrency[19] was also notable in that it was not based on composing sequential processes. His work differed from the Actor model because it was based on a fixed number of processes of fixed topology communicating numbers and strings using synchronous communication. The original Communicating Sequential Processes model[20] published by Tony Hoare differed from the Actor model because it was based on the parallel composition of a fixed number of sequential processes connected in a fixed topology, and communicating using synchronous message-passing based on process names (see Actor model and process calculi history). Later versions of CSP abandoned communication based on process names in favor of anonymous communication via channels, an approach also used in Milner's work on the Calculus of Communicating Systems and the π-calculus. These early models by Milner and Hoare both had the property of bounded nondeterminism. Modern, theoretical CSP ([Hoare 1985] and [Roscoe 2005]) explicitly provides unbounded nondeterminism. Current importanceForty years after the publication of Moore's Law, hardware development is furthering local and nonlocal massive concurrency. Local concurrency is enabled by new hardware for 64-bit multi-core (Platform 2015 Unveiled at IDF Spring 2005) microprocessors, multi-chip modules, and high performance interconnect. Nonlocal concurrency is being enabled by new hardware for wired and wireless broadband packet switched communications (see Wi-Fi and Ultra wideband). Both local and nonlocal storage capacities are growing exponentially. According to Hewitt [2006], the Actor model faces issues in computer and communications architecture, concurrent programming languages, and Web Services including the following:
Many of the ideas introduced in the Actor model are now also finding application in multi-agent systems for these same reasons [Hewitt 2006b 2007b]. The key difference is that agent systems (in most definitions) impose extra constraints upon the Actors, typically requiring that they make use of commitments and goals. The Actor model is also being applied to client cloud computing [21]. Actor researchersImportant contributions to the semantics of Actors have been made by: Gul Agha, Beppe Attardi, Henry Baker, Will Clinger, Irene Greif, Carl Hewitt, Carl Manning, Ian Mason, Ugo Montanari, Maria Simi, Scott Smith, Carolyn Talcott, Prasanna Thati, and Aki Yonezawa. Important contributions to the implementation of Actors have been made by: Bill Athas, Russ Atkinson, Beppe Attardi, Henry Baker, Gerry Barber, Peter Bishop, Nanette Boden, Jean-Pierre Briot, Bill Dally, Peter de Jong, Jessie Dedecker, Travis Desell, Ken Kahn, Carl Hewitt, Henry Lieberman, Carl Manning, Tom Reinhardt, Chuck Seitz, Richard Steiger, Dan Theriault, Mario Tokoro, Carlos Varela, Darrell Woelk. Programming with ActorsA number of different programming languages employ the Actor model or some variation of it. These languages include: Early Actor programming languagesLater Actor programming languages
Actor libraries and frameworksActor libraries or frameworks have also been implemented to permit actor-style programming in languages that don't have actors built-in. Among these frameworks are:
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In computer science, a non-blocking algorithm ensures that threads competing for a shared resource do not have their execution indefinitely postponed by mutual exclusion. A non-blocking algorithm is lock-free if there is guaranteed system-wide progress; wait-free if there is also guaranteed per-thread progress. Literature up to the turn of the 21st century used "non-blocking" synonymously with lock-free. However, since 2003,[1] the term has been weakened to only prevent progress-blocking interactions with a preemptive scheduler. In modern usage, therefore, an algorithm is non-blocking if the suspension of one or more threads will not stop the potential progress of the remaining threads. They are designed to avoid requiring a critical section. Often, these algorithms allow multiple processes to make progress on a problem without ever blocking each other. For some operations, these algorithms provide an alternative to locking mechanisms.
[edit] MotivationMain article: The problems with locks
The traditional approach to multi-threaded programming is to use locks to synchronize access to shared resources. Synchronization primitives such as mutexes, semaphores, and critical sections are all mechanisms by which a programmer can ensure that certain sections of code do not execute concurrently if doing so would corrupt shared memory structures. If one thread attempts to acquire a lock that is already held by another thread, the thread will block until the lock is free. Blocking a thread is undesirable for many reasons. An obvious reason is that while the thread is blocked, it cannot accomplish anything. If the blocked thread is performing a high-priority or real-time task, it is highly undesirable to halt its progress. Other problems are less obvious. Certain interactions between locks can lead to error conditions such as deadlock, livelock, and priority inversion. Using locks also involves a trade-off between coarse-grained locking, which can significantly reduce opportunities for parallelism, and fine-grained locking, which requires more careful design, increases overhead and is more prone to bugs. Non-blocking algorithms are also safe for use in interrupt handlers: even though the preempted thread cannot be resumed, progress is still possible without it. In contrast, global data structures protected by mutual exclusion cannot safely be accessed in a handler, as the preempted thread may be the one holding the lock. Non-blocking algorithms have the potential to prevent priority inversion, as no thread is forced to wait for a suspended thread to complete. However, as livelock is still possible, threads have to wait when they encounter contention; hence, priority inversion is still possible depending upon the contention management system used. Lock-free algorithms, below, avoid priority inversion. [edit] ImplementationWith few exceptions, non-blocking algorithms use atomic read-modify-write primitives that the hardware must provide, the most notable of which is compare and swap (CAS). Critical sections are almost always implemented using standard interfaces over these primitives. Until recently, all non-blocking algorithms had to be written "natively" with the underlying primitives to achieve acceptable performance. However, the emerging field of software transactional memory promises standard abstractions for writing efficient non-blocking code. Much research has also been done in providing basic data structures such as stacks, queues, sets, and hash tables. These allow programs to easily exchange data between threads asynchronously. Additionally, some data structures are weak enough to be implemented without special atomic primitives. These exceptions include:
[edit] Wait-freedomWait-freedom is the strongest non-blocking guarantee of progress, combining guaranteed system-wide throughput with starvation-freedom. An algorithm is wait-free if every operation has a bound on the number of steps the algorithm will take before the operation completes. It was shown in the 1980s[2] that all algorithms can be implemented wait-free, and many transformations from serial code, called universal constructions, have been demonstrated. However, the resulting performance does not in general match even naïve blocking designs. It has also been shown[3] that the widely-available atomic conditional primitives, CAS and LL/SC, cannot provide starvation-free implementations of many common data structures without memory costs growing linearly in the number of threads. Wait-free algorithms are therefore rare, both in research and in practice. [edit] Lock-freedomLock-freedom allows individual threads to starve but guarantees system-wide throughput. An algorithm is lock-free if it satisfies that when the program threads are run sufficiently long at least one of the threads make progress (for some sensible definition of progress). All wait-free algorithms are lock-free. In general, a lock-free algorithm can run in four phases: completing one's own operation, assisting an obstructing operation, aborting an obstructing operation, and waiting. Completing one's own operation is complicated by the possibility of concurrent assistance and abortion, but is invariably the fastest path to completion. The decision about when to assist, abort or wait when an obstruction is met is the responsibility of a contention manager. This may be very simple (assist higher priority operations, abort lower priority ones), or may be more optimized to achieve better throughput, or lower the latency of prioritized operations. Correct concurrent assistance is typically the most complex part of a lock-free algorithm, and often very costly to execute: not only does the assisting thread slow down, but thanks to the mechanics of shared memory, the thread being assisted will be slowed, too, if it is still running. [edit] Obstruction-freedomObstruction-freedom is possibly the weakest natural non-blocking progress guarantee. An algorithm is obstruction-free if at any point, a single thread executed in isolation (i.e., with all obstructing threads suspended) for a bounded number of steps will complete its operation. All lock-free algorithms are obstruction-free. Obstruction-freedom demands only that any partially-completed operation can be aborted and the changes made rolled back. Dropping concurrent assistance can often result in much simpler algorithms that are easier to validate. Preventing the system from continually live-locking is the task of a contention manager. Obstruction-freedom is also called optimistic concurrency control. Some obstruction-free algorithms use a pair of "consistency markers" in the data structure. Processes reading the data structure first read one consistency marker, then read the relevant data into an internal buffer, then read the other marker, and then compare the markers. The data is consistent if the two markers are identical. Markers may be non-identical when the read is interrupted by another process updating the data structure. In such a case, the process discards the data in the internal buffer and tries again. [edit] See also
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