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Circular buffering makes a good implementation strategy for a queue that has fixed maximum size. Should a maximum size be adopted for a queue, then a circular buffer is a completely ideal implementation; all queue operations are constant time. However, expanding a circular buffer requires shifting memory, which is comparatively costly.
The small queue is used to filter out one-hit-wonders (objects that are only accessed once in a short time window); the main queue is used to store popular objects and uses reinsertion to keep them in the cache; and the ghost queue is used to catch potentially-popular objects that are evicted from the small queue.
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 ...
In queueing theory, a discipline within the mathematical theory of probability, a Markovian arrival process (MAP or MArP [1]) is a mathematical model for the time between job arrivals to a system. The simplest such process is a Poisson process where the time between each arrival is exponentially distributed. [2] [3]
Minimize the cross-entropy between this distribution and a target distribution to produce a better sample in the next iteration. Reuven Rubinstein developed the method in the context of rare-event simulation , where tiny probabilities must be estimated, for example in network reliability analysis, queueing models, or performance analysis of ...
Q-learning can identify an optimal action-selection policy for any given finite Markov decision process, given infinite exploration time and a partly random policy. [2] "Q" refers to the function that the algorithm computes: the expected reward—that is, the quality—of an action taken in a given state. [3]
There is a close connection between machine learning and compression. A system that predicts the posterior probabilities of a sequence given its entire history can be used for optimal data compression (by using arithmetic coding on the output distribution). Conversely, an optimal compressor can be used for prediction (by finding the symbol that ...
Read and write addresses are initially both at the first memory location and the FIFO queue is empty. In both cases, the read and write addresses end up being equal. To distinguish between the two situations, a simple and robust solution is to add one extra bit for each read and write address which is inverted each time the address wraps. With ...