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Queue Theory Applied to Distributed Network System, Approximate analytical…
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Approximate analytical formulas are useful not only predicting performance and scalability of system, but also for analyzing the performance bottlenecks based on real measurements
- Markov(M) => easier to implement ;
- General(G) => is not characterized by any single probability, complex to implement;
- Deterministic => characterized by various constants
- number of arrivals => Poisson probability distribution;
- interarrival time => Exponential probability distribution;
- service time => Exponential probability distribution.
M/M/∞/∞ FIFO
- arrival rate(Markovian/Memoryless distr.)
- service rate(Markovian/Memoryless distr.)
- queue capacity
- population
- M/M/1 - single queue
- M/M/m - Multiple Parallel Queues(multiple nodes)
- q(M/M/m) - Single Queue Multi Server(multiple processors)
- Open - has external input and output(request doesn't circulate in the system)
- Closed - requests circulate continuously, never leaving the system
- With feedback - multiple visits of requests to resources
- Without feedback - requests visit resources only once
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:star: Poisson Distribution
:star: Exponential Distribution
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