Wide area traffic: the failure of Poisson modeling
IEEE/ACM Transactions on Networking (TON)
The MMAP[K]/PH[K]/1 queues with a last-come-first-served preemptive service discipline
Queueing Systems: Theory and Applications
The Versatility of MMAP[K] and the MMAP[K]/G[K]/1 Queue
Queueing Systems: Theory and Applications
The BMAP/G/1 QUEUE: A Tutorial
Performance Evaluation of Computer and Communication Systems, Joint Tutorial Papers of Performance '93 and Sigmetrics '93
Performance Evaluation of Complex Systems: Techniques and Tools, Performance 2002, Tutorial Lectures
PhFit: A General Phase-Type Fitting Tool
TOOLS '02 Proceedings of the 12th International Conference on Computer Performance Evaluation, Modelling Techniques and Tools
Fitting world-wide web request traces with the EM-algorithm
Performance Evaluation - Special issue: Internet performance and control of network systems
Effects of failure correlation on software in operation
PRDC '00 Proceedings of the 2000 Pacific Rim International Symposium on Dependable Computing
Modeling IP traffic using the batch Markovian arrival process
Performance Evaluation - Modelling techniques and tools for computer performance evaluation
Modeling IP traffic: joint characterization of packet arrivals and packet sizes using BMAPs
Computer Networks: The International Journal of Computer and Telecommunications Networking
QEST '05 Proceedings of the Second International Conference on the Quantitative Evaluation of Systems
Closed form solutions for mapping general distributions to quasi-minimal PH distributions
Performance Evaluation - Modelling techniques and tools for computer performance evaluation
A Novel Approach for Phase-Type Fitting with the EM Algorithm
IEEE Transactions on Dependable and Secure Computing
Long-Range Dependence at the Disk Drive Level
QEST '06 Proceedings of the 3rd international conference on the Quantitative Evaluation of Systems
Efficient phase-type fitting with aggregated traffic traces
Performance Evaluation
A minimal representation of Markov arrival processes and a moments matching method
Performance Evaluation
Queues in DOCSIS cable modem networks
Computers and Operations Research
A traffic based decomposition of two-class queueing networks with priority service
Computer Networks: The International Journal of Computer and Telecommunications Networking
Trace data characterization and fitting for Markov modeling
Performance Evaluation
A Heuristic Approach for Fitting MAPs to Moments and Joint Moments
QEST '09 Proceedings of the 2009 Sixth International Conference on the Quantitative Evaluation of Systems
Explicit inverse characterizations of acyclic MAPs of second order
EPEW'06 Proceedings of the Third European conference on Formal Methods and Stochastic Models for Performance Evaluation
Performance evaluation of a kitting process
ASMTA'11 Proceedings of the 18th international conference on Analytical and stochastic modeling techniques and applications
MMAP|M|N queueing system with impatient heterogeneous customers as a model of a contact center
Computers and Operations Research
Departure process analysis of the multi-type MMAP[K]/PH[K]/1 FCFS queue
Performance Evaluation
The workload-dependent MAP/PH/1 queue with infinite/finite workload capacity
Performance Evaluation
Computers and Operations Research
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Markovian arrival processes are a powerful class of stochastic processes to represent stochastic workloads that include autocorrelation in performance or dependability modeling. However, fitting the parameters of a Markovian arrival process to given measurement data is non-trivial and most known methods focus on a single class case, where all events are of the same type and only the sequence of interarrival times is of interest. In this paper, we propose a method to fit data to a multi-class Markovian arrival process, where arrivals can be partitioned into a finite set of classes. This allows us to use a Markovian arrival process to represent workloads where interarrival times are correlated across customer classes and to achieve models of greater accuracy. The fitting approach performs in several consecutive steps and applies a single non-linear optimization step and several non-negative least squares computations.