The Markov-modulated Poisson process (MMPP) cookbook
Performance Evaluation
An EM algorithm for estimation in Markov-modulated Poisson processes
Computational Statistics & Data Analysis
Performance Evaluation of Complex Systems: Techniques and Tools, Performance 2002, Tutorial Lectures
The PSR Methodology: Integrating Hardware and Software Models
Proceedings of the 17th International Conference on Application and Theory of Petri Nets
Performance impacts of autocorrelated flows in multi-tiered systems
Performance Evaluation
Burstiness in multi-tier applications: symptoms, causes, and new models
Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware
Faster Maximum Likelihood Estimation Algorithms for Markovian Arrival Processes
QEST '09 Proceedings of the 2009 Sixth International Conference on the Quantitative Evaluation of Systems
Performance aware open-world software in a 3-layer architecture
Proceedings of the first joint WOSP/SIPEW international conference on Performance engineering
KPC-Toolbox: Best recipes for automatic trace fitting using Markovian Arrival Processes
Performance Evaluation
Performance sensitive self-adaptive service-oriented software using hidden Markov models
Proceedings of the 2nd ACM/SPEC International Conference on Performance engineering
Enhancing a QoS-based self-adaptive framework with energy management capabilities
Proceedings of the joint ACM SIGSOFT conference -- QoSA and ACM SIGSOFT symposium -- ISARCS on Quality of software architectures -- QoSA and architecting critical systems -- ISARCS
Dealing with Burstiness in Multi-Tier Applications: Models and Their Parameterization
IEEE Transactions on Software Engineering
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Software is often embedded in dynamic contexts where it is subjected to high variable, non-stable, and usually bursty workloads. A key requirement for a software system is to be able to self-react to workload changes by adapting its behavior dynamically, to ensure both the correct functionalities and the required performance. Research on fitting variable workload traces into formal models has been carried out using Markovian Modulated Poisson Processes (MMPP). These works concentrate on modeling stable workload states, but accurate modeling of transient times still deserves attention since they are critical moments for the self-adaptation. In this work, we build on research in the area of MMPP trace fitting and we propose a Petri net fine-grained model for highly variable workloads that also accounts for transient times. We analyze differences between models of adaptive software that accurately represent workload state changes and models that do not. We evaluate their performance and availability and compare the results.