Semi-Markov Model of Processing Requests Reliability and Availability in Mobile Cloud Computing Systems

Martyna, Jerzy

doi:10.1007/978-3-030-76773-0_26

Cited by 1 publication

(2 citation statements)

References 13 publications

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“…The analyzed references considering Markov modelling for assessing the availability and cybersecurity of cloud and IoT systems can be divided into three groups: the references related to assessing availability and reliability [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27], the references related to assessing cybersecurity [28][29][30][31][32][33][34][35][36][37][38][39], and references related to assessing both availability and cybersecurity as attributes of dependability of cloud and IoT systems [40][41][42][43].…”

Section: State Of the Artmentioning

confidence: 99%

“…A feature of the proposed methodology is the combined assessment of reliability and cybersecurity, which has been demonstrated for IoT smart factory infrastructure. To predict the future availability and reliability of processing requests in a mobile cloud computing (MCC) system, a semi-Markov processes-based multi-state model was defined in [21]. Utilizing this model, decision-making on increasing the speed, disk space of the system, etc.…”

Section: State Of the Artmentioning

confidence: 99%

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Combining Markov and Semi-Markov Modelling for Assessing Availability and Cybersecurity of Cloud and IoT Systems

et al. 2022

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This paper suggests a strategy (C5) for assessing cloud and IoT system (CIS) dependability, availability, and cybersecurity based on the continuous collection, comparison, choice, and combination of Markov and semi-Markov models (MMs and SMMs). It proposes the systematic building of an adequate and accurate model to evaluate CISs considering (1) continuous evolution of the model(s) together with systems induced by changes in the CIS or physical and cyber environment parameters; (2) the necessity of collecting data on faults, failures, vulnerabilities, cyber-attacks, privacy violations, and patches to obtain actual data for assessment; (3) renewing the model set based on analysis of CIS operation; (4) the possibility of choice and utilizing “off-the-shelf” models with understandable techniques for their development to assure improved accuracy of assessment; (5) renewing the models during application of CIS by time, component or mixed combining, taking into consideration different operation and maintenance events. The results obtained were algorithms for data collection and analysis, choice, and combining appropriate MM and SMMs and their different types, such as multi-fragmental and multiphase models, considering changing failure rates, cyber-attack parameters, periodical maintenance, etc. To provide and verify the approach, several private and public clouds and IoT systems were researched and discussed in the context of C5 and proposed algorithms.

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