Reliability and Availability Analysis in Practice

Trivedi, Kishor S.; Bobbio, Andrea

doi:10.1007/978-3-030-55732-4_22

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…It is useful to highlight that, the assumptions on the considered repair rates stem from the fact that mean-timeto-repairs (namely 1/µ) are approximately constant over time. It is especially valid for the software infrastructures (as in our case) where repair actions are meant to be reboot actions, as also confirmed by credited literature [41]- [43].…”

Section: Cnf Availability Modelsupporting

confidence: 72%

A Latency-Driven Availability Assessment for Multi-Tenant Service Chains

Simone¹,

Mauro

Natella

et al. 2023

IEEE Trans. Serv. Comput.

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Nowadays, most telecommunication services adhere to the Service Function Chain (SFC) paradigm, where network functions are implemented via software. In particular, container virtualization is becoming a popular approach to deploy network functions and to enable resource slicing among several tenants. The resulting infrastructure is a complex system composed by a huge amount of containers implementing different SFC functionalities, along with different tenants sharing the same chain. The complexity of such a scenario lead us to evaluate two critical metrics: the steady-state availability (the probability that a system is functioning in long runs) and the latency (the time between a service request and the pertinent response). Consequently, we propose a latency-driven availability assessment for multi-tenant service chains implemented via Containerized Network Functions (CNFs). We adopt a multi-state system to model single CNFs and the queueing formalism to characterize the service latency. To efficiently compute the availability, we develop a modified version of the Multidimensional Universal Generating Function (MUGF) technique. Finally, we solve an optimization problem to minimize the SFC cost under an availability constraint. As a relevant example of SFC, we consider a containerized version of IP Multimedia Subsystem, whose parameters have been estimated through fault injection techniques and load tests.

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Section: Cnf Availability Modelsupporting

confidence: 72%

A Latency-Driven Availability Assessment for Multi-Tenant Service Chains

Simone¹,

Mauro

Natella

et al. 2023

IEEE Trans. Serv. Comput.

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“…The Erlang distribution 28 is a special case of the hypoexponential distribution 31 (introduced in the next section) when all k states have an identical rate λ . In this case, the distribution is referred to as an Erlang distribution of order k .…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…The hypoexponential distribution 31 is a combination of k ≥ 2 sequential states which are exponentially distributed, but with individual rates ( λ 1 , λ 2 , . . . , λ k ) . A hypoexponential distribution of order k = 2 , where λ 1 ≠ λ 2 and λ 1 , λ 2 > 0 is denoted as Hypo ( λ 1 , λ 2 ) .…”

Section: Theoretical Backgroundmentioning

confidence: 99%

SynBPS: a parametric simulation framework for the generation of event-log data

Riess

2024

SIMULATION

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In the pursuit of ecological validity, current business process simulation methods are calibrated to data from existing processes. This is important for realistic what-if analysis in the context of these processes. However, this is not always the “right tool for the job.” To test hypotheses in the area of predictive process monitoring, it can be more helpful to simulate event-log data from a theoretical process, where all aspects can be manipulated. One example is when assessing the influence of process complexity or variability on the performance of a new prediction method. In this case, the ability to include control variables and systematically change process characteristics is a key to fully understanding their influence. Calibrating a simulation model from observed data alone can in these cases be limiting. This paper proposes a simulation framework, Synthetic Business Process Simulation (SynBPS), a Python library for the generation of event-log data from synthetic processes. Aspects such as process complexity, stability, trace distribution, duration distribution, and case arrivals can be fully controlled by the user. The overall architecture is described in detail, and a demonstration of the framework is presented.

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