Performability evaluation of emergency call center

Lima, Marcus A. de Q.V.; Maciel, Paulo; Silva, Bruno; Guimarães, Almir Pereira

doi:10.1016/j.peva.2014.07.023

Cited by 19 publications

(7 citation statements)

References 11 publications

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“…Authors in [9] proposed models to evaluate an emergency call center, with focus on evaluating power system impacts in call center service downtime. The call center architecture modeled is composed of energy, networking, voice, and customer service structures.…”

Section: Related Workmentioning

confidence: 99%

How to Improve Cloud Services Availability? Investigating the Impact of Power and It Subsystems Failures

Rosendo

Leoni

Gomes

et al. 2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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The cloud data center is a complex system composed of power, cooling, and IT subsystems. The power subsystem is crucial to feed the IT equipment. Power disruptions may result in service unavailability. This paper analyzes the impact of the power subsystem failures on IT services regarding different architecture configurations based on TIA-942 standard such as non-redundant, redundant, concurrently maintainable, and fault tolerant. We model both subsystems, power and IT, through Stochastic Petri Net (SPN). The availability results show that a fault tolerant power and IT configuration reduces the downtime from 54.1 to 34.5 hours/year when compared to a nonredundant architecture. The sensibility analysis results show that the failure and repair rates of the server component in a fault tolerant system present the highest impact on overall data center availability.

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Section: Related Workmentioning

confidence: 99%

How to Improve Cloud Services Availability? Investigating the Impact of Power and It Subsystems Failures

Rosendo

Leoni

Gomes

et al. 2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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“…de QV Lima et al evaluate the performability of an emergency call center that hosts police and fire department services. The performance is measured with respect to throughput and number of attendants, whereas downtime and number of nines are metrics to infer availability.…”

Section: Related Workmentioning

confidence: 99%

Measuring the impact of data center failures on a cloud‐based emergency medical call system

Gomes

Santos

Rosendo

et al. 2019

Concurrency and Computation

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Summary Emergency call services are expected to be highly available in order to minimize the loss of urgent calls and, as a consequence, minimize loss of life due to lack of timely medical response. This service availability depends heavily on the cloud data center on which it is hosted. However, availability information alone cannot provide sufficient understanding of how failures impact the service and users' perception. In this paper, we evaluate the impact of failures on an emergency call system, considering service‐level metrics such as the number of affected calls per failure and the time an emergency service takes until it recovers from a failure. We analyze a real data set from an emergency call center for a large Brazilian city. From stochastic models that represent a cloud data center, we evaluate different data center architectures to observe the impact of failures on the emergency call service. Results show that changing data center's architecture in order to improve availability from two to three nines cannot decrease the average number of affected calls per failure. On the other hand, it can decrease the probability to affect a considerable number of calls at the same time.

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“…Feldman, Mandelbaum et al (2008) develop a staffing model to maintain time-stable performance when arrival rates are time-varying. Lima, Maciel et al (2014) develop a general call center evaluation approach and examine the issue of system downtime. The Erlang C model also assumes that the service time follows an exponential distribution.…”

Section: Queuing Models and The Associated Literaturementioning

confidence: 99%

Evaluating the Performance of the Erlang Models for Call Centers

Robbins¹

2019

IJAST

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In this paper we evaluate two queuing models used to analyze call centers; the Erlang C and Erlang A models. The Erlang C is a simple model that ignores caller abandonment and is the most commonly used model. The Erlang A model allows for abandonment, but performance measures are more difficult to calculate. We compare the theoretical performance predictions of these models to a steady state simulation model where many of the simplifying assumptions used in standard analytical models are relaxed. Our findings support the assertion that the Erlang A model is more accurate, but we find that in contrast to the Erlang C model, Erlang A tends to be optimistically biased. Our findings indicate that neither model clearly dominates the other in all situations and that care must be taken to select the correct model based on call center conditions and the intended purpose of the model.

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Performability evaluation of emergency call center

Cited by 19 publications

References 11 publications

How to Improve Cloud Services Availability? Investigating the Impact of Power and It Subsystems Failures

How to Improve Cloud Services Availability? Investigating the Impact of Power and It Subsystems Failures

Measuring the impact of data center failures on a cloud‐based emergency medical call system

Evaluating the Performance of the Erlang Models for Call Centers

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