Impact of Service Function Aging on the Dependability for MEC Service Function Chain

Bai, Jing; Chang, Xiaolin; Machida, Fumio; Jiang, Lili; Han, Zhen; Trivedi, Kishor S.

doi:10.1109/tdsc.2022.3150782

Cited by 20 publications

(15 citation statements)

References 25 publications

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“…Researches have explored analytical modeling approaches to evaluate the availability and reliability in the virtualized system. See [24]- [32] and references therein.…”

Section: Analytical Modelingmentioning

confidence: 99%

“…In this paper, we relax this assumption and allow recovery time and failure time to follow any kind of distribution. There were modeling-based studies [29]- [32] in which all time intervals between events followed any kind of distribution. Machida et al [29] proposed SMP models to compare the effectiveness of time-based rejuvenation, time-based life-extension and hybrid approaches, from the aspects of job completion time and system availability.…”

Section: Analytical Modelingmentioning

confidence: 99%

“…They in [31] presented an SMP model to analyze the vehicle platooning service resilience. They further in [32] analyzed the impact of VNF aging on the dependability on MEC-SFC services. These model in [30]- [32] did not capture the time-dependent behaviors between the subservices and the Host OSes that run them.…”

Section: Analytical Modelingmentioning

confidence: 99%

“…They further in [32] analyzed the impact of VNF aging on the dependability on MEC-SFC services. These model in [30]- [32] did not capture the time-dependent behaviors between the subservices and the Host OSes that run them. These studies [24]- [32] ignored time-dependent behaviors between VNFs, between Host OSes, and between VNFs and Host OSes.…”

Section: Analytical Modelingmentioning

confidence: 99%

“…There were several analytical models to investigate the availability and/or reliability in a virtualized system. They often assumed that all time intervals between events were N 2 > REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) < exponentially distributed [26]- [28] and ignored time-dependent behaviors between VNFs and host OSes [26]- [32]. In addition, many studies only evaluated one of transient availability, steady-state availability and reliability [27]- [30].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Towards UAV-Based MEC Service Chain Resilience Evaluation: A Quantitative Modeling Approach

Bai

Chang

Rodríguez

et al. 2023

IEEE Trans. Veh. Technol.

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Unmanned aerial vehicle (UAV) and network function virtualization (NFV) facilitate the deployment of multiaccess edge computing (MEC). In the UAV-based MEC (UMEC) network, virtualized network function (VNF) can be implemented as a lightweight container running on UMEC host operating system (OS). However, UMEC network is vulnerable to attack, which can result in resource degradation and even UMEC service disruption. Rejuvenation techniques, such as failover technique and live container migration technique, can mitigate the impact of resource degradation but their effectiveness to improve the resilience of UMEC services should be evaluated. This paper presents a quantitative modeling approach based on semi-Markov process to investigate the resilience of a UMEC service chain consisting of any number of VNFs executed in any number of UMEC hosts in terms of availability and reliability. Unlike existing studies, the semi-Markov model constructed in this paper can capture the time-dependent behaviors between VNFs, between host OSes, and between VNFs and host OSes on the condition that the holding times of the recovery and failure events follow any kind of distribution. We perform the sensitivity analysis to identify potential resilience bottlenecks. The results highlight that migration time is the parameter significantly affecting the resilience, which shed the insight on designing the UMEC service chain with high-grade resilience requirements. In addition, we carry out the numerical experiments to reveal that: (i) the type of failure time distribution has a significant effect on the resilience; and (ii) the resilience increases with decreasing number of VNFs, while the availability increases with increasing number of UMEC hosts and the reliability decreases with increasing number of UMEC hosts, which can provide meaningful guidance for the UAV placement optimization in the UMEC network.

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“…Researches have explored analytical modeling approaches to evaluate the availability and reliability in the virtualized system. See [24]- [32] and references therein.…”

Section: Analytical Modelingmentioning

confidence: 99%

Section: Analytical Modelingmentioning

confidence: 99%

Section: Analytical Modelingmentioning

confidence: 99%

Section: Analytical Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Towards UAV-Based MEC Service Chain Resilience Evaluation: A Quantitative Modeling Approach

Bai

Chang

Rodríguez

et al. 2023

IEEE Trans. Veh. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

TTAFPred: Prediction of time to aging failure for software systems based on a two-stream multi-scale features fusion network

Jia,

Yu,

Zhang

et al. 2024

Software Qual J

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Software aging refers to the performance degradation and failure crash phenomena in long-running systems. As a proactive remedy, software rejuvenation can be scheduled timely to mitigate aging effects. Inescapably, how to accurately predict the time to aging failure (TTAF) of software is a prerequisite for implementing effective rejuvenation. However, the characterization of software aging is relatively complicated, leading to the selection of aging indicators case by case, which means that only fitting the variation trend of a single indicator for prediction models to formulate a rejuvenation schedule may be limited. To fill this gap, this paper proposes a novel framework called TTAFPred, which directly constructs the direct mapping relationships between the software aging process considering multiple system indicators and TTAF. Specifically, this framework includes three parts, i.e., data preprocessing, software degradation feature extraction, and TTAF prediction modules. First, the raw data is processed into the input form required by the network. Secondly, a temporal relationship extraction stream integrating bidirectional gated recurrent unit (BiGRU) with attention mechanism is used to extract temporal features from raw inputs. Synchronously, a spatial relationships extraction stream is adopted to extract the spatial features for enhancing the representation ability of degraded features by using the multiscale one-dimensional convolutional neural network (1DCNN) with the residual connection. Then, extracted temporal-spatial features from the two streams are further fused. Finally, two fully-connected layers are constructed to estimate the TTAF. The experiments are performed on two mainstream software systems (OpenStack and Android), and four sets of real run-to-failure data for each software system are collected. The effectiveness of the proposed TTAFPred is verified through extensive experiments with its seven competing models, and the prediction performance can be improved by 9.1%, 8.0%, and 8.0%, respectively, in terms of three evaluation metrics, compared to the best baseline model.

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Quantitative understanding serial-parallel hybrid sfc services: a dependability perspective

Bai

Chang

Machida

et al. 2022

Peer-to-Peer Netw. Appl.

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Container technology, as the key enabler behind microservice architectures, is widely applied in Cloud and Edge Computing. A long and continuous running of operating system (OS) hosting container-based services can encounter software aging that leads to performance deterioration and even causes system failures. OS rejuvenation techniques can mitigate the impact of software aging but the rejuvenation trigger interval needs to be carefully determined to reduce the downtime cost due to rejuvenation. This paper proposes a comprehensive semi-Markovbased approach to quantitatively evaluate the effect of OS rejuvenation on the dependability and the performance of a container-based service. In contrast to the existing studies, we neither restrict the distributions of time intervals of events to be exponential nor assume that backup resources are always available. Through the numerical study, we show the optimal container-migration trigger intervals that can maximize the dependability or minimize the performance of a container-based service.

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Impact of Service Function Aging on the Dependability for MEC Service Function Chain

Cited by 20 publications

References 25 publications

Towards UAV-Based MEC Service Chain Resilience Evaluation: A Quantitative Modeling Approach

Towards UAV-Based MEC Service Chain Resilience Evaluation: A Quantitative Modeling Approach

TTAFPred: Prediction of time to aging failure for software systems based on a two-stream multi-scale features fusion network

Quantitative understanding serial-parallel hybrid sfc services: a dependability perspective

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