Performance Analysis of an Edge Computing SaaS System for Mobile Users

Bonadio, Alessio; Chiti, Francesco; Fantacci, R.

doi:10.1109/tvt.2019.2957938

Cited by 24 publications

(18 citation statements)

References 29 publications

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“…In addition, to investigate the performance of cooperation strategies among edge servers, which could increase the service acceptance ratio for IoV applications, the authors in [15] proposed a Queueing theory based analytical model to investigate the performance of load sharing schemes in edge computing, with the aim of obtaining the metrics of packet blocking probability and average waiting time. Similarly, the authors in [16] developed a Markov multi-server queuing model to evaluate the performance of edge computing systems with limited computation capabilities. The minimum number of processors was derived based on the proposed model to satisfy certain QoS requirements.…”

Section: Related Workmentioning

confidence: 99%

“…It can capture the essential features of the system operation, gain significant insights, and offer a cost-effective and versatile tool to theoretically identify the performance bottleneck with different design alternatives and under various working conditions. Recently, several studies on the model analysis of intelligent edge computing have been recently reported in the current literature [13] [14] [15] [16]. Specifically, the works in [13] [14] investigated the service provisioning capabilities of edge computing systems for mobile devices with respect to service response time and outage probability.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, the works in [13] [14] investigated the service provisioning capabilities of edge computing systems for mobile devices with respect to service response time and outage probability. Furthermore, because the load sharing technology is of paramount importance for the edge computing system to handle a large number of task arrivals, the authors in [15] [16] proposed novel analytical models to investigate the performance improvement of load sharing for mobile edge computing systems. Although some progress has been made in the model analysis of edge computing systems, the existing analytical models can be hardly used to investigate the performance of intelligent edge computing with IoV applications.…”

Section: Introductionmentioning

confidence: 99%

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Performance Modelling and Quantitative Analysis of Vehicular Edge Computing With Bursty Task Arrivals

Miao

Min

Zhang

et al. 2023

IEEE Trans. on Mobile Comput.

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The quantitative performance analysis plays a critical role in assessing the capability of Vehicular Edge Computing (VEC) systems to meet the requirements of vehicular applications. However, developing accurate analytical models for VEC systems is extremely challenging due to the unique features of intelligent vehicular applications. Specifically, recent work revealed that the tasks generated by intelligent vehicular applications exhibit a high degree of burstiness, rendering the existing models that were designed based on the assumption of the non-bursty Poisson process unsuitable for VEC systems. To fill this gap, we developed an original analytical model to investigate the performance of VEC systems with bursty task arrivals. To facilitate vehicle cooperation, a new priority-based resource allocation scheme is exploited to schedule the tasks of vehicular applications, which are modelled by a Markov Modulated Poisson Process (MMPP). Next, a multi-state Markov chain is established to investigate the impact of load sharing strategy on the performance of VEC systems. Then, the end-to-end transmission latency is derived based on the proposed model. Comprehensive experiments are conducted to validate the accuracy of this analytical model under various system configurations. Furthermore, the developed model is used as a cost-effective tool to investigate the performance bottleneck of VEC systems.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance Modelling and Quantitative Analysis of Vehicular Edge Computing With Bursty Task Arrivals

Miao

Min

Zhang

et al. 2023

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

show abstract

“…The allocation matrixΦ can be simplified considering the deployment of SaaS and PaaS for the WSCP. Let us definē as a [ × ( + )] delay matrix, andΦ as a [ × ( + )] binary allocation matrix as below: 4 The deployment of the IaaS is needed only if the CFNs request at least two applications that are supported by two different platforms, otherwise SaaS and PaaS is sufficient, hence going back to the solutions modeled in the first + columns. 5 It should be noted that not all the solutions in this space respect the feasibility conditions defined in Def.1.…”

Section: B Solutions To the Problem 1) Reduced Space Optimal Solutionmentioning

confidence: 99%

“…When moving from a centralized cloud architecture to a distributed edge architecture, a proper service model deployment policy becomes of paramount importance for coping with users requests while respecting their requirements [2], [3]. Some works have considered SaaS, PaaS or IaaS model deployment in edge networks [4]- [6]. Game-theoretic approaches have also been considered for resource allocation in cloud and fog environments [7], [8].…”

Section: Introductionmentioning

confidence: 99%