Avaliação de Desempenho de Computação Móvel na Borda Usando Redes de Petri Estocásticas
Abstract
Mobile Edge Computing (MEC) has emerged as an alternative to reduce network latency that has leaded to the data flow processing closer to the user. However, the server machines resource capacity can directly influence MEC performance. This paper proposes a Stochastic Petri Net (SPN) model to such a scenario and analyzes its performance, considering several parameters that can directly affect the Mean Response Time (MRT) and Utilization Level. We also present numerical analysis that serve as a practical guide to assist computer infrastructure managers to design their architectures, finding the trade-off between MRT and utilization.
References
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Cau, E., Corici, M., Bellavista, P., Foschini, L., Carella, G., Edmonds, A., and Bohnert, T. M. (2016). Efficient exploitation of mobile edge computing for virtualized 5g in epc architectures. In 2016 4th IEEE international conference on mobile cloud computing, services, and engineering (MobileCloud), pages 100–109. IEEE.
Gopika Premsankar, M. d. F. and Taleb, T. (2018). Edge computing for the internet of things: A case study. 5:1275–1284. Guangshun Li, J. W. and Junhua Wu, J. S. (2018). Data processing delay optimization in mobile edge computing. 2018.
Jain, R. (1990). The art ofcomputer systems performance analysis: techniques for expe- rimental design, measurement, simulation, and modeling. John Wiley & Sons.
Jararweh, Y., Doulat, A., Darabseh, A., Alsmirat, M., Al-Ayyoub, M., and Benkhelifa, E. (2016). Sdmec: Software defined system for mobile edge computing. In 2016 IEEE International Conference on Cloud Engineering Workshop (IC2EW), pages 88– 93. IEEE.
Jung, H. (2016). Cisco visual networking index: Global mobile data traffic forecast up- date, 2015–2020 white paper. Technical report, Technical report, Cisco Systems Inc.
Kitanov, S., Monteiro, E., and Janevski, T. (2016). 5g and the fog—survey of related technologies and research directions. In 2016 18th Mediterranean Electrotechnical Conference (MELECON), pages 1–6. IEEE.
Little, J. D. (1961). A proof for the queuing formula: L= λ w. Operations research, 9(3):383–387.
Orsini, G., Bade, D., and Lamersdorf, W. (2015). Computing at the mobile edge: Desig- ning elastic android applications for computation offloading. In 2015 8th IFIP Wireless and Mobile Networking Conference (WMNC), pages 112–119. IEEE.
Trinh, C. and Yao, L. (2017). Energy-aware mobile edge computing for low-latency visual data processing. pages 128–133.
Yuanzhe and Shangguang (2018). An energy-aware edge server placement algorithm in mobile edge computing. In IEEE International Conference on Edge Computing (EDGE).
Published
2019-07-08
How to Cite
SANTOS, Brena; CARVALHO, Daniel ; FÉ, Iure ; SILVA, Francisco Airton.
Avaliação de Desempenho de Computação Móvel na Borda Usando Redes de Petri Estocásticas. In: WORKSHOP ON PERFORMANCE OF COMPUTER AND COMMUNICATION SYSTEMS (WPERFORMANCE), 2019. , 2019, Belém.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
ISSN 2595-6167.
DOI: https://doi.org/10.5753/wperformance.2019.6474.
