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Assessing mobile applications performance and energy consumption through experiments and Stochastic models

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Abstract

Energy consumption, execution time, and availability are common terms in discussions on application development for mobile devices. Mobile applications executing in a mobile cloud computing (MCC) environment must consider several issues, such as Internet connections problems and CPU performance. Misconceptions during the design phase can have a significant impact on costs and time-to-market, or even make the application development unfeasible. Anticipating the best configuration for each type of application is a challenge that many developers are not prepared to tackle. In this work, we propose models to rapidly estimate execution time, availability, and energy consumption of mobile applications executing in an MCC environment. We defined a methodology to create and validate Deterministic and Stochastic Petri net (DSPN) models to evaluate these three critical metrics. The DSPNs results were compared with results obtained through experiments performed on a testbed environment. We analyzed an image processing application, regarding connections type (WLAN, WiFi, and 3G), servers type (MCC or cloudlet), and functionalities performance. Our numerical analyses indicate, for instance, that the use of a cloudlet significantly improves performance and energy efficiency. Besides, the baseline scenario took us one month to implement, while modeling and evaluation the three scenarios required less than one day. In this way, our DSPN models represent a powerful tool for mobile developers to plan efficient and cost-effective mobile applications. They allow rapidly assess execution time, availability, and energy consumption metrics to improve the quality of mobile applications.

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References

  1. (2015) Instagram blog. http://blog.instagram.com/post/129662501137/150922-400million. Accessed 10 Jan 2018

  2. (2017) Tl-wr740n. http://www.tp-link.com/us/products/details/cat-5506_TL-WR740N.html#specifications. Accessed 11 Jan 2018

  3. Ajmone Marsan M, Bobbio A, Donatelli S (1998) Petri nets in performance analysis: an introduction. Springer, Berlin, pp 211–256. https://doi.org/10.1007/3-540-65306-6_17

    Book  Google Scholar 

  4. Allam H, Nassiri N, Rajan A, Ahmad J (2017) A critical overview of latest challenges and solutions of mobile cloud computing. In: 2017 Second international conference on fog and mobile edge computing (FMEC), pp 225–229. https://doi.org/10.1109/FMEC.2017.7946435

  5. Andrade E, Nogueira B (2018) Performability evaluation of a cloud-based disaster recovery solution for IT environments. J Grid Comput. https://doi.org/10.1007/s10723-018-9446-2

  6. Andrade E, Nogueira B, Matos R, Callou G, Maciel P (2017) Availability modeling and analysis of a disaster-recovery-as-a-service solution. Computing, pp 1–26. https://doi.org/10.1007/s00607-017-0539-8

  7. Avizienis A, Laprie J, Randell B (2001) Fundamental concepts of dependability. LAAS-CNRS, Technical Report N01145. https://pld.ttu.ee/IAF0530/16/avi1.pdf. Accessed 05 Dec 2017

  8. Balasubramanian N, Balasubramanian A, Venkataramani A (2009) Energy consumption in mobile phones: a measurement study and implications for network applications. In: Proceedings of the 9th ACM SIGCOMM conference on internet measurement. ACM, IMC ’09, New York, pp 280–293. https://doi.org/10.1145/1644893.1644927

  9. Benkhelifa E, Welsh T, Tawalbeh L, Jararweh Y, Basalamah A (2016) Energy optimisation for mobile device power consumption: a survey and a unified view of modelling for a comprehensive network simulation. Mob Netw Appl 21(4):575–588. https://doi.org/10.1007/s11036-016-0756-y

    Article  Google Scholar 

  10. Chaffey D (2018) Mobile marketing statistics compilation. https://www.smartinsights.com/mobile-marketing/mobile-marketing-analytics/mobile-marketing-statistics/. Accessed 2 Sept 2018

  11. Chang X, Martinez JM, Trivedi KS (2018) Transient performance analysis of smart grid with dynamic power distribution. Inf. Sci. 422:98–109. https://doi.org/10.1016/j.ins.2017.09.003

    Article  Google Scholar 

  12. Cooper T, Farrell R (2007) Value-chain engineering of a tower-top cellular base station system. In: Vehicular technology conference, 2007. VTC2007-Spring. IEEE 65th, pp 3184–3188. https://doi.org/10.1109/VETECS.2007.652

  13. Costa I, Araujo J, Dantas J, Campos E, Silva FA, Maciel P (2016) Availability evaluation and sensitivity analysis of a mobile backend-as-a-service platform. Qual. Reliab. Eng. Int. 32(7):2191–2205. https://doi.org/10.1002/qre.1927

    Article  Google Scholar 

  14. Dantas J, Matos R, Araujo J, Maciel P (2012) An availability model for eucalyptus platform: an analysis of warm-standy replication mechanism. In: Systems, man, and cybernetics (SMC), IEEE International Conference, pp 1664–1669. https://doi.org/10.1109/ICSMC.2012.6377976

  15. Dinh HT, Lee C, Niyato D, Wang P (2011) A survey of mobile cloud computing: architecture, applications, and approaches. Wirel Commun Mob Comput 13(18):1587–1611. https://doi.org/10.1002/wcm.1203

    Article  Google Scholar 

  16. Evans G, Miller J, Pena MI, MacAllister A, Winer E (2017) Evaluating the microsoft hololens through an augmented reality assembly application. Degraded environments: sensing, processing, and display 2017, vol 10197. International Society for Optics and Photonics, Bellingham

    Google Scholar 

  17. Jain R (1991) The art of computer systems performance analysis. Wiley, New York

    MATH  Google Scholar 

  18. Kovachev D, Cao Y, Klamma R (2011) Mobile cloud computing: a comparison of application models. https://arxiv.org/abs/1107.4940. Accessed 11 Jan 2018

  19. Kumar K, Liu J, Lu YH, Bhargava B (2013) A survey of computation offloading for mobile systems. Mob Netw Appl 18(1):129–140. https://doi.org/10.1007/s11036-012-0368-0

    Article  Google Scholar 

  20. Lin Y, Kamarainen T, Francesco MD, Yla-Jaaki A (2015) Performance evaluation of remote display access for mobile cloud computing. Comput Commun 72:17–25. https://doi.org/10.1016/j.comcom.2015.05.006

    Article  Google Scholar 

  21. Liu F, Shu P, Jin H, Ding L, Yu J, Niu D, Li B (2013) Gearing resource-poor mobile devices with powerful clouds: architectures, challenges, and applications. IEEE Wirel Commun 20(3):1. https://doi.org/10.1109/MWC.2013.6549279

    Article  Google Scholar 

  22. Marsan MA, Chiola G (1986) On Petri nets with deterministic and exponentially distributed firing times. In: European workshop on applications and theory in Petri nets. Springer, Berlin, Heidelberg, pp 132–145. http://dl.acm.org/citation.cfm?id=647734.735301. Accessed 10 Dec 2017

  23. Marsan MA, Balbo G, Conte G, Donatelli S, Franceschinis G (1994) Modelling with generalized Stochastic Petri nets, 1st edn. Wiley, New York

    MATH  Google Scholar 

  24. Nguyen TA, Kim DS, Park JS (2016) Availability modeling and analysis of a data center for disaster tolerance. Future Gener Comput Syst 56:27–50. https://doi.org/10.1016/j.future.2015.08.017

    Article  Google Scholar 

  25. Nurmi D, Wolski R, Grzegorczyk C, Obertelli G, Soman S, Youseff L, Zagorodnov D (2009) Eucalyptus: an open-source cloud computing infrastructure. J Phys: Conf Ser 180(1). https://doi.org/10.1088/1742-6596/180/1/012051

  26. Oliveira D, Araujo J, Matos R, Maciel P (2013) Availability and energy consumption analysis of mobile cloud environments. In: 2013 IEEE international conference on systems, man, and cybernetics (SMC), pp 4086–4091. https://doi.org/10.1109/SMC.2013.697

  27. Oliveira D, Matos R, Dantas J, Ferreira Ja, Silva B, Callou G, Maciel P, Brinkmann A (2017) Advanced stochastic petri net modeling with the mercury scripting language. In: Proceedings of the 11th EAI international conference on performance evaluation methodologies and tools. ACM, VALUETOOLS 2017, New York, pp 192–197. https://doi.org/10.1145/3150928.3150959

  28. OpenStack (2018) Openstack. https://openstack.org/. Accessed 19 Jan 2018

  29. Petri CA (1962) Kommunikation mit automaten. Ph.D. thesis, Universität Hamburg, Hamburg

  30. Pushp S, Hwang C, Koh C, Yoon J, Liu Y, Choi S, Song J (2017) Demo: frog: optimizing power consumption of mobile games using perception-aware frame rate scaling. In: Proceedings of the 23rd annual international conference on mobile computing and networking. ACM, MobiCom ’17, New York, pp 498–500. https://doi.org/10.1145/3117811.3119868

  31. Satyanarayanan M (1996) Fundamental challenges in mobile computing. In: Proceedings of the fifteenth annual ACM symposium on principles of distributed computing. ACM, PODC ’96, New York, pp 1–7. https://doi.org/10.1145/248052.248053

  32. Satyanarayanan M, Bahl P, Caceres R, Davies N (2009) The case for vm-based cloudlets in mobile computing. IEEE Pervasive Comput 8(4):14–23. https://doi.org/10.1109/MPRV.2009.82

    Article  Google Scholar 

  33. Sensor Tower (2018) Global app revenue grew 35% in 2017 to nearly \$60 billion. https://sensortower.com/blog/app-revenue-and-downloads-2017. Accessed 19 Jan 2018

  34. Sherr I, Tibken S (2014) Why is my battery stuck in the ’90s? https://www.cnet.com/news/why-batteries-arent-getting-better/. Accessed 8 Dec 2017

  35. Shorin D, Zimmermann A, Maciel P (2012) Transforming uml state machines into stochastic petri nets for energy consumption estimation of embedded systems. In: Sustainable Internet and ICT for Sustainability (SustainIT), pp 1–6

  36. Silva JS, Lins FAA, Sousa ETG, Summer HB, Fernandes CM (2017) Invasive technique for measuring the energy consumption of mobile devices applications in mobile cloud environments. In: 2017 IEEE international conference on systems, man, and cybernetics (SMC), pp 2724–2729. https://doi.org/10.1109/SMC.2017.8123038

  37. Sousa E, Lins F, Tavares E, Cunha P, Maciel P (2015) A modeling approach for cloud infrastructure planning considering dependability and cost requirements. IEEE Trans Syst Man Cybern Syst 45(4):549–558. https://doi.org/10.1109/TSMC.2014.2358642

    Article  Google Scholar 

  38. Strategy Analytics (2009) Cellphone energy gap is widening. https://www4.strategyanalytics.com/default.aspx?mod=pressreleaseviewer&a0=4656. Accessed 11 Jan 2018

  39. Temple S (2018) Mobile vs Desktop Usage in 2018: mobile takes the lead. Tech. rep. https://www.stonetemple.com/mobile-vs-desktop-usage-study/. Accessed 15 May 2018

  40. Torres E, Callou G, Andrade E (2018) A hierarchical approach for availability and performance analysis of private cloud storage services. Computing. https://doi.org/10.1007/s00607-018-0588-7

  41. Trivedi KS (1982) Probability and statistics with reliability, queuing and computer science applications. Prentice Hall PTR, Upper Saddle River

    MATH  Google Scholar 

  42. Yuan W, Nahrstedt K (2003) Energy-efficient soft real-time cpu scheduling for mobile multimedia systems. SIGOPS Oper Syst Rev 37(5):149–163. https://doi.org/10.1145/1165389.945460

    Article  Google Scholar 

  43. Zhang L, Tiwana B, Dick R, Qian Z, Mao Z, Wang Z, Yang L (2010) Accurate online power estimation and automatic battery behavior based power model generation for smartphones. In: IEEE/ACM/IFIP international conference on hardware/software codesign and system synthesis (CODES+ISSS), pp 105–114

  44. Zhang W, Wen Y, Wu DO (2013) Energy-efficient scheduling policy for collaborative execution in mobile cloud computing. In: 2013 Proceedings IEEE INFOCOM, pp 190–194. https://doi.org/10.1109/INFCOM.2013.6566761

  45. Zimmermann A, Knoke M (2007) TimeNet 4.0: A Software Tool for the Performability Evaluation with Stochastic and Colored Petri Nets. User Manual. Forschungsberichte der Fakultät IV - Elektrotechnik und Informatik, Techn. Univ., Fak. IV, Elektrotechnik und Informatik

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Acknowledgements

This research was partially funded by the Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco (FACEPE) by the Grant IBPG-0418-1.03/15.

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Authors and Affiliations

  1. Informatics Center, Federal University of Pernambuco, Recife, Brazil

    Júlio Mendonça & Ricardo Lima

  2. Department of Computing, Federal Rural University of Pernambuco, Recife, Brazil

    Ermeson Andrade

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  1. Júlio Mendonça
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Correspondence to Júlio Mendonça.

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Mendonça, J., Andrade, E. & Lima, R. Assessing mobile applications performance and energy consumption through experiments and Stochastic models. Computing 101, 1789–1811 (2019). https://doi.org/10.1007/s00607-019-00707-6

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