LMM: latency-aware micro-service mashup in mobile edge computing environment

Abstract

Internet of Things (IoT) applications introduce a set of stringent requirements (e.g., low latency, high bandwidth) to network and computing paradigm. 5G networks are faced with great challenges for supporting IoT services. The centralized cloud computing paradigm also becomes inefficient for those stringent requirements. Only extending spectrum resources cannot solve the problem effectively. Mobile edge computing offers an IT service environment at the Radio Access Network edge and presents great opportunities for the development of IoT applications. With the capability to reduce latency and offer an improved user experience, mobile edge computing becomes a key technology toward 5G. To achieve abundant sharing, complex IoT applications have been implemented as a set of lightweight micro-services that are distributed among containers over the mobile edge network. How to produce the optimal collocation of suitable micro-service for an application in mobile edge computing environment is an important issue that should be addressed. To address this issue, we propose a latency-aware micro-service mashup approach in this paper. Firstly, the problem is formulated into an integer nonlinear programming. Then, we prove the NP-hardness of the problem by reducing it into the delay constrained least cost problem. Finally, we propose an approximation latency-aware micro-service mashup approach to solve the problem. Experiment results show that the proposed approach achieves a substantial reduction in network resource consumption while still ensuring the latency constraint.

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References

  1. 1.

    Kitchin R (2014) The real-time city? Big data and smart urbanism. GeoJournal 79:1–14

    Article  Google Scholar 

  2. 2.

    Clement SJ, McKee DW, Xu J (April 2017) Service-oriented reference architecture for smart cities. In: 2017 IEEE symposium on service-oriented system engineering (SOSE), pp 81–85

  3. 3.

    Jin J, Gubbi J, Marusic S, Palaniswami M (2014) An information framework for creating a smart city through internet of things. IEEE Internet Things J 1:112–121

    Article  Google Scholar 

  4. 4.

    Sanchez L, Muoz L, Galache JA, Sotres P, Santana JR, Gutierrez V, Ramdhany R, Gluhak A, Krco S, Theodoridis E, Pfisterer D (2014) Smartsantander: Iot experimentation over a smart city testbed. Comput Netw 61:217–238 (Special issue on Future Internet Testbeds Part I)

    Article  Google Scholar 

  5. 5.

    Wang Y, Zheng Y, Xue Y (2014) Travel time estimation of a path using sparse trajectories. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’14, New York, NY, USA. ACM, New York, pp 25–34

  6. 6.

    Li Y, Zheng Y, Zhang H, Chen L (2015) Traffic prediction in a bike-sharing system. In: Proceedings of the 23rd SIGSPATIAL international conference on advances in geographic information systems, SIGSPATIAL ’15, New York, NY, USA. ACM, New York, pp 33:1–33:10

  7. 7.

    Wang H, Ma S, Dai H (2019) A rhombic dodecahedron topology for human-centric banking big data. IEEE Trans Comput Soc Syst 6:1095–1105

    Article  Google Scholar 

  8. 8.

    Wang H, Ma S, Dai H-N, Imran M, Wang T (2019) Blockchain-based data privacy management with nudge theory in open banking. Future Gener Comput Syst. https://doi.org/10.1016/j.future.2019.09.010

    Article  Google Scholar 

  9. 9.

    Drolia U, Guo K, Tan J, Gandhi R, Narasimhan P (2017) Cachier: edge-caching for recognition applications. In: 2017 IEEE 37th international conference on distributed computing systems (ICDCS), pp 276–286

  10. 10.

    Benchara FZ, Youssfi M, Bouattane O, Ouajji H (2016) A new efficient distributed computing middleware based on cloud micro-services for HPC. In: 2016 5th international conference on multimedia computing and systems (ICMCS), pp 354–359

  11. 11.

    Bhamare D, Samaka M, Erbad A, Jain R, Gupta L, Chan HA (2017) Multi-objective scheduling of micro-services for optimal service function chains. In: 2017 IEEE international conference on communications (ICC), pp 1–6

  12. 12.

    Cherradi G, Bouziri AE, Boulmakoul A, Zeitouni K (2017) Real-time hazmat environmental information system: a micro-service based architecture. Procedia Comput Sci 109:982–987. In: 8th international conference on ambient systems, networks and technologies, ANT-2017 and the 7th international conference on sustainable energy information technology, SEIT 2017, 16–19 May 2017, Madeira, Portugal

  13. 13.

    Zhou A, Wang S, Cheng B, Zheng Z, Yang F, Chang RN, Lyu MR, Buyya R (2017) Cloud service reliability enhancement via virtual machine placement optimization. IEEE Trans Serv Comput 10:902–913

    Article  Google Scholar 

  14. 14.

    Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener Comput Syst 28(5):755–768 (Special section: energy efficiency in large-scale distributed systems)

    Article  Google Scholar 

  15. 15.

    Zhang K, Mao Y, Leng S, Zhao Q, Li L, Peng X, Pan L, Maharjan S, Zhang Y (2016) Energy-efficient offloading for mobile edge computing in 5G heterogeneous networks. IEEE Access 4:5896–5907

    Article  Google Scholar 

  16. 16.

    You C, Huang K, Chae H, Kim B (2017) Energy-efficient resource allocation for mobile-edge computation offloading. IEEE Trans Wirel Commun 16:1397–1411

    Article  Google Scholar 

  17. 17.

    Liu C, Cao Y, Luo Y, Chen G, Vokkarane V, Yunsheng M, Chen S, Hou P (2018) A new deep learning-based food recognition system for dietary assessment on an edge computing service infrastructure. IEEE Trans Serv Comput 11:249–261

    Article  Google Scholar 

  18. 18.

    Higashino T, Yamaguchi H, Hiromori A, Uchiyama A, Yasumoto K (2017) Edge computing and iot based research for building safe smart cities resistant to disasters. In: 2017 IEEE 37th international conference on distributed computing systems (ICDCS), pp 1729–1737

  19. 19.

    Cao B, Liu J, Tang M, Zheng Z, Wang G (2013) Mashup service recommendation based on user interest and social network. In: 2013 IEEE 20th international conference on web services, pp 99–106

  20. 20.

    Im J, Kim S, Kim D (2013) Iot mashup as a service: cloud-based mashup service for the internet of things. In: 2013 IEEE international conference on services computing, pp 462–469

  21. 21.

    Wang P, Ding Z, Jiang C, Zhou M, Zheng Y (2016) Automatic web service composition based on uncertainty execution effects. IEEE Trans Serv Comput 9:551–565

    Article  Google Scholar 

  22. 22.

    Jin H, Yao X, Chen Y (2017) Correlation-aware QoS modeling and manufacturing cloud service composition. J Intell Manuf 28:1947–1960

    Article  Google Scholar 

  23. 23.

    Rimal BP, Maier M (2017) Workflow scheduling in multi-tenant cloud computing environments. IEEE Trans Parallel Distrib Syst 28:290–304

    Article  Google Scholar 

  24. 24.

    Zhang F, Cao J, Hwang K, Li K, Khan SU (2015) Adaptive workflow scheduling on cloud computing platforms with iterativeordinal optimization. IEEE Trans Cloud Comput 3:156–168

    Article  Google Scholar 

  25. 25.

    Deldari A, Naghibzadeh M, Abrishami S (2017) CCA: a deadline-constrained workflow scheduling algorithm for multicore resources on the cloud. J Supercomput 73:756–781

    Article  Google Scholar 

  26. 26.

    Verma A, Kaushal S (2017) A hybrid multi-objective particle swarm optimization for scientific workflow scheduling. Parallel Comput 62:1–19

    MathSciNet  Article  Google Scholar 

  27. 27.

    Verbelen T, Simoens P, De Turck F, Dhoedt B (2012) Cloudlets: bringing the cloud to the mobile user. In: Proceedings of the third ACM workshop on mobile cloud computing and services, MCS ’12, New York, NY, USA. ACM, New York, pp 29–36

  28. 28.

    Xu Z, Liang W, Xu W, Jia M, Guo S (2016) Efficient algorithms for capacitated cloudlet placements. IEEE Trans Parallel Distrib Syst 27:2866–2880

    Article  Google Scholar 

  29. 29.

    Chen X, Jiao L, Li W, Fu X (2016) Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans Netw 24:2795–2808

    Article  Google Scholar 

  30. 30.

    Hoang DT, Niyato D, Wang P (2012) Optimal admission control policy for mobile cloud computing hotspot with cloudlet. In: 2012 IEEE wireless communications and networking conference (WCNC), pp 3145–3149

  31. 31.

    Cardellini V, De Nitto Personé V, Di Valerio V, Facchinei F, Grassi V, Lo Presti F, Piccialli V (2016) A game-theoretic approach to computation offloading in mobile cloud computing. Math Program 157:421–449

    MathSciNet  Article  Google Scholar 

  32. 32.

    Gelenbe E, Lent R, Douratsos M (2012) Choosing a local or remote cloud. In: 2012 second symposium on network cloud computing and applications, pp 25–30

  33. 33.

    Jia M, Cao J, Liang W (2017) Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks. IEEE Trans Cloud Comput 5:725–737

    Article  Google Scholar 

  34. 34.

    Malawski M, Juve G, Deelman E, Nabrzyski J (2015) Algorithms for cost- and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds. Future Gener Comput Syst 48:1–18 (Special section, business and industry specific cloud)

    Article  Google Scholar 

  35. 35.

    Zhu Z, Zhang G, Li M, Liu X (2016) Evolutionary multi-objective workflow scheduling in cloud. IEEE Trans Parallel Distrib Syst 27:1344–1357

    Article  Google Scholar 

  36. 36.

    Arabnejad H, Barbosa JG, Prodan R (2016) Low-time complexity budget-deadline constrained workflow scheduling on heterogeneous resources. Future Gener Comput Syst 55:29–40

    Article  Google Scholar 

  37. 37.

    Lee YC, Han H, Zomaya AY, Yousif M (2015) Resource-efficient workflow scheduling in clouds. Knowl-Based Syst 80:153–162 (25th anniversary of knowledge-based systems)

    Article  Google Scholar 

  38. 38.

    Wang H, Chen X, Wu Q, Yu Q, Hu X, Zheng Z, Bouguettaya A (2017) Integrating reinforcement learning with multi-agent techniques for adaptive service composition. ACM Trans Auton Adapt Syst 12:8:1–8:42

    Google Scholar 

  39. 39.

    Deng S, Wu H, Hu D, Zhao JL (2016) Service selection for composition with QoS correlations. IEEE Trans Serv Comput 9:291–303

    Article  Google Scholar 

  40. 40.

    Chen F, Dou R, Li M, Wu H (2016) A flexible QoS-aware web service composition method by multi-objective optimization in cloud manufacturing. Comput Ind Eng 99:423–431

    Article  Google Scholar 

  41. 41.

    Deng S, Wu H, Taheri J, Zomaya AY, Wu Z (2016) Cost performance driven service mashup: a developer perspective. IEEE Trans Parallel Distrib Syst 27:2234–2247

    Article  Google Scholar 

  42. 42.

    Sun Y, Zhou S, Xu J (2017) EMM: energy-aware mobility management for mobile edge computing in ultra dense networks. IEEE J Sel Areas Commun 35:2637–2646

    Article  Google Scholar 

  43. 43.

    Hassin R (1992) Approximation schemes for the restricted shortest path problem. Math Oper Res 17(1):36–42

    MathSciNet  Article  Google Scholar 

  44. 44.

    Duran MA, Grossmann IE (1986) An outer-approximation algorithm for a class of mixed-integer nonlinear programs. Math Program 36:307–339

    MathSciNet  Article  Google Scholar 

  45. 45.

    Niu C, Li Y, Hu RQ, Ye F (2017) Fast and efficient radio resource allocation in dynamic ultra-dense heterogeneous networks. IEEE Access 5:1911–1924

    Google Scholar 

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Acknowledgements

I would like to express my gratitude to all those who helped me during the writing of this paper. The work presented in this study is supported by NSFC (61602054), NSFC (61571066).

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Correspondence to Shaohua Wan.

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Zhou, A., Wang, S., Wan, S. et al. LMM: latency-aware micro-service mashup in mobile edge computing environment. Neural Comput & Applic 32, 15411–15425 (2020). https://doi.org/10.1007/s00521-019-04693-w

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Keywords

  • Micro-service
  • Mobile edge computing
  • Network resource consumption
  • Latency
  • Mashup