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Optimal deployment of cloudlets based on cost and latency in Internet of Things networks

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Abstract

With the development of the Internet of Things (IoT), a large amount of data is generated on the network edge. Given the limited computing power of mobile devices (MDs) and access to computing resources from remote clouds, which leads to high latency to MDs, edge computing provides a way to reduce service latency by building a miniature cloud (Cloudlet). MDs transfer tasks they generate to nearby cloudlets for lower latency. Although a lot of research has been done in the field of edge computing, little attention has been paid to how to deploy cloudlets in the network. In this paper, we study the cloudlet deployment on a large number of wireless access points (APs) in an IoT network to optimize both deployment cost and network latency. When the cloudlets has been deployed in the network, we propose a fault-tolerant cloudlet deployment scheme. When the original cloudlets in the network fail, the software-defined network technology is used to start the fault-tolerant cloudlets in time to ensure the stability of the network latency. To address the above problems, we propose a binary-based differential evolution cuckoo search (BDECS) algorithm, which selects the permanent cloudlet deployment location among a large number of APs on the network. Extensive simulations reveal that the proposed algorithm has better performance in minimizing cost and latency compared with other deploymegt algorithms. Moreover, the convergence speed of the BDECS algorithm is also superior to other algorithms.

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References

  1. Shi, W., Sun, H., Cao, J., Zhang, Q., & Liu, W. (2017). Edge computing-an emerging computing model for the internet of everything era. Journal of computer research and development, 54(5), 907–924.

    Google Scholar 

  2. Satyanarayanan, M., Bahl, V., Caceres, R., & Davies, N. (2009). The case for VM-based cloudlets in mobile computing. IEEE Pervasive Computing.

  3. Rodrigues, T. G., Suto, K., Nishiyama, H., & Kato, N. (2016). Hybrid method for minimizing service delay in edge cloud computing through vm migration and transmission power control. IEEE Transactions on Computers, 66(5), 810–819.

    Article  MathSciNet  Google Scholar 

  4. Pang, Z., Sun, L., Wang, Z., Tian, E., & Yang, S. (2015). A survey of cloudlet based mobile computing. In International Conference on Cloud Computing and Big Data (CCBD) (pp. 268–275). IEEE.

  5. Brogi, A., & Forti, S. (2017). Qos-aware deployment of iot applications through the fog. IEEE Internet of Things Journal, 4(5), 1185–1192.

    Article  Google Scholar 

  6. Lantz, B., Heller, B., & McKeown, N. (2010). A network in a laptop: rapid prototyping for software-defined networks. In Proceedings of the 9th ACM SIGCOMM Workshop on Hot Topics in Networks (p. 19). ACM.

  7. Kawamoto, Y., Yamada, N., Nishiyama, H., Kato, N., Shimizu, Y., & Zheng, Y. (2017). A feedback control-based crowd dynamics management in iot system. IEEE Internet of Things Journal, 4(5), 1466–1476.

    Article  Google Scholar 

  8. Cao, Z., Panwar, S. S., Kodialam, M., & Lakshman, T. (2017). Enhancing mobile networks with software defined networking and cloud computing. IEEE/ACM Transactions on Networking (TON), 25(3), 1431–1444.

    Article  Google Scholar 

  9. Cardei, M., Yang, S., & Wu, J. (2007). Fault-tolerant topology control for heterogeneous wireless sensor networks. In IEEE International Conference on Mobile Adhoc and Sensor Systems (pp. 1–9). IEEE.

  10. Jiao, L., Pu, L., Wang, L., Lin, X., & Li, J. (2018). Multiple granularity online control of cloudlet networks for edge computing. In 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) (pp. 1–9). IEEE.

  11. Xu, X., Chen, Y., Yuan, Y., Huang, T., Zhang, X., & Qi, L. (2019). Blockchain-based cloudlet management for multimedia workflow in mobile cloud computing. Multimedia Tools and Applications, 1, 1–26.

    Google Scholar 

  12. Tong, L., Li, Y., & Gao, W. (2016). A hierarchical edge cloud architecture for mobile computing. In IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications (pp 1–9). IEEE.

  13. 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 (pp. 29–36). ACM.

  14. Yao, D., Gui, L., Hou, F., Sun, F., Mo, D., & Shan, H. (2017). Load balancing oriented computation offloading in mobile cloudlet. In: IEEE 86th Vehicular Technology Conference (VTC-Fall) (pp. 1–6). IEEE.

  15. Jia, M., Liang, W., Xu, Z., & Huang, M. (2016). Cloudlet load balancing in wireless metropolitan area networks. In IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications (pp. 1–9). IEEE.

  16. Crawford, B., Soto, R., Berríos, N., Johnson, F., & Paredes, F. (2015). Solving the set covering problem with binary cat swarm optimization. In International Conference in Swarm Intelligence (pp. 41–48). Springer.

  17. Zeng, D., Gu, L., Guo, S., Cheng, Z., & Yu, S. (2016). Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Transactions on Computers, 65(12), 3702–3712.

    Article  MathSciNet  MATH  Google Scholar 

  18. Kiani, A., & Ansari, N. (2017). Toward hierarchical mobile edge computing: An auction-based profit maximization approach. IEEE Internet of Things Journal, 4(6), 2082–2091.

    Article  Google Scholar 

  19. Whaiduzzaman, M., Naveed, A., & Gani, A. (2016). Mobicore: Mobile device based cloudlet resource enhancement for optimal task response. IEEE Transactions on Services Computing, 11(1), 144–154.

    Article  Google Scholar 

  20. Zhang, H., Xiao, Y., Bu, S., Niyato, D., Yu, F. R., & Han, Z. (2017). Computing resource allocation in three-tier iot fog networks: A joint optimization approach combining stackelberg game and matching. IEEE Internet of Things Journal, 4(5), 1204–1215.

    Article  Google Scholar 

  21. Guan, S., Boukerche, A., & Ahmadvand, S. (2018). A cloudlet-based mobile computing model for resource and energy efficient offloading. In IEEE Symposium on Computers and Communications (ISCC) (pp. 00980–00985). IEEE.

  22. Deng, R., Lu, R., Lai, C., Luan, T. H., & Liang, H. (2016). Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption. IEEE Internet of Things Journal, 3(6), 1171–1181.

    Google Scholar 

  23. Xu, Z., Liang, W., Xu, W., Jia, M., & Guo, S. (2015). Efficient algorithms for capacitated cloudlet placements. IEEE Transactions on Parallel and Distributed Systems, 27(10), 2866–2880.

    Article  Google Scholar 

  24. Jia, M., Cao, J., & Liang, W. (2015). Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks. IEEE Transactions on Cloud Computing, 5(4), 725–737.

    Article  Google Scholar 

  25. Fan, Q., & Ansari, N. (2017). Cost aware cloudlet placement for big data processing at the edge. In IEEE International Conference on Communications (ICC) (pp 1–6). IEEE.

  26. Li, Y., & Wang, S. (2018). An energy-aware edge server placement algorithm in mobile edge computing. In IEEE International Conference on Edge Computing (EDGE) (pp. 66–73). IEEE.

  27. Meng, J., Shi, W., Tan, H., & Li, X. (2017). Cloudlet placement and minimum-delay routing in cloudlet computing. In 3rd International Conference on Big Data Computing and Communications (BIGCOM) (pp 297–304). IEEE.

  28. Zhao, L., Sun, W., Shi, Y., & Liu, J. (2018). Optimal placement of cloudlets for access delay minimization in sdn-based internet of things networks. IEEE Internet of Things Journal, 5(2), 1334–1344.

    Article  Google Scholar 

  29. Mondal, S., Das, G., & Wong, E. (2018). Ccompassion: A hybrid cloudlet placement framework over passive optical access networks. In IEEE INFOCOM 2018-IEEE Conference on Computer Communications (pp 216–224). IEEE.

  30. Yao, H., Bai, C., Xiong, M., Zeng, D., & Fu, Z. (2017). Heterogeneous cloudlet deployment and user-cloudlet association toward cost effective fog computing. Concurrency and Computation: Practice and Experience, 29(16), e3975.

    Article  Google Scholar 

  31. Mozaffari, M., Saad, W., Bennis, M., & Debbah, M. (2015). Drone small cells in the clouds: Design, deployment and performance analysis. In IEEE Global Communications Conference (GLOBECOM) (pp 1–6).

  32. Jeong, S., Simeone, O., & Kang, J. (2018). Mobile edge computing via a uav-mounted cloudlet: Optimization of bit allocation and path planning. IEEE Transactions on Vehicular Technology, 67(3), 2049–2063.

    Article  Google Scholar 

  33. Hughes, R., Muheidat, F., Lee, M., & Tawalbeh, L.A. (2019). Floor based sensors walk identification system using dynamic time warping with cloudlet support. In IEEE 13th International Conference on Semantic Computing (ICSC) (pp. 440–444).

  34. Satyanarayanan, M., Gibbons, PB., Mummert, L., Pillai, P., Simoens, P., & Sukthankar, R. (2017). Cloudlet-based just-in-time indexing of IoT video. In Global Internet of Things Summit (GIoTS) (pp. 1–8).

  35. Fan, Q., & Ansari, N. (2016). Green energy aware user association in heterogeneous networks. In IEEE wireless communications and networking conference (pp. 1–6). IEEE

  36. Yu, C., Lumezanu, C., Sharma, A., Xu, Q., Jiang, G., & Madhyastha, H.V. (2015). Software-defined latency monitoring in data center networks. In International Conference on Passive and Active Network Measurement (pp. 360–372). Springer.

  37. Charikar, M., Guha, S., Tardos, É., & Shmoys, D. B. (2002). A constant-factor approximation algorithm for the k-median problem. Journal of Computer and System Sciences, 65(1), 129–149.

    Article  MathSciNet  MATH  Google Scholar 

  38. Gandomi, A. H., Yang, X. S., & Alavi, A. H. (2013). Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems. Engineering with Computers, 29(1), 17–35.

    Article  Google Scholar 

  39. Yang, XS., & Deb, S. (2009). Cuckoo search via lévy flights. In World Congress on Nature & Biologically Inspired Computing (NaBIC) (pp. 210–214). IEEE.

  40. Yang, X.S., & Deb, S. (2010). Engineering optimisation by cuckoo search. arXiv preprint arXiv:1005.2908.

  41. Feng, D., Ruan, Q., & Du, L. (2013). Binary cuckoo search algorithm. Jisuanji Yingyong/ Journal of Computer Applications, 33(6), 1566–1570.

    Article  Google Scholar 

  42. Floyd, R. W. (1962). Algorithm 97: shortest path. Communications of the ACM, 5(6), 345.

  43. Kong, X., Gao, L., Ouyang, H., & Ge, Y. (2014). Binary differential evolution algorithm based on parameterless mutation strategy. Journal of Northeast University, 1, 484–488.

    MATH  Google Scholar 

  44. Solis, F. J., & Wets, R. J. B. (1981). Minimization by random search techniques. Mathematics of Operations Research, 6(1), 19–30.

    Article  MathSciNet  MATH  Google Scholar 

  45. Wang, F., He, X. S., Wang, Y., & Yang, S. M. (2012). Markov model and convergence analysis based on cuckoo search algorithm. Computer Engineering, 38(11), 180–182.

    Google Scholar 

  46. Barabási, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.

    Article  MathSciNet  MATH  Google Scholar 

  47. Holland, J. H. (1992). Adaptation in natural and artificial system.

  48. Steinbrunn, M., Moerkotte, G., & Kemper, A. (1997). Heuristic and randomized optimization for the join ordering problem. The VLDB Journal-The International Journal on Very Large Data Bases, 6(3), 191–208.

    Article  Google Scholar 

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Acknowledgements

This work was supported by the International Science and Technology Cooperation Program of the Science and Technology Department of Shaanxi Province, China (Grant No. 2018KW-049), the Special Scientific Research Program of Education Department of Shaanxi Province, China (Grant No. 17JK0711), the Communication Soft Science Program of Ministry of Industry and Information Technology, China (Grant No. 2019-R-29), the International Science and Technology Cooperation Program of the Science and Technology Department of Shaanxi Province, China (Grant No. 2019KW-008), Science and Technology Project in Shaanxi Province of China (Program No. 2019ZDLGY07-08), the Special Scientific Research Program of Education Department of Shaanxi Province (Grant No. 19JK0806), and Graduate Innovation Fund of Xi’an University of Posts and Telecommunications (Grant No. CXJJLY2019071).

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Authors and Affiliations

  1. School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, 710121, Shaanxi, China

    Zhongmin Wang & Xiaomin Jin

  2. Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an University of Posts and Telecommunications, Xi’an, 710121, Shaanxi, China

    Zhongmin Wang, Feng Gao & Xiaomin Jin

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  1. Zhongmin Wang
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Correspondence to Feng Gao.

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Wang, Z., Gao, F. & Jin, X. Optimal deployment of cloudlets based on cost and latency in Internet of Things networks. Wireless Netw 26, 6077–6093 (2020). https://doi.org/10.1007/s11276-020-02418-9

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