Zhi Zhou
ABSTRACT
Edge computing is envisioned to be the de-facto paradigm of hosting emerging low latency Internet-of-Things (IoT) data streaming services.For IoT data streaming in edge computing, cost management is of strategic significance, due to the low cost-efficiency of edge servers. While existing literature adopts a reactive approach to dynamically provisioning edge servers to reduce cost, the delay of server activation and instantiation has been mostly ignored. In this paper, we target a proactive approach to dynamic edge server provisioning for real-time IoT data streaming across edge nodes, which adjusts server provisioning ahead of time, based on prediction of the upcoming workload. To effectively predict upcoming workload, a learning-based method online gradient descent is applied. We further combine the online learning method with an online optimization algorithm for server provisioning in a joint online optimization framework, through (1) minimizing of the regret incurred by inaccurate workload prediction, and (2) minimizing the cost incurred by near-optimal online decisions. The resulting predictive online algorithm can well leverage the power of prediction and achieve a good performance guarantee, as verified by both rigorous theoretical analysis and extensive trace-driven evaluations.
- C. V. N. Index, "Cisco visual networking index: global mobile data traffic forecast update, 2016--2020," Tech. Rep, 2017.Google Scholar
- W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, "Edge computing: Vision and challenges," IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637--646, 2016.Google ScholarCross Ref
- Z. Zhou, X. Chen, E. Li, L. Zeng, K. Luo, and J. Zhang, "Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing," SYSU Technical report, April 2019.Google Scholar
- (2018) Microsoft will invest 5 billion dollars in IoT. Here's why. https://blogs.microsoft.com/iot/2018/04/04/microsoft-will-invest-5-billion-in-iot-heres-why/.Google Scholar
- O. Dekel, "Machine learning on the edge," in Proc. of the Workshop on Trends in Machine-Learning, 2017. Google ScholarDigital Library
- M. Lin, Z. Liu, A. Wierman, and L. L. H. Andrew, "Online algorithms for geographical load balancing," in Proc. of IGCC, 2012. Google ScholarDigital Library
- S. Shalev-Shwartz, Online Learning and Online Convex Optimization. Now Publishers Inc., 2012.Google Scholar
- Z. Zhou, F. Liu, Y. Xu, R. Zou, H. Xu, J. C. Lui, and H. Jin, "Carbon-aware load balancing for geo-distributed cloud services," in Proc. of IEEE MASCOTS, 2013. Google ScholarDigital Library
- L. Jiao, L. Pu, L. Wang, X. Lin, and J. Li, "Multiple granularity online control of cloudlet networks for edge computing," in Proc. of IEEE SECON, 2018.Google Scholar
- I. Hou, T. Zhao, S. Wang, K. Chan et al., "Asymptotically optimal algorithm for online reconfiguration of edge-clouds," in Proc. of ACM Mobihoc, 2016. Google ScholarDigital Library
- L. Lu, J. Tu, C.-K. Chau, M. Chen, and X. Lin, "Online energy generation scheduling for microgrids with intermittent energy sources and co-generation," in Proc. of ACM SIGMETRICS, 2013. Google ScholarDigital Library
- M. Shi, X. Lin, S. Fahmy, and D. H. Shin, "Competitive online convex optimization with switching costs and ramp constraints," in Proc. of IEEE INFOCOM, 2018.Google Scholar
- X. Zhang, C. Wu, Z. Li, and F. C. Lau, "Proactive vnf provisioning with multi-timescale cloud resources: Fusing online learning and online optimization," in Proc. of IEEE INFOCOM, 2017.Google Scholar
- X. Chen, W. Li, S. Lu, Z. Zhou, and X. Fu, "Efficient resource allocation for on-demand mobile-edge cloud computing," IEEE Transactions on Vehicular Technology, vol. 67, no. 9, pp. 8769--8780, 2018.Google ScholarCross Ref
- P. Suetens, P. Fua, and A. J. Hanson, "Computational strategies for object recognition," ACM Computing Surveys (CSUR), vol. 24, no. 1, 1992. Google ScholarDigital Library
- A. Qureshi, "Power-demand routing in massive geo-distributed systems," Ph.D. dissertation, Massachusetts Institute of Technology, 2010. Google ScholarDigital Library
- Online Technical Report. {Online}. Available: https://1drv.ms/b/s!Ar9mS_s-frkZgc5ec4KvUiQfRftv7wGoogle Scholar
- V. V. Vazirani, Approximation algorithms. Springer Science & Business Media, 2013. Google ScholarDigital Library
- N. Buchbinder, S. Chen, and J. Naor, "Competitive analysis via regularization," in Proc. of ACM/SIAM SODA, 2014. Google ScholarDigital Library
- S. Boyd and L. Vandenberghe, Convex optimization. Cambridge university press, 2004. Google ScholarDigital Library
- Rajiv, Khuller, Samir, Parthasarathy, Srinivasan, Srinivasan, and Aravind, "Dependent rounding and its applications to approximation algorithms." Journal of the Acm, vol. 53, no. 3, pp. 324--360, 2006. Google ScholarDigital Library
- L. Pu, L. Jiao, X. Chen, L. Wang, Q. Xie, and Y. Xu, "Online resource allocation, content placement and request routing for cost-efficient edge caching in cloud radio access networks," IEEE Journal on Selected Areas in Communications, vol. 36, no. 8, pp. 1751--1767, 2018.Google ScholarCross Ref
- Our open data - Transport for London. https://tfl.gov.uk/info-for/open-data-users/our-open-data.Google Scholar
- Energy Tariffs - EDF Energy. https://www.edfenergy.com/sme-business/tariffs.Google Scholar
Index Terms
- Predictive Online Server Provisioning for Cost-Efficient IoT Data Streaming Across Collaborative Edges
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