物聯網 (IOT) 在近年來快速崛起,並被視為未來數年內最具影響 的技術之一。是一種透過將物體透過網路戶聯互通,實現智能化識 別和管理的計算概念。它的應用非常廣泛,包括交通管理,健康醫 療,無人駕駛車,智慧家電等等。在這篇論文中,我們利用結合穿 戴式裝置與雲端計算的技術,實作了一個即時監控分析病人健康的 物聯網應用服務系統。並探討系統中分散式串流計算的動態資源分 配問題。讓我們的系統可以在雲端平台上根據使用者和計算量的變 化作調整能,提升系統的計算成本效益以及效能保證。

IOT (Internet of Thing) rapid rise in recently years. It can be taken as one of the most influential technology in the future. It is a computational concept through the connection between physical object and Internet to realize intelligent identification and management. It has been widely used including transportation management, medical healthcare, driverless car and intelligent appliances. In this thesis, we use the technology in combination with wearable device and cloud computing, and im- plement an IOT application service which can monitor and analyze the health state of patient in real-time. We also discuss the problem of dynamic resource manage- ment in distributed stream computing system. Our system can adjust the resources according to the loading of computation from users to promote the cost-effectiveness and guarantee the performance simultaneously.

1 Introduction 4
2 Healthcare System 7
2.1 Frontendhealthcaredevice........................ 7
2.2 Backendcloudservice .......................... 8
2.3 ApacheStorm............................... 10
3 Motivation 12
3.1 TradeoffbetweenLatencyandResource ................ 12
3.2 ResourceAssignmentinDAGiscomplicated . . . . . . . . . . . . . 13
3.3 Loadingisunpredictable......................... 13
4 Resource Management System 15
4.1 Architecture................................ 16
4.1.1 Monitor.............................. 16
4.1.2 DataCollector .......................... 17
4.1.3 PolicyEngine........................... 18
4.1.4 DecisionEngine.......................... 19
5 Algorithm 20
5.1 OnlineControlAlgorithm ........................ 20
5.1.1 CriticalBoltFirst ........................ 21
5.1.2 CriticalPathFirst ........................ 21
5.2 OfflineControlAlgorithm ........................ 21
5.2.1 CriticalDifferenceDelayFirst.................. 22
5.2.2 CriticalProportionDelayFirst ................. 22
6 Evaluation 24
6.1 Environment ............................... 24
6.2 Experiment1:SimpleDAG ....................... 25
6.3 Experiment2:ComplexDAG ...................... 26
6.4 DynamicLoadingBoostAware ..................... 27
7 Related work
8 Conclusion

[1] Apache hadoop https://storm.apache.org.
[2] Apache Software Foundation Samza http://samza.incubator.apache.org/.
[3] D. J. Abadi, Y. Ahmad, M. Balazinska, U. Cetintemel, M. Cherniack, J.-H. Hwang, W. Lindner, A. Maskey, A. Rasin, E. Ryvkina, et al. The design of the borealis stream processing engine. In CIDR, volume 5, pages 277–289, 2005.
[4] D. J. Abadi, D. Carney, U. Çetintemel, M. Cherniack, C. Convey, S. Lee, M. Stonebraker, N. Tatbul, and S. Zdonik. Aurora: a new model and archi- tecture for data stream management. The VLDB Journal—The International Journal on Very Large Data Bases, 12(2):120–139, 2003.
[5] L. Atzori, A. Iera, and G. Morabito. The internet of things: A survey. Computer Networks, 54(15):2787 – 2805, 2010.
[6] N. Bui and M. Zorzi. Health care applications: A solution based on the internet of things. In Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL ’11, pages 131:1– 131:5, New York, NY, USA, 2011. ACM.
[7] M. Cherniack, H. Balakrishnan, M. Balazinska, D. Carney, U. Cetintemel, Y. Xing, and S. B. Zdonik. Scalable distributed stream processing. In CIDR, volume 3, pages 257–268, 2003.
[8] C. Doukas and I. Maglogiannis. Bringing iot and cloud computing towards pervasive healthcare. In Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), 2012 Sixth International Conference on, pages 922–926, July 2012.
[9] J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami. Internet of things (iot): A vision, architectural elements, and future directions. Future Generation Com- puter Systems, 29(7):1645 – 1660, 2013. Including Special sections: Cyber- enabled Distributed Computing for Ubiquitous Cloud and Network Services; Cloud Computing and Scientific Applications — Big Data, Scalable Analytics, and Beyond.
[10] D. Henriksson, Y. Lu, and T. Abdelzaher. Improved prediction for web server delay control. In Real-Time Systems, 2004. ECRTS 2004. Proceedings. 16th Euromicro Conference on, pages 61–68, June 2004.
[11] F. Junqueira and B. Reed. ZooKeeper: Distributed Process Coordination. ” O’Reilly Media, Inc.”, 2013.
[12] S. Kamburugamuve. Survey of distributed stream processing for large stream sources. Technical report, 2013.
[13] C. Lu, Y. Lu, T. F. Abdelzaher, J. Stankovic, S. H. Son, et al. Feedback control architecture and design methodology for service delay guarantees in web servers. Parallel and Distributed Systems, IEEE Transactions on, 17(9): 1014–1027, 2006.
[14] Y. Lu, T. Abdelzaher, C. Lu, L. Sha, and X. Liu. Feedback control with queueing-theoretic prediction for relative delay guarantees in web servers. In Real-Time and Embedded Technology and Applications Symposium, 2003. Pro- ceedings. The 9th IEEE, pages 208–217, May 2003.
[15] L. Neumeyer, B. Robbins, A. Nair, and A. Kesari. S4: Distributed stream computing platform. In Data Mining Workshops (ICDMW), 2010 IEEE Inter- national Conference on, pages 170–177. IEEE, 2010.
[16] J. Stankovic, T. He, T. Abdelzaher, M. Marley, G. Tao, S. Son, and C. Lu. Feedback control scheduling in distributed real-time systems. In Real-Time Systems Symposium, 2001. (RTSS 2001). Proceedings. 22nd IEEE, pages 59– 70, Dec 2001.
[17] M. Swan. Sensor mania! the internet of things, wearable computing, objective metrics, and the quantified self 2.0. Journal of Sensor and Actuator Networks, 1(3):217–253, 2012.
[18] J. Wei. How wearables intersect with the cloud and the internet of things : Con- siderations for the developers of wearables. Consumer Electronics Magazine, IEEE, 3(3):53–56, July 2014.