為了滿足感測應用的位置感知與即時反應需求，霧運算日漸受到重視。然而，
由於其有限的電池電量、有限的運算能力以及有限的通訊資源，可攜型嵌入式裝置
支援此類應用的需求深具挑戰性。因此，本論文提出了一種針對時間敏感之感測
應用的節能任務卸載策略以及任務卸載配置器，能夠有效權衡霧運算系統中的運算
成本及通訊成本，進而降低應用程式之反應時間和裝置之能量消耗；並提出了具有
反應時間排程性測試的即時排程器來服務霧運算應用。經由一連串實驗與小型霧運
算系統的驗證，實驗結果顯示本方法能夠有效節省能源消耗。

To meet the location awareness and real-time response of applications, fog
computing has received significant interests. However, it is challenging for
mobile embedded systems to support the demand of such applications due to
its limited resource of energy, computation, and communication. An energy-efficient offloading decision and a offloading dispatcher are presented that enables balancing the tradeoff between response time and energy consumption
with both communication and computation consideration. A run-time scheduler
with end-to-end latency schedulability test is presented to service timing
sensitive application. The evaluation results revealed that a considerable
amount of energy can be conserved using this framework, and its practicability was evidenced using a real-life case study.

[1] S. Lee and M. Annavaram, “Wireless body area networks: Where does energy go?,” in Workload Characterization (IISWC), 2012 IEEE International Symposium on, pp. 25–35, IEEE, 2012.
[2] M. M. Hassan, K. Lin, X. Yue, and J. Wan, “A multimedia healthcare data sharing approach through cloud-based body area network,” Future Generation Computer Systems, 2016.
[3] H. Kalantarian, C. Sideris, B. Mortazavi, N. Alshurafa, and M. Sarrafzadeh, “Dynamic computation offloading for low-power wearable health monitoring systems,” 2016.
[4] K. Kumar, J. Liu, Y.-H. Lu, and B. Bhargava, “A survey of computation offloading for mobile systems,” Mobile Networks and Applications, vol. 18, no. 1, pp. 129–140, 2013.
[5] K. Kumar and Y.-H. Lu, “Cloud computing for mobile users: Can offloading computation save energy?,” Computer, vol. 43, no. 4, pp. 51–56, 2010.
[6] E. Cuervo, A. Balasubramanian, D.-k. Cho, A. Wolman, S. Saroiu, R. Chandra, and P. Bahl, “Maui: making smartphones last longer with code offload,” in Proceedings of the 8th international conference on Mobile systems, applications, and services, pp. 49–62, ACM, 2010.
[7] S. Kosta, A. Aucinas, P. Hui, R. Mortier, and X. Zhang, “Thinkair: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading,” in INFOCOM, 2012 Proceedings IEEE, pp. 945–953, IEEE, 2012.
[8] R. Kemp, N. Palmer, T. Kielmann, and H. Bal, “Cuckoo: a computation offloading framework for smartphones,” in International Conference on Mobile Computing, Applications, and Services, pp. 59–79, Springer, 2010.
[9] A. Y. Ding, B. Han, Y. Xiao, P. Hui, A. Srinivasan, M. Kojo, and S. Tarkoma, “Enabling energy-aware collaborative mobile data offloading for smartphones,” in 2013 IEEE International Conference on Sensing, Communications and Networking (SECON), pp. 487–495, IEEE, 2013.
[10] A. Saarinen, M. Siekkinen, Y. Xiao, J. K. Nurminen, M. Kemppainen, and P. Hui, “Smartdiet: offloading popular apps to save energy,” ACM SIGCOMM Computer Communication Review, vol. 42, no. 4, pp. 297–298, 2012.
[11] A. Pathak, Y. C. Hu, M. Zhang, P. Bahl, and Y.-M. Wang, “Enabling automatic offloading of resource-intensive smartphone applications,” 2011.
[12] J. Flinn, D. Narayanan, and M. Satyanarayanan, “Self-tuned remote execution for pervasive computing,” in Hot Topics in Operating Systems, 2001. Proceedings of the Eighth Workshop on, pp. 61–66, IEEE, 2001.
[13] A. Cidon, T. M. London, S. Katti, C. Kozyrakis, and M. Rosenblum, “Mars: adaptive remote execution for multi-threaded mobile devices,” in Proceedings of the 3rd ACM SOSP Workshop on Networking, Systems, and Applications on Mobile Handhelds, p. 1, ACM, 2011.
[14] C. Ragona, F. Granelli, C. Fiandrino, D. Kliazovich, and P. Bouvry, “Energy-efficient computation offloading for wearable devices and smartphones in mobile cloud computing,” in 2015 IEEE Global Communications Conference (GLOBECOM), pp. 1–6, IEEE, 2015.
[15] F. A. Ali, P. Simoens, T. Verbelen, P. Demeester, and B. Dhoedt, “Mobile device power models for energy efficient dynamic offloading at runtime,” Journal of Systems and Software, vol. 113, pp. 173–187, 2016.
[16] B.-K. Huang, C.-C. Cheng, C.-H. Lin, and P.-C. Hsiu, “A cloud-based offloading service for computation-intensive mobile applications,” in Embedded and Real-Time Computing Systems and Applications (RTCSA), 2015 IEEE 21st International Conference on, pp. 80–89, IEEE, 2015.
[17] R. Kumari et al., “An efficient data offloading to cloud mechanism for smart healthcare sensors,” in Next Generation Computing Technologies (NGCT), 2015 1st International Conference on, pp. 90–95, IEEE, 2015.
[18] M. V. Barbera, S. Kosta, A. Mei, and J. Stefa, “To offload or not to offload? the bandwidth and energy costs ofmobile cloud computing,” in INFOCOM, 2013 Proceedings IEEE, pp. 1285–1293, IEEE, 2013.
[19] M. Akram and A. ElNahas, “Energy-aware offloading technique for mobile cloud computing,” in Future Internet of Things and Cloud (FiCloud), 2015 3rd International Conference on, pp. 349–356, IEEE, 2015.
[20] A. Mukherjee and D. De, “Low power offloading strategy for femto-cloud mobile network,” Engineering Science and Technology, an International Journal, vol. 19, no. 1, pp. 260–270, 2016.
[21] Y.-D. Lin, E. T.-H. Chu, Y.-C. Lai, and T.-J. Huang, “Time-and-energy aware computation offloading in handheld devices to coprocessors and clouds,” IEEE Systems Journal, vol. 9, no. 2, pp. 393–405, 2015.
[22] L. Xiang, S. Ye, Y. Feng, B. Li, and B. Li, “Ready, set, go: Coalesced offloading from mobile devices to the cloud,” in IEEE INFOCOM2014-IEEE Conference on Computer Communications, pp. 2373–2381, IEEE, 2014.
[23] A. Toma and J.-J. Chen, “Computation offloading for real-time systems,” in Proceedings of the 28th Annual ACM Symposium on Applied Computing, pp.1650–1651, ACM, 2013.
[24] J.-M. Chung, Y.-S. Park, J.-H. Park, and H. Cho, “Adaptive cloud offloading of augmented reality applications on smart devices for minimum energy consumption,” KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, vol. 9, no. 8, pp. 3090–3102, 2015.
[25] M. Firdhous, O. Ghazali, and S. Hassan, “Fog computing: Will it be the
future of cloud computing?,” The Third International Conference on Informatics & Applications (ICIA2014), 2014.
[26] F. Bonomi, R.Milito, P. Natarajan, and J. Zhu, “Fog computing: A platform for internet of things and analytics,” in Big Data and Internet of Things:A Roadmap for Smart Environments, pp. 169–186, Springer, 2014.
[27] T. H. Luan, L. Gao, Z. Li, Y. Xiang, G. Wei, and L. Sun, “Fog computing:Focusing on mobile users at the edge,” arXiv preprint arXiv:1502.01815, 2015.
[28] L. M. Vaquero and L. Rodero-Merino, “Finding your way in the fog:Towards a comprehensive definition of fog computing,” ACM SIGCOMM Computer Communication Review, vol. 44, no. 5, pp. 27–32, 2014.
[29] S. Yi, C. Li, and Q. Li, “A survey of fog computing: concepts, applications and issues,” in Proceedings of the 2015 Workshop onMobile Big Data, pp. 37–42, ACM, 2015.
[30] I. Stojmenovic, “Fog computing: A cloud to the ground support for smart
things and machine-to-machine networks,” in Telecommunication Networks and Applications Conference (ATNAC), 2014 Australasian, pp. 117–122, IEEE, 2014.
[31] K. Hong, D. Lillethun, U. Ramachandran, B. Ottenwぴalder, and B. Koldehofe, “Mobile fog: A programming model for large-scale applications on the internet of things,” in Proceedings of the second ACM SIGCOMM workshop
on Mobile cloud computing, pp. 15–20, ACM, 2013.
[32] H. Dubey, J. Yang, N. Constant, A. M. Amiri, Q. Yang, and K. Makodiya,
“Fog data: Enhancing telehealth big data through fog computing,” in Proceedings of the ASE BigData & SocialInformatics 2015, p. 14, ACM, 2015.
[33] D. Yao, C. Yu, H. Jin, and J. Zhou, “Energy efficient task scheduling in mobile cloud computing,” in IFIP International Conference on Network and
Parallel Computing, pp. 344–355, Springer, 2013.
[34] B. Li, Y. Pei, H.Wu, and B. Shen, “Heuristics to allocate high-performance cloudlets for computation offloading in mobile ad hoc clouds,” The Journal of Supercomputing, vol. 71, no. 8, pp. 3009–3036, 2015.
[35] J. K. Zao, T.-T. Gan, C.-K. You, C.-E. Chung, Y.-T. Wang, S. J. R. M´endez, T. Mullen, C. Yu, C. Kothe, C.-T. Hsiao, et al., “Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology,” Frontiers in human neuroscience, vol. 8, 2014.
[36] P. Edholm, “End node pacing for qos and bandwidthmanagement,” July 29
2003. US Patent 6,600,721.
[37] D.-g. Zhang, K. Zheng, T. Zhang, and X. Wang, “A novel multicast routing method with minimum transmission for wsn of cloud computing service,” Soft Computing, vol. 19, no. 7, pp. 1817–1827, 2015.
[38] Y.-C. Kao and Y.-S. Chen, “Data-locality-aware mapreduce real-time
scheduling framework,” Journal of Systems and Software, vol. 112, pp. 65–
77, 2016.
[39] L. T. Phan, Z. Zhang, Q. Zheng, B. T. Loo, and I. Lee, “An empirical analysis of scheduling techniques for real-time cloud-based data processing,” in Service-Oriented Computing and Applications (SOCA), 2011 IEEE International Conference on, pp. 1–8, IEEE, 2011.
[40] M. F. Cloutier, C. Paradis, and V.M.Weaver, “A raspberry pi cluster instrumented for fine-grained power measurement,” Electronics, vol. 5, no. 4, p. 61, 2016.
[41] X. Zhu, S. Han, P.-C. Huang, A. K.Mok, and D. Chen, “Mbstar: A real-time communication protocol for wireless body area networks,” in 2011 23rd
Euromicro Conference on Real-Time Systems, pp. 57–66, IEEE, 2011.
[42] B. Chen and D. Pompili, “Transmission of patient vital signs using wireless body area networks,”Mobile Networks and Applications, vol. 16, no. 6, pp. 663–682, 2011.
[43] H. B. Elhadj, J. Elias, L. Chaari, and L. Kamoun, “Multi-attribute decision making handover algorithm for wireless body area networks,” Computer Communications, vol. 81, pp. 97–108, 2016.
[44] T. N. Gia, M. Jiang, A.-M. Rahmani, T. Westerlund, P. Liljeberg, and
H. Tenhunen, “Fog computing in healthcare internet of things: A case study on ecg feature extraction,” in Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM), 2015 IEEE International Conference on, pp. 356–363, IEEE, 2015.
[45] D-Link Systems, Inc., Wireless AC1000 Dual Band Cloud Router, DIR-
820L, 2017.
[46] J. Leverich and C. Kozyrakis, “On the energy (in) efficiency of hadoop clusters,” ACM SIGOPS Operating Systems Review, vol. 44, no. 1, pp. 61–65,
2010.
[47] Keysight Technologies, Keysight 34461A/34460A Digital Multimeters,
2014.