Leveraging Constrained Devices for Custom Code Execution in the Internet of Things
With the ever-growing scale of the IoT, transmitting a massive volume of sensor data through the network will be too taxing. However, it will be challenging to include resource-constrained IoT devices as processing nodes in the fog computing hierarchy. To allow the execution of custom code sent by users on these devices, which are too limited for many current tools, we developed a platform called LibMiletusCOISA (LMC). Moreover, we created two models where the user can choose a cost metric (e.g., energy consumption) and then use it to decide whether to execute their code on the cloud or on the device that collected the data. We employed these models to characterize different scenarios and simulate future situations where changes in the technology can impact this decision.
Auler, R., Millani, C. E., Brisighello, A., Linhares, A., and Borin, E. (2017). Handling IoT platform heterogeneity with COISA, a compact OpenISA virtual platform. Concurr. Comp.-Pract. E., 29(22):e3932.
Bittencourt, L., Immich, R., Sakellariou, R., Fonseca, N., Madeira, E., Curado, M., Villas,L., da Silva, L., Lee, C., and Rana, O. (2018). The Internet of Things, Fog and Cloud continuum: Integration and challenges. Internet of Things, 3-4:134-155.
Bonomi, F., Milito, R., Zhu, J., and Addepalli, S. (2012). Fog Computing and Its Role in the Internet of Things. In Proc. MCC "12, pages 13-16.
Bormann, C., Ersue, M., and Keranen, A. (2014). Terminology for Constrained-Node Networks. Technical report, Internet Engineering Task Force.
Cisco Systems (2019). Introduction to IOx. https://developer.cisco.com/docs/iox/. Accessed: May 01, 2019.
Deng, R., Lu, R., Lai, C., Luan, T. H., and Liang, H. (2016). Optimal Workload Allocation in Fog-Cloud Computing Toward Balanced Delay and Power Consumption. IEEE Internet Things, 3(6):1171-1181.
FogHorn Systems (2019). Real-Time Edge Intelligence for Industrial IoT. www.foghorn.io/. Accessed: May 01, 2019.
Gurun, S., Krintz, C., and Wolski, R. (2004). NWSLite: A Light-Weight Prediction Utility for Mobile Devices. In Proc. MobiSys "04, pages 2-11.
Iorga, M., Feldman, L., Barton, R., Martin, M. J., Goren, N., and Mahmoudi, C. (2018). Fog Computing Conceptual Model - Recommendations of the National Institute of Standards and Technology. Technical report, National Institute of Standards and Technology.
Jayaraman, P. P., Gomes, J. B., Nguyen, H. L., Abdallah, Z. S., Krishnaswamy, S., and Zaslavsky, A. (2014). CARDAP: A Scalable Energy-Efficient Context Aware Distributed Mobile Data Analytics Platform for the Fog. In Proc. ADBIS "14, pages 192-206.
Liu, L., Chang, Z., Guo, X., Mao, S., and Ristaniemi, T. (2018). Multiobjective Optimization for Computation Offloading in Fog Computing. IEEE Internet Things, 5(1):283—294.
Lucero, S. (2016). IoT platforms: enabling the Internet of Things. Technical report, IHS Technology.
MotorolaMobilityLLC (2019). LibMiletus - IoT prototyping made easy! Accessed: May 02, 2019.
Neto, J. L. D., Yu, S.-Y., Macedo, D. F., Nogueira, J. M. S., Langar, R., and Secci,S. (2018). ULOOF: A User Level Online Offloading Framework for Mobile Edge Computing. IEEE T. Mobile Comput., 17(11):2660-2674.
Pisani, F., Martins do Rosario, V., and Borin, E. (2019). Fog vs. Cloud Computing: Should I Stay or Should 1 Go? Future Internet, 11(2).
Saint, A. (2015). Where next for the Internet of Things? 10(1):72-75.
Williams, S., Waterman, A., and Patterson, D. (2009). Roofline: An Insightful Visual Performance Model for Multicore Architectures. Commun. ACM, 52(4):65-76.
Xu, J. and Ren, S. (2016). Online Learning for Offloading and Autoscaling in Renewable-Powered Mobile Edge Computing. In Proc. GLOBECOM '16.