Fog Computing in IoT Smart Environments via Named Data Networking: A Study on Service Orchestration Mechanisms

[EN] By offering low-latency and context-aware services, fog computing will have a peculiar role in the deployment of Internet of Things (IoT) applications for smart environments. Unlike the conventional remote cloud, for which consolidated architectures and deployment options exist, many design and implementation aspects remain open when considering the latest fog computing paradigm. In this paper, we focus on the problems of dynamically discovering the processing and storage resources distributed among fog nodes and, accordingly, orchestrating them for the provisioning of IoT services for smart environments. In particular, we show how these functionalities can be effectively supported by the revolutionary Named Data Networking (NDN) paradigm. Originally conceived to support named content delivery, NDN can be extended to request and provide named computation services, with NDN nodes acting as both content routers and in-network service executors. To substantiate our analysis, we present an NDN fog computing framework with focus on a smart campus scenario, where the execution of IoT services is dynamically orchestrated and performed by NDN nodes in a distributed fashion. A simulation campaign in ndnSIM, the reference network simulator of the NDN research community, is also presented to assess the performance of our proposal against state-of-the-art solutions. Results confirm the superiority of the proposal in terms of service provisioning time, paid at the expenses of a slightly higher amount of traffic exchanged among fog nodes.This research was partially funded by the Italian Government under grant PON ARS01_00836 for the COGITO (A COGnItive dynamic sysTem to allOw buildings to learn and adapt) PON Project.Amadeo, M.; Ruggeri, G.; Campolo, C.; Molinaro, A.; Loscri, V.; Tavares De Araujo Cesariny Calafate, CM. (2019). Fog Computing in IoT Smart Environments via Named Data Networking: A Study on Service Orchestration Mechanisms. Future Internet. 11(11):1-21. https://doi.org/10.3390/fi11110222S1211111Lee, I., & Lee, K. (2015). The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58(4), 431-440. doi:10.1016/j.bushor.2015.03.008Cicirelli, F., Guerrieri, A., Spezzano, G., Vinci, A., Briante, O., Iera, A., & Ruggeri, G. (2018). Edge Computing and Social Internet of Things for Large-Scale Smart Environments Development. IEEE Internet of Things Journal, 5(4), 2557-2571. doi:10.1109/jiot.2017.2775739Chiang, M., & Zhang, T. (2016). Fog and IoT: An Overview of Research Opportunities. IEEE Internet of Things Journal, 3(6), 854-864. doi:10.1109/jiot.2016.2584538Openfog Consortiumhttp://www.openfogconsortium.org/Zhang, L., Afanasyev, A., Burke, J., Jacobson, V., claffy, kc, Crowley, P., … Zhang, B. (2014). Named data networking. ACM SIGCOMM Computer Communication Review, 44(3), 66-73. doi:10.1145/2656877.2656887Amadeo, M., Ruggeri, G., Campolo, C., & Molinaro, A. (2019). IoT Services Allocation at the Edge via Named Data Networking: From Optimal Bounds to Practical Design. IEEE Transactions on Network and Service Management, 16(2), 661-674. doi:10.1109/tnsm.2019.2900274ndnSIM 2.0: A New Version of the NDN Simulator for NS-3https://www.researchgate.net/profile/Spyridon_Mastorakis/publication/281652451_ndnSIM_20_A_new_version_of_the_NDN_simulator_for_NS-3/links/5b196020a6fdcca67b63660d/ndnSIM-20-A-new-version-of-the-NDN-simulator-for-NS-3.pdfAhlgren, B., Dannewitz, C., Imbrenda, C., Kutscher, D., & Ohlman, B. (2012). A survey of information-centric networking. IEEE Communications Magazine, 50(7), 26-36. doi:10.1109/mcom.2012.6231276NFD Developer’s Guidehttps://named-data.net/wp-content/uploads/2016/03/ndn-0021-diff-5..6-nfd-developer-guide.pdfPiro, G., Amadeo, M., Boggia, G., Campolo, C., Grieco, L. A., Molinaro, A., & Ruggeri, G. (2019). Gazing into the Crystal Ball: When the Future Internet Meets the Mobile Clouds. IEEE Transactions on Cloud Computing, 7(1), 210-223. doi:10.1109/tcc.2016.2573307Zhang, G., Li, Y., & Lin, T. (2013). Caching in information centric networking: A survey. Computer Networks, 57(16), 3128-3141. doi:10.1016/j.comnet.2013.07.007Yi, C., Afanasyev, A., Moiseenko, I., Wang, L., Zhang, B., & Zhang, L. (2013). A case for stateful forwarding plane. Computer Communications, 36(7), 779-791. doi:10.1016/j.comcom.2013.01.005Amadeo, M., Briante, O., Campolo, C., Molinaro, A., & Ruggeri, G. (2016). Information-centric networking for M2M communications: Design and deployment. Computer Communications, 89-90, 105-116. doi:10.1016/j.comcom.2016.03.009Tourani, R., Misra, S., Mick, T., & Panwar, G. (2018). Security, Privacy, and Access Control in Information-Centric Networking: A Survey. IEEE Communications Surveys & Tutorials, 20(1), 566-600. doi:10.1109/comst.2017.2749508Ndn-ace: Access Control for Constrained Environments over Named Data Networkinghttp://new.named-data.net/wp-content/uploads/2015/12/ndn-0036-1-ndn-ace.pdfZhang, Z., Yu, Y., Zhang, H., Newberry, E., Mastorakis, S., Li, Y., … Zhang, L. (2018). An Overview of Security Support in Named Data Networking. IEEE Communications Magazine, 56(11), 62-68. doi:10.1109/mcom.2018.1701147Cisco White Paperhttps://www.cisco.com/c/dam/en_us/solutions/trends/iot/docs/computing-overview.pdfAazam, M., Zeadally, S., & Harras, K. A. (2018). Deploying Fog Computing in Industrial Internet of Things and Industry 4.0. IEEE Transactions on Industrial Informatics, 14(10), 4674-4682. doi:10.1109/tii.2018.2855198Hou, X., Li, Y., Chen, M., Wu, D., Jin, D., & Chen, S. (2016). Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures. IEEE Transactions on Vehicular Technology, 65(6), 3860-3873. doi:10.1109/tvt.2016.2532863Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., … Jue, J. P. (2019). All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture, 98, 289-330. doi:10.1016/j.sysarc.2019.02.009Baktir, A. C., Ozgovde, A., & Ersoy, C. (2017). How Can Edge Computing Benefit From Software-Defined Networking: A Survey, Use Cases, and Future Directions. IEEE Communications Surveys & Tutorials, 19(4), 2359-2391. doi:10.1109/comst.2017.2717482Duan, Q., Yan, Y., & Vasilakos, A. V. (2012). A Survey on Service-Oriented Network Virtualization Toward Convergence of Networking and Cloud Computing. IEEE Transactions on Network and Service Management, 9(4), 373-392. doi:10.1109/tnsm.2012.113012.120310Amadeo, M., Campolo, C., & Molinaro, A. (2016). NDNe: Enhancing Named Data Networking to Support Cloudification at the Edge. IEEE Communications Letters, 20(11), 2264-2267. doi:10.1109/lcomm.2016.2597850Krol, M., Marxer, C., Grewe, D., Psaras, I., & Tschudin, C. (2018). Open Security Issues for Edge Named Function Environments. IEEE Communications Magazine, 56(11), 69-75. doi:10.1109/mcom.2018.170111711801-2:2017 Information Technology—Generic Cabling for Customer Premiseshttps://www.iso.org/standard/66183.htm

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices, at the edge of the current network. To achieve higher performance in this new paradigm one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis is used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource, and less extensive towards the estimation, discovery and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of non-functional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless Communications and Mobile Computing journa

Mobility-aware hierarchical fog computing framework for Industrial Internet of Things (IIoT)

The Industrial Internet of Things (IIoTs) is an emerging area that forms the collaborative environment for devices to share resources. In IIoT, many sensors, actuators, and other devices are used to improve industrial efficiency. As most of the devices are mobile; therefore, the impact of mobility can be seen in terms of low-device utilization. Thus, most of the time, the available resources are underutilized. Therefore, the inception of the fog computing model in IIoT has reduced the communication delay in executing complex tasks. However, it is not feasible to cover the entire region through fog nodes; therefore, fog node selection and placement is still the challenging task. This paper proposes a multi-level hierarchical fog node deployment model for the industrial environment. Moreover, the scheme utilized the IoT devices as a fog node; however, the selection depends on energy, path/location, network properties, storage, and available computing resources. Therefore, the scheme used the location-aware module before engaging the device for task computation. The framework is evaluated in terms of memory, CPU, scalability, and system efficiency; also compared with the existing approach in terms of task acceptance rate. The scheme is compared with xFogSim framework that is capable to handle workload upto 1000 devices. However, the task acceptance ratio is higher in the proposed framework due to its multi-tier model. The workload acceptance ratio is 85% reported with 3000 devices; whereas, in xFogsim the ratio is reduced to approx. 68%. The primary reason for high workload acceptation is that the proposed solution utilizes the unused resources of the user devices for computations