Top

Information Systems Frontiers

Published in:

29-06-2022

Security, Cost and Energy Aware Scheduling of Real-Time IoT Workflows in a Mist Computing Environment

Authors: Georgios L. Stavrinides, Helen D. Karatza

Published in: Information Systems Frontiers | Issue 4/2024

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

The rapid expansion of the Internet of Things (IoT) led to the emergence of new computing paradigms, such as mist and fog computing, in order to tackle the problem of transferring vast volumes of data to remote cloud data centers. In this paper, we propose a security, cost and energy aware scheduling heuristic for real-time workflow jobs that process IoT data with various security requirements. The environment under study is a four-tier architecture, consisting of IoT, mist, fog and cloud layers. The resources in the mist, fog and cloud tiers are considered to be heterogeneous. The proposed scheduling approach is compared to a baseline strategy, which is security aware, but not cost and energy aware. The performance of both heuristics is evaluated through extensive simulation experiments, under different values of security level probabilities for the initial IoT input data of the entry tasks of the workflow jobs. The simulation results reveal that the proposed approach, not only provides a better Quality of Service (QoS) compared to the baseline strategy, but it also achieves monetary cost and energy savings.

previous article Cloud, IoT and Data Science

next article Analysis of the Cryptographic Algorithms in IoT Communications

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Al-Bzoor, M., Al-assem, E., Alawneh, L., & Jararweh, Y. (2021). Autonomous underwater vehicles support for enhanced performance in the Internet of underwater things. Transactions on Emerging Telecommunications Technologies, 32(3), e4225. https://doi.org/10.1002/ett.4225CrossRef

Aslanpour, M. S., Gill, S. S., & Toosi, A. N. (2020). Performance evaluation metrics for cloud, fog and edge computing: a review, taxonomy, benchmarks and standards for future research. Internet of Things, 12, 100273. https://doi.org/10.1016/j.iot.2020.100273CrossRef

Auluck, N., Rana, O., Nepal, S., Jones, A., & Singh, A. (2019). Scheduling real time security aware tasks in fog networks. IEEE Transactions on Services Computing, 1–14. https://doi.org/10.1109/TSC.2019.2914649

Awan, I., Younas, M., & Benbernou, S. (2021). Convergence of cloud, Internet of Things, and big data: new platforms and applications. Concurrency and Computation: Practice and Experience, 33(23), e6667. https://doi.org/10.1002/cpe.6668CrossRef

Barik, R. K., Misra, C., Lenka, R. K., Dubey, H., & Mankodiya, K. (2019). Hybrid mist-cloud systems for large scale geospatial big data analytics and processing: opportunities and challenges. Arabian Journal of Geosciences, 12(2), 1–15. https://doi.org/10.1007/s12517-018-4104-3

Beloglazov, A., Abawajy, J., & Buyya, R. (2012). Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Generation Computer Systems, 28(5), 755–768. https://doi.org/10.1016/j.future.2011.04.017CrossRef

Bhattacharya, S., Senapati, S., Soy, S. K., Misra, C., & Barik, R. K. (2020). Performance analysis of enhanced mist-assisted cloud computing model for healthcare system. In Proceedings of the 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA’20), (pp. 1–5). https://doi.org/10.1109/ICCSEA49143.2020.9132914

Buttazzo, G. C. (2011). Hard real-time computing systems: Predictable scheduling algorithms and applications, 3rd edn. New York: Springer. https://doi.org/10.1007/978-1-4614-0676-1CrossRef

Chen, Y. (2020). Service-oriented computing and system integration: Software, IoT, big data, and AI as services, 7th edn. Dubuque: Kendall Hunt Publishing.

Dogo, E. M., Salami, A. F., Aigbavboa, C. O., & Nkonyana, T. (2019). Taking cloud computing to the extreme edge: a review of mist computing for smart cities and industry 4.0 in Africa. In F. Al-Turjman (Ed.) Edge computing: from hype to reality (1st ed., pp. 107–132). https://doi.org/10.1007/978-3-319-99061-3_7. Springer.

European Parliament, E. U. (2016). Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). Official Journal of the European Union. Retrieved from: https://eur-lex.europa.eu/eli/reg/2016/679/oj. Accessed: 2021-11-20.

Felicetti, C., Furfaro, A., Saccà, D, Vatalaro, M., Lanuzza, M., & Crupi, F. (2019). Making IoT services accountable: a solution based on blockchain and physically unclonable functions. In Proceedings of the 12th International Conference on Internet and Distributed Computing Systems (IDCS’19), (pp. 294–305). https://doi.org/10.1007/978-3-030-34914-1_28

Furfaro, A., Piccolo, A., Parise, A., Argento, L., & Saccà, D (2018). A cloud-based platform for the emulation of complex cybersecurity scenarios. Future Generation Computer Systems, 89, 791–803. https://doi.org/10.1016/j.future.2018.07.025CrossRef

Hensh, F., Gupta, M., & Nene, M. J. (2021). Mist-Edge-Cloud (MEC) Computing: an integrated computing architecture. In Proceedings of the Second International Conference on Electronics and Sustainable Communication Systems (ICESC’21), (pp. 1035–1040). https://doi.org/10.1109/ICESC51422.2021.9532929

Hosen, A. S. M. S., Sharma, P. K., & Cho, G. H. (2021a). MSRM-IoT: a reliable resource management for cloud, fog and mist assisted IoT networks. IEEE Internet of Things Journal, 1–11. https://doi.org/10.1109/JIOT.2021.3090779

Hosen, A. S. M. S., Sharma, P. K., Ra, I. H., & Cho, G. H. (2021b). SPTM-EC: a security and privacy-preserving task management in edge computing for IIoT. IEEE Transactions on Industrial Informatics, 1–9. https://doi.org/10.1109/TII.2021.3123260

Hsu, P. F. (2022). A deeper look at cloud adoption trajectory and dilemma. Information Systems Frontiers, 24(1), 177–194. https://doi.org/10.1007/s10796-020-10049-wCrossRef

Ijaz, S., Munir, E. U., Ahmad, S. G., Rafique, M. M., & Rana, O. F. (2021). Energy-makespan optimization of workflow scheduling in fog-cloud computing. Computing, 103(9), 2033–2059. https://doi.org/10.1007/s00607-021-00930-0

Iorga, M., Feldman, L., Barton, R., Martin, M. J., Goren, N., & Mahmoudi, C. (2018). Fog computing conceptual model (Tech. Rep. No. 500-325). National Institute of Standards and Technology, U.S. Department of Commerce. https://doi.org/10.6028/NIST.SP.500-325

Islam, M. S. U., Kumar, A., & Hu, Y. C. (2021). Context-aware scheduling in fog computing: a survey, taxonomy, challenges and future directions. Journal of Network and Computer Applications, 180, 103008. https://doi.org/10.1016/j.jnca.2021.103008CrossRef

Jaddoa, A., Sakellari, G., Panaousis, E., Loukas, G., & Sarigiannidis, P. G. (2020). Dynamic decision support for resource offloading in heterogeneous Internet of Things environments. Simulation Modelling Practice and Theory, 101, 102019. https://doi.org/10.1016/j.simpat.2019.102019CrossRef

Karatza, H. D., & Hilzer, R. C. (2003). Parallel job scheduling in homogeneous distributed systems. Simulation, 79(5–6), 287–298. https://doi.org/10.1177/0037549703037148CrossRef

Kim, K. H., Buyya, R., & Kim, J. (2007). Power aware scheduling of bag-of-tasks applications with deadline constraints on DVS-enabled clusters. In Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGRID’07), (pp. 541–548). https://doi.org/10.1109/CCGRID.2007.85

Lapegna, M., Balzano, W., Meyer, N., & Romano, D. (2021). Clustering algorithms on low-power and high-performance devices for edge computing environments. Sensors, 21(16), 5395. https://doi.org/10.3390/s21165395CrossRef

Liyanage, M., Chang, C., & Srirama, S. N. (2018). Adaptive mobile web server framework for mist computing in the Internet of Things. International Journal of Pervasive Computing and Communications, 14(3/4), 247–267. https://doi.org/10.1108/IJPCC-D-18-00023CrossRef

Loukas, G., Yoon, Y., Sakellari, G., Vuong, T., & Heartfield, R. (2017). Computation offloading of a vehicle’s continuous intrusion detection workload for energy efficiency and performance. Simulation Modelling Practice and Theory, 73, 83–94. https://doi.org/10.1016/j.simpat.2016.08.005CrossRef

Luo, Q., Hu, S., Li, C., Li, G., & Shi, W. (2021). Resource scheduling in edge computing: a survey. IEEE Communications Surveys & Tutorials, 23(4), 2131–2165. https://doi.org/10.1109/COMST.2021.3106401CrossRef

Neves, N., Sebastião, N., Matos, D., Tomás, P, Flores, P., & Roma, N. (2015). Multicore SIMD ASIP for next-generation sequencing and alignment biochip platforms. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 23(7), 1287–1300. https://doi.org/10.1109/TVLSI.2014.2333757CrossRef

OpenFog. (2016). OpenFog Architecture Overview (Tech. Rep. No. OPFWP001.0216). Boston: OpenFog Consortium Architecture Working Group.

Panda, S. K., Gupta, I., & Jana, P. K. (2019). Task scheduling algorithms for multi-cloud systems: allocation-aware approach. Information Systems Frontiers, 21(2), 241–259. https://doi.org/10.1007/s10796-017-9742-6CrossRef

Pisani, F., de Oliveira, F. M. C., de Souza Gama, E., Immich, R., Bittencourt, L. F., & Borin, E. (2020). Fog computing on constrained devices: paving the way for the future IoT. In F. Xhafa, & A. K. Sangaiah (Eds.) Advances in Edge Computing: Massive Parallel Processing and Applications (1st ed., vol. 35, pp. 22–60). https://doi.org/10.3233/APC200003. IOS Press.

Preuveneers, D., Garofalo, G., & Joosen, W. (2021). Cloud and edge based data analytics for privacy-preserving multi-modal engagement monitoring in the classroom. Information Systems Frontiers, 23(1), 151–164. https://doi.org/10.1007/s10796-020-09993-4CrossRef

Rahbari, D., Kabirzadeh, S., & Nickray, M. (2017). A security aware scheduling in fog computing by hyper heuristic algorithm. In Proceedings of the 3rd Iranian Conference on Intelligent Systems and Signal Processing (ICSPIS’17), (pp. 87–92). https://doi.org/10.1109/ICSPIS.2017.8311595

Ren, W., Tong, X., Du, J., Wang, N., Li, S., Min, G., & Zhao, Z. (2021). Privacy enhancing techniques in the Internet of Things using data anonymisation. Information Systems Frontiers, 1–12. https://doi.org/10.1007/s10796-021-10116-w

Ridha Al-Khafaji, H. M., Saleh Alomari, E., & Majdi, H. S. (2019). Secured environment for cloud integrated fog and mist architecture. In Proceedings of the 2019 IEEE International Conference on Electrical Engineering and Photonics (EExPolytech’19), (pp. 112–116). https://doi.org/10.1109/EExPolytech.2019.8906885

Shah-Mansouri, H., & Wong, V. W. S. (2018). Hierarchical fog-cloud computing for IoT systems: a computation offloading game. IEEE Internet of Things Journal, 5(4), 3246–3257. https://doi.org/10.1109/JIOT.2018.2838022

Stavrinides, G. L., & Karatza, H. D. (2017). Simulation-based performance evaluation of an energy-aware heuristic for the scheduling of HPC applications in large-scale distributed systems. In Proceedings of the 8th ACM/SPEC International Conference on Performance Engineering (ICPE’17), 3rd International Workshop on Energy-Aware Simulation (ENERGY-SIM’17), (pp. 49–54). https://doi.org/10.1145/3053600.3053611

Stavrinides, G. L., & Karatza, H. D. (2018a). Energy-aware scheduling of real-time workflow applications in clouds utilizing DVFS and approximate computations. In Proceedings of the IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud’18), (pp. 33–40). https://doi.org/10.1109/FiCloud.2018.00013

Stavrinides, G. L., & Karatza, H. D. (2018b). The impact of workload variability on the energy efficiency of large-scale heterogeneous distributed systems. Simulation Modelling Practice and Theory, 89, 135–143. https://doi.org/10.1016/j.simpat.2018.09.013

Stavrinides, G. L., & Karatza, H. D. (2019a). Cost-effective utilization of complementary cloud resources for the scheduling of real-time workflow applications in a fog environment. In Proceedings of the 7th International Conference on Future Internet of Things and Cloud (FiCloud’19), (pp. 1–8). https://doi.org/10.1109/FiCloud.2019.00009

Stavrinides, G. L., & Karatza, H. D. (2019b). An energy-efficient, QoS-aware and cost-effective scheduling approach for real-time workflow applications in cloud computing systems utilizing DVFS and approximate computations. Future Generation Computer Systems, 96, 216–226. https://doi.org/10.1016/j.future.2019.02.019

Stavrinides, G. L., & Karatza, H. D. (2019c). A hybrid approach to scheduling real-time IoT workflows in fog and cloud environments. Multimedia Tools and Applications, 78(17), 24639–24655. https://doi.org/10.1007/s11042-018-7051-9

Stavrinides, G. L., & Karatza, H. D. (2020a). Orchestration of real-time workflows with varying input data locality in a heterogeneous fog environment. In Proceedings of the Fifth International Conference on Fog and Mobile Edge Computing (FMEC’20), (pp. 202–209). https://doi.org/10.1109/FMEC49853.2020.9144824

Stavrinides, G. L., & Karatza, H. D. (2020b). Scheduling real-time bag-of-tasks applications with approximate computations in SaaS clouds. Concurrency and Computation: Practice and Experience, 32(1), e4208. https://doi.org/10.1002/cpe.4208

Stavrinides, G. L., & Karatza, H. D. (2021a). Cost-aware cloud bursting in a fog-cloud environment with real-time workflow applications. Concurrency and Computation: Practice and Experience, 33(23), e5850. https://doi.org/10.1002/cpe.5850

Stavrinides, G. L., & Karatza, H. D. (2021b). Dynamic scheduling of bags-of-tasks with sensitive input data and end-to-end deadlines in a hybrid cloud. Multimedia Tools and Applications, 80(11), 16781–16803. https://doi.org/10.1007/s11042-020-08974-8

Stavrinides, G. L., & Karatza, H. D. (2021c). Orchestrating real-time IoT workflows in a fog computing environment utilizing partial computations with end-to-end error propagation. Cluster Computing, 24(4), 3629–3650. https://doi.org/10.1007/s10586-021-03327-y

Stavrinides, G. L., & Karatza, H. D. (2021d). Security and cost aware scheduling of real-time IoT workflows in a mist computing environment. In Proceedings of the 8th International Conference on Future Internet of Things and Cloud (FiCloud’21), (pp. 34–41). https://doi.org/10.1109/FiCloud49777.2021.00013

Stavrinides, G. L., & Karatza, H. D. (2022). Workload scheduling in fog and cloud environments: emerging concepts and research directions. In P. Nicopolitidis, S. Misra, L. T. Yang, B. Zeigler, & Z. Ning (Eds.) Advances in Computing, Informatics, Networking and Cybersecurity (1st ed., vol. 289, pp. 3–32). https://doi.org/10.1007/978-3-030-87049-2_1. Springer.

Suárez-Albela, M, Fraga-Lamas, P., Castedo, L., & Fernández-Caramés, T. M. (2019). Clock frequency impact on the performance of high-security cryptographic cipher suites for energy-efficient resource-constrained IoT devices. Sensors, 19(1), 15:1–15:16. https://doi.org/10.3390/s19010015

Sujana, J. A. J., Geethanjali, M., Raj, R. V., & Revathi, T. (2019). Trust model based scheduling of stochastic workflows in cloud and fog computing. In H. Das, R. K. Barik, H. Dubey, & D. S. Roy (Eds.) Cloud Computing for Geospatial Big Data Analytics: Intelligent Edge, Fog and Mist Computing (1st ed., pp. 29–54). https://doi.org/10.1007/978-3-030-03359-02. Springer.

Toor, A., Islam, S. U., Sohail, N., Akhunzada, A., Boudjadar, J., Khattak, H. A., Din, I. U., & Rodrigues, J. J. P. C. (2019). Energy and performance aware fog computing: a case of DVFS and green renewable energy. Future Generation Computer Systems, 101, 1112–1121. https://doi.org/10.1016/j.future.2019.07.010

Tripathy, S. S., Barik, R. K., & Roy, D. S. (2020). Secure-M2FBalancer: a secure mist to fog computing-based distributed load balancing framework for smart city application. In Proceedings of the 4th International Conference on Communication, Device and Networking (ICCDN’20), (pp. 277–285). https://doi.org/10.1007/978-981-16-2911-2_30

Wen, Y., Wang, Z., Zhang, Y., Liu, J., Cao, B., & Chen, J. (2019). Energy and cost aware scheduling with batch processing for instance-intensive IoT workflows in clouds. Future Generation Computer Systems, 101, 39–50. https://doi.org/10.1016/j.future.2019.05.046CrossRef

Zhou, Z., Abawajy, J. H., Li, F., Hu, Z., Chowdhury, M. U., Alelaiwi, A., & Li, K. (2018). Fine-grained energy consumption model of servers based on task characteristics in cloud data center. IEEE Access, 6, 27080–27090. https://doi.org/10.1109/ACCESS.2017.2732458CrossRef

Zhu, J., Hu, J., Zhang, M., Chen, Y., & Bi, S. (2020). A fog computing model for implementing motion guide to visually impaired. Simulation Modelling Practice and Theory, 101, 102015. https://doi.org/10.1016/j.simpat.2019.102015CrossRef

Title: Security, Cost and Energy Aware Scheduling of Real-Time IoT Workflows in a Mist Computing Environment
Authors: Georgios L. Stavrinides
Helen D. Karatza
Publication date: 29-06-2022
Publisher: Springer US
Published in: Information Systems Frontiers / Issue 4/2024
Print ISSN: 1387-3326
Electronic ISSN: 1572-9419
DOI: https://doi.org/10.1007/s10796-022-10304-2

Impulsive Social Shopping in Social Commerce Platforms: The Role of Perceived Proximity

Throwing Good Money After Bad: Risk Mitigation Strategies in the P2P Lending Platforms

Open Access

Analysis of the Cryptographic Algorithms in IoT Communications

Open Access

TGV: A Visualization Tool for Temporal Property Graph Databases

From Caterpillar to Butterfly: Social Media Engagement of the Socially Excluded Third-Gender People in Bangladesh

Open Access

Springer Professional

Security, Cost and Energy Aware Scheduling of Real-Time IoT Workflows in a Mist Computing Environment

Abstract

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 4/2024

Impulsive Social Shopping in Social Commerce Platforms: The Role of Perceived Proximity

Throwing Good Money After Bad: Risk Mitigation Strategies in the P2P Lending Platforms

Analysis of the Cryptographic Algorithms in IoT Communications

TGV: A Visualization Tool for Temporal Property Graph Databases

From Caterpillar to Butterfly: Social Media Engagement of the Socially Excluded Third-Gender People in Bangladesh

HRL-Edge-Cloud: Multi-Resource Allocation in Edge-Cloud based Smart-StreetScape System using Heuristic Reinforcement Learning

Premium Partner

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Impulsive Social Shopping in Social Commerce Platforms: The Role of Perceived Proximity

Throwing Good Money After Bad: Risk Mitigation Strategies in the P2P Lending Platforms

Analysis of the Cryptographic Algorithms in IoT Communications

TGV: A Visualization Tool for Temporal Property Graph Databases

From Caterpillar to Butterfly: Social Media Engagement of the Socially Excluded Third-Gender People in Bangladesh

HRL-Edge-Cloud: Multi-Resource Allocation in Edge-Cloud based Smart-StreetScape System using Heuristic Reinforcement Learning