DOI : https://doi.org/10.18517/ijaseit.14.6.15956

Enhanced Chaos-Driven Automation: A Unique Resilience Testing Toolkit for Cloud-Native IoT Networks

Yu Weiyuan (1), Mohd Hafeez Osman (2), Rodziah Atan (3), Wan Nurhayati Wan Ab (4), Rahman (5)
(1) Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
(2) Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
(3) Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
(4) Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
(5) Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
Fulltext View | Download
How to cite (IJASEIT) :
Weiyuan , Yu, et al. “Enhanced Chaos-Driven Automation: A Unique Resilience Testing Toolkit for Cloud-Native IoT Networks ”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 6, Dec. 2024, pp. 2059-67, doi:10.18517/ijaseit.14.6.15956.
Citation Format :
Conventional approaches, such as static load testing and synthetic monitoring, typically evaluate system performance under controlled conditions but do not fully capture the unpredictable scenarios encountered in real-world operations. For instance, static load testing involves applying a predetermined load to the system to measure performance metrics like response time and throughput, which may not reflect the variability and chaos of actual usage. Similarly, synthetic monitoring uses scripted transactions to check system availability and performance, but these scripts often lack the complexity and variability of real-world interactions. This research aims to overcome these limitations by utilizing advanced chaos engineering techniques to simulate a range of faults, including network latency, service crashes, resource exhaustion, message loss, and security attacks. The proposed tool integrates components for data generation, fault injection, storage, monitoring, and visualization, allowing for a thorough evaluation of system robustness. The methodology involves conducting controlled experiments within an AWS-based cloud-native IoT environment to assess the tool’s effectiveness. These experiments demonstrate that the tool effectively identifies weaknesses in system resilience and improves overall robustness. By replicating real-world disruptions and analyzing system responses, the tool provides critical insights into the behavior of IoT devices under stress. The study concludes that this chaos engineering tool significantly enhances the ability to detect and address vulnerabilities, supporting creating more resilient IoT systems. Future work will expand the range of simulated faults, validate the tool across various cloud platforms, and incorporate additional real-time analysis features.

Z. Shu and G. Yan, “IoTInfer: Automated Blackbox Fuzz Testing of IoT Network Protocols Guided by Finite State Machine Inference,” IEEE Internet of Things Journal, vol. 9, no. 22, pp. 22737–22751, Nov. 2022, doi: 10.1109/jiot.2022.3182589.

D. Silva, L. I. Carvalho, J. Soares, and R. C. Sofia, “A Performance Analysis of Internet of Things Networking Protocols: Evaluating MQTT, CoAP, OPC UA,” Applied Sciences, vol. 11, no. 11, p. 4879, May 2021, doi: 10.3390/app11114879.

S. V. Mukherji, R. Sinha, S. Basak, and S. P. Kar, “Smart Agriculture using Internet of Things and MQTT Protocol,” 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), Feb. 2019, doi:10.1109/comitcon.2019.8862233.

S. Qazi, B. A. Khawaja, and Q. U. Farooq, “IoT-Equipped and AI-Enabled Next Generation Smart Agriculture: A Critical Review, Current Challenges and Future Trends,” IEEE Access, vol. 10, pp. 21219–21235, 2022, doi: 10.1109/access.2022.3152544.

M. Pyingkodi et al., “Sensor Based Smart Agriculture with IoT Technologies: A Review,” 2022 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–7, Jan. 2022, doi: 10.1109/iccci54379.2022.9741001.

A. Basiri et al., “Chaos Engineering,” IEEE Software, vol. 33, no. 3, pp. 35–41, May 2016, doi: 10.1109/ms.2016.60.

P. Dedousis, G. Stergiopoulos, G. Arampatzis, and D. Gritzalis, “Enhancing Operational Resilience of Critical Infrastructure Processes Through Chaos Engineering,” IEEE Access, vol. 11, pp. 106172–106189, 2023, doi: 10.1109/access.2023.3316028.

S. Nikolovski and P. Mitrevski, “Data Protection and Recovery Performance Analysis of Cloud-Based Recovery Service,” 2023 58th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST), Jun. 2023, doi:10.1109/icest58410.2023.10187249.

X. Tang, “Reliability-Aware Cost-Efficient Scientific Workflows Scheduling Strategy on Multi-Cloud Systems,” IEEE Transactions on Cloud Computing, vol. 10, no. 4, pp. 2909–2919, Oct. 2022, doi:10.1109/tcc.2021.3057422.

A. S. Shaikh, “A Survey on Exchanging Data Using MQTT Protocol in Arduino,” International Journal for Research in Applied Science and Engineering Technology, vol. 9, no. VII, pp. 3081–3082, Jul. 2021, doi: 10.22214/ijraset.2021.37007.

A. R. Alkhafajee, A. M. A. Al-Muqarm, A. H. Alwan, and Z. R. Mohammed, “Security and Performance Analysis of MQTT Protocol with TLS in IoT Networks,” 2021 4th International Iraqi Conference on Engineering Technology and Their Applications (IICETA), pp. 206–211, Sep. 2021, doi: 10.1109/iiceta51758.2021.9717495.

A. Awajan, “A Novel Deep Learning-Based Intrusion Detection System for IoT Networks,” Computers, vol. 12, no. 2, p. 34, Feb. 2023, doi: 10.3390/computers12020034.

Y. Chen, Y. Sun, C. Wang, and T. Taleb, “Dynamic Task Allocation and Service Migration in Edge-Cloud IoT System Based on Deep Reinforcement Learning,” IEEE Internet of Things Journal, vol. 9, no. 18, pp. 16742–16757, Sep. 2022, doi: 10.1109/jiot.2022.3164441.

E. Gómez-Marín, L. Parrilla, G. Mauro, A. Escobar-Molero, D. P. Morales, and E. Castillo, “RESEKRA: Remote Enrollment Using SEaled Keys for Remote Attestation,” Sensors, vol. 22, no. 13, p. 5060, Jul. 2022, doi: 10.3390/s22135060.

G. Peralta, P. Garrido, J. Bilbao, R. Agüero, and P. M. Crespo, “On the Combination of Multi-Cloud and Network Coding for Cost-Efficient Storage in Industrial Applications,” Sensors, vol. 19, no. 7, p. 1673, Apr. 2019, doi: 10.3390/s19071673.

F. Poltronieri, M. Tortonesi, and C. Stefanelli, “ChaosTwin: A Chaos Engineering and Digital Twin Approach for the Design of Resilient IT Services,” 2021 17th International Conference on Network and Service Management (CNSM), Oct. 2021, doi:10.23919/cnsm52442.2021.9615519.

M. Rozsíval and A. Smrčka, “NetLoiter: A Tool for Automated Testing of Network Applications using Fault-injection,” 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), pp. 207–210, Jun. 2023, doi:10.1109/dsn-w58399.2023.00057.

L. Nurfiqin, “Analisis Quality Of Service (QoS) Protokol MQTT dan HTTP Pada Sistem Smart Metering Arus Listrik,” Jurnal Repositor, vol. 3, no. 1, Dec. 2020, doi: 10.22219/repositor.v3i1.1084.

R. Zitouni, J. Petit, A. Djoudi, and L. George, “IoT-Based Urban Traffic-Light Control: Modelling, Prototyping and Evaluation of MQTT Protocol,” 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 182–189, Jul. 2019, doi: 10.1109/ithings/greencom/cpscom/smartdata.2019.00051.

D. Borsatti, W. Cerroni, F. Tonini, and C. Raffaelli, “From IoT to Cloud: Applications and Performance of the MQTT Protocol,” 2020 22nd International Conference on Transparent Optical Networks (ICTON), Jul. 2020, doi: 10.1109/icton51198.2020.9203167.

D. Eridani, K. T. Martono, and A. A. Hanifah, “MQTT Performance as a Message Protocol in an IoT based Chili Crops Greenhouse Prototyping,” 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), pp. 184–189, Nov. 2019, doi:10.1109/icitisee48480.2019.9003975.

B. Mishra, B. Mishra, and A. Kertesz, “Stress-Testing MQTT Brokers: A Comparative Analysis of Performance Measurements,” Energies, vol. 14, no. 18, p. 5817, Sep. 2021, doi: 10.3390/en14185817.

S. Arora and A. Ksentini, “Dynamic Resource Allocation and Placement of Cloud Native Network Services,” ICC 2021 - IEEE International Conference on Communications, Jun. 2021, doi:10.1109/icc42927.2021.9500276.

D. Breitgand, V. Eisenberg, N. Naaman, N. Rozenbaum, and A. Weit, “Toward True Cloud Native NFV MANO,” 2021 12th International Conference on Network of the Future (NoF), Oct. 2021, doi:10.1109/nof52522.2021.9609908.

W. Liao, & J. Draper, “Cloud Computing and Docker Containerization: A Survey”, Proceedings of the 2019 Pacific Rim International Symposium on Dependable Computing (PRDC), 2019, doi:10.1109/PRDC47759.2019.8997375

H. Jernberg, “Building a Framework for Chaos Engineering”, LU-CS-EX, 2020.

D. Craveiro, & J. Barreiros, “Chaos Engineering Tool Analysis”, 2023.

A. Gangolli, Q. H. Mahmoud, and A. Azim, “A Systematic Review of Fault Injection Attacks on IoT Systems,” Electronics, vol. 11, no. 13, p. 2023, Jun. 2022, doi: 10.3390/electronics11132023.

A. Pierce, J. Schanck, A. Groeger, R. Salih, and M. R. Clark, “Chaos engineering experiments in middleware systems using targeted network degradation and automatic fault injection,” Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2021, p. 8, Apr. 2021, doi: 10.1117/12.2584986.

L. Zhang, “ Application-Level Chaos Engineering”. PhD thesis, KTH Royal Institute of Technology, 2022.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).