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Optimization Planning Techniques with Meta-Heuristic Algorithms in IoT: Performance and QoS Evaluation

Yıl 2024, Cilt: 7 Sayı: 2, 173 - 186, 31.08.2024

Murat Koca , İsa Avcı

https://doi.org/10.35377/saucis...1452049

Öz

Big data analysis used by Internet of Things (IoT) objects is one of the most difficult issues to deal with today due to the data increase rate. Container technology is one of the many technologies available to address this problem. Because of its adaptability, portability, and scalability, it is particularly useful in IoT micro-services. The most promising lightweight virtualization method for providing cloud services has emerged owing to the variety of workloads and cloud resources. The scheduler component is critical in cloud container services for optimizing performance and lowering costs. Even though containers have gained enormous traction in cloud computing, very few thorough publications address container scheduling strategies. This work organizes its most innovative contribution around optimization scheduling techniques, which are based on three meta-heuristic algorithms. These algorithms include the particle swarm algorithm, the genetic algorithm, and the ant colony algorithm. We examine the main advantages, drawbacks, and significant difficulties of the existing approaches based on performance indicators. In addition, we made a fair comparison of the employed algorithms by evaluating their performance through Quality of Service (QoS) while each algorithm proposed a contribution. Finally, it reveals a plethora of potential future research areas for maximizing the use of emergent container technology.

Anahtar Kelimeler

IoT micro-services Container method Optimization algorithms Scheduling methods Meta- heuristic algorithms

Destekleyen Kurum

Van Yuzuncu Yil University Scientific Research Projects Coordination Unit

Proje Numarası

FYD-2022-10337

Kaynakça

  • I. Lee and K. Lee, “The Internet of Things (IoT): Applications, investments, and challenges for enterprises,” Bus. Horiz., vol. 58, no. 4, pp. 431–440, Jul. 2015, doi: 10.1016/J.BUSHOR.2015.03.008.
  • W. W. W. Gartner, “ Gartner says the Internet of Things will transform the data center.”
  • Y. Alahmad, T. Daradkeh, and A. Agarwal, “Availability-Aware Container Scheduler for Application Services in Cloud,” 2018 IEEE 37th Int. Perform. Comput. Commun. Conf. IPCCC 2018, Jul. 2018, doi: 10.1109/PCCC.2018.8711295.
  • M. Alouane and H. El Bakkali, “Virtualization in Cloud Computing: Existing solutions and new approach,” Proc. 2016 Int. Conf. Cloud Comput. Technol. Appl. CloudTech 2016, pp. 116–123, Feb. 2017, doi: 10.1109/CLOUDTECH.2016.7847687.
  • D. Merkel, “Docker: Lightweight Linux Containers for Consistent Development and Deployment”, Accessed: May 10, 2023. [Online]. Available: http://www.docker.io
  • X. Li, P. Garraghan, X. Jiang, Z. Wu, and J. Xu, “Holistic Virtual Machine Scheduling in Cloud Datacenters towards Minimizing Total Energy,” IEEE Trans. Parallel Distrib. Syst., vol. 29, no. 6, pp. 1317–1331, Jun. 2018, doi: 10.1109/TPDS.2017.2688445.
  • M. Lin, J. Xi, W. Bai, and J. Wu, “Ant Colony Algorithm for Multi-Objective Optimization of Container-Based Microservice Scheduling in Cloud,” IEEE Access, vol. 7, pp. 83088–83100, 2019, doi: 10.1109/ACCESS.2019.2924414.
  • B. Liu, J. Li, W. Lin, W. Bai, P. Li, and Q. Gao, “K-PSO: An improved PSO-based container scheduling algorithm for big data applications,” Int. J. Netw. Manag., vol. 31, no. 2, p. e2092, Mar. 2021, doi: 10.1002/NEM.2092.
  • F. Chen, X. Zhou, and C. Shi, “The container scheduling method based on the min-min in edge computing,” ACM Int. Conf. Proceeding Ser., pp. 83–90, May 2019, doi: 10.1145/3335484.3335506.
  • A. R. Arunarani, D. Manjula, and V. Sugumaran, “Task scheduling techniques in cloud computing: A literature survey,” Futur. Gener. Comput. Syst., vol. 91, pp. 407–415, Feb. 2019, doi: 10.1016/J.FUTURE.2018.09.014.
  • P. J. Maenhaut, B. Volckaert, V. Ongenae, and F. De Turck, “Resource Management in a Containerized Cloud: Status and Challenges,” J. Netw. Syst. Manag. 2019 282, vol. 28, no. 2, pp. 197–246, Nov. 2019, doi: 10.1007/S10922-019-09504-0.
  • H. I. Christensen, A. Khan, S. Pokutta, and P. Tetali, “Approximation and online algorithms for multidimensional bin packing: A survey,” Comput. Sci. Rev., vol. 24, pp. 63–79, May 2017, doi: 10.1016/J.COSREV.2016.12.001.
  • K. Hussain, M. N. Mohd Salleh, S. Cheng, and Y. Shi, “Metaheuristic research: a comprehensive survey,” Artif. Intell. Rev. 2018 524, vol. 52, no. 4, pp. 2191–2233, Jan. 2018, doi: 10.1007/S10462-017-9605-Z.
  • S. Pouyanfar et al., “A Survey on Deep Learning,” ACM Comput. Surv., vol. 51, no. 5, Sep. 2018, doi: 10.1145/3234150.
  • W. Attaoui, E. Sabir, H. Elbiaze, and M. Guizani, “VNF and CNF Placement in 5G: Recent Advances and Future Trends,” IEEE Trans. Netw. Serv. Manag., vol. 20, no. 4, pp. 4698–4733, Dec. 2023, doi: 10.1109/TNSM.2023.3264005.
  • S. B. Nath, S. Chattopadhyay, R. Karmakar, S. K. Addya, S. Chakraborty, and S. K. Ghosh, “PTC: Pick-test-choose to place containerized micro-services in IoT,” Proc. - IEEE Glob. Commun. Conf. GLOBECOM, 2019, doi: 10.1109/GLOBECOM38437.2019.9013163.
  • H. M. R. Al-Khafaji, “Improving Quality Indicators of the Cloud-Based IoT Networks Using an Improved Form of Seagull Optimization Algorithm,” Futur. Internet 2022, Vol. 14, Page 281, vol. 14, no. 10, p. 281, Sep. 2022, doi: 10.3390/FI14100281.
  • P. Satyanarayana, G. Diwakar, B. V. Subbayamma, N. V. Phani Sai Kumar, M. Arun, and S. Gopalakrishnan, “Comparative analysis of new meta-heuristic-variants for privacy preservation in wireless mobile adhoc networks for IoT applications,” Comput. Commun., vol. 198, pp. 262–281, Jan. 2023, doi: 10.1016/J.COMCOM.2022.12.006.
  • D. Bhamare, M. Samaka, A. Erbad, R. Jain, L. Gupta, and H. A. Chan, “Multi-objective scheduling of micro-services for optimal service function chains,” IEEE Int. Conf. Commun., Jul. 2017, doi: 10.1109/ICC.2017.7996729.
  • C. Kaewkasi and K. Chuenmuneewong, “Improvement of container scheduling for Docker using Ant Colony Optimization,” 2017 9th Int. Conf. Knowl. Smart Technol. Crunching Inf. Everything, KST 2017, pp. 254–259, Mar. 2017, doi: 10.1109/KST.2017.7886112.
  • T. Shi, H. Ma, and G. Chen, “Energy-Aware Container Consolidation Based on PSO in Cloud Data Centers,” 2018 IEEE Congr. Evol. Comput. CEC 2018 - Proc., Sep. 2018, doi: 10.1109/CEC.2018.8477708.
  • B. Tan, H. Ma, and Y. Mei, “A Hybrid Genetic Programming Hyper-Heuristic Approach for Online Two-level Resource Allocation in Container-based Clouds,” 2019 IEEE Congr. Evol. Comput. CEC 2019 - Proc., pp. 2681–2688, Jun. 2019, doi: 10.1109/CEC.2019.8790220.
  • S. S. Gill and R. Buyya, “A Taxonomy and Future Directions for Sustainable Cloud Computing,” ACM Comput. Surv., vol. 51, no. 5, Dec. 2018, doi: 10.1145/3241038.
  • M. Dorigo, M. Birattari, and T. Stutzle, “Ant colony optimization,” IEEE Comput. Intell. Mag., vol. 1, no. 4, pp. 28–39, Nov. 2006, doi: 10.1109/MCI.2006.329691.
  • B. Burvall, “Improvement of Container Placement Using Multi-Objective Ant Colony Optimization,” DEGREE Proj. Comput. Sci. Eng., 2019, Accessed: May 10, 2023. [Online]. Available: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-249709
  • J. Yang, X. Shi, M. Marchese, and Y. Liang, “An ant colony optimization method for generalized TSP problem,” Prog. Nat. Sci., vol. 18, no. 11, pp. 1417–1422, Nov. 2008, doi: 10.1016/J.PNSC.2008.03.028.
  • C. Guerrero, I. Lera, and C. Juiz, “Genetic algorithm for multi-objective optimization of container allocation in cloud architecture,” J. Grid Comput., vol. 16, no. 1, pp. 113–135, Nov. 2017, doi: 10.1007/S10723-017-9419-X/METRICS.
  • C. Teixeira, J. A. Covas, T. Stützle, and A. Gaspar-Cunha, “Multi-objective ant colony optimization for the twin-screw configuration problem,” https://doi.org/10.1080/0305215X.2011.639370, vol. 44, no. 3, pp. 351–371, Mar. 2012, doi: 10.1080/0305215X.2011.639370.
  • R. Zhang, Y. Chen, B. Dong, F. Tian, and Q. Zheng, “A Genetic Algorithm-Based Energy-Efficient Container Placement Strategy in CaaS,” IEEE Access, vol. 7, pp. 121360–121373, 2019, doi: 10.1109/ACCESS.2019.2937553.
  • M. Imdoukh, I. Ahmad, and M. Alfailakawi, “Optimizing scheduling decisions of container management tool using many-objective genetic algorithm,” Concurr. Comput. Pract. Exp., vol. 32, no. 5, p. e5536, Mar. 2020, doi: 10.1002/CPE.5536.
  • A. Dhumal and D. Janakiram, “C-Balancer: A System for Container Profiling and Scheduling,” Sep. 2020, Accessed: May 10, 2023. [Online]. Available: https://arxiv.org/abs/2009.08912v1
  • L. Li, J. Chen, and W. Yan, “A particle swarm optimization-based container scheduling algorithm of docker platform,” ACM Int. Conf. Proceeding Ser., pp. 12–17, Nov. 2018, doi: 10.1145/3290420.3290432.
  • J. Kennedy and R. Eberhart, “Particle swarm optimization,” Proc. ICNN’95 - Int. Conf. Neural Networks, vol. 4, pp. 1942–1948, doi: 10.1109/ICNN.1995.488968.
  • L. J. Li, Z. B. Huang, F. Liu, and Q. H. Wu, “A heuristic particle swarm optimizer for optimization of pin connected structures,” Comput. Struct., vol. 85, no. 7–8, pp. 340–349, Apr. 2007, doi: 10.1016/J.COMPSTRUC.2006.11.020.
  • Y. Guo and W. Yao, “A container scheduling strategy based on neighborhood division in micro service,” IEEE/IFIP Netw. Oper. Manag. Symp. Cogn. Manag. a Cyber World, NOMS 2018, pp. 1–6, Jul. 2018, doi: 10.1109/NOMS.2018.8406285.
  • X. H. Shi, Y. C. Liang, H. P. Lee, C. Lu, and Q. X. Wang, “Particle swarm optimization-based algorithms for TSP and generalized TSP,” Inf. Process. Lett., vol. 103, no. 5, pp. 169–176, Aug. 2007, doi: 10.1016/J.IPL.2007.03.010.
  • M. Adhikari and S. N. Srirama, “Multi-objective accelerated particle swarm optimization with a container-based scheduling for Internet-of-Things in cloud environment,” J. Netw. Comput. Appl., vol. 137, pp. 35–61, Jul. 2019, doi: 10.1016/J.JNCA.2019.04.003.
  • G. Fan, L. Chen, H. Yu, and W. Qi, “Multi-objective optimization of container-based microservice scheduling in edge computing,” Comput. Sci. Inf., vol. 18, no. 1, pp. 23–42, 2021, Accessed: May 12, 2023. [Online]. Available: http://www.doiserbia.nb.rs/Article.aspx?id=1820-02142000041F

Yıl 2024, Cilt: 7 Sayı: 2, 173 - 186, 31.08.2024

Murat Koca , İsa Avcı

https://doi.org/10.35377/saucis...1452049

Öz

Proje Numarası

FYD-2022-10337

Kaynakça

  • I. Lee and K. Lee, “The Internet of Things (IoT): Applications, investments, and challenges for enterprises,” Bus. Horiz., vol. 58, no. 4, pp. 431–440, Jul. 2015, doi: 10.1016/J.BUSHOR.2015.03.008.
  • W. W. W. Gartner, “ Gartner says the Internet of Things will transform the data center.”
  • Y. Alahmad, T. Daradkeh, and A. Agarwal, “Availability-Aware Container Scheduler for Application Services in Cloud,” 2018 IEEE 37th Int. Perform. Comput. Commun. Conf. IPCCC 2018, Jul. 2018, doi: 10.1109/PCCC.2018.8711295.
  • M. Alouane and H. El Bakkali, “Virtualization in Cloud Computing: Existing solutions and new approach,” Proc. 2016 Int. Conf. Cloud Comput. Technol. Appl. CloudTech 2016, pp. 116–123, Feb. 2017, doi: 10.1109/CLOUDTECH.2016.7847687.
  • D. Merkel, “Docker: Lightweight Linux Containers for Consistent Development and Deployment”, Accessed: May 10, 2023. [Online]. Available: http://www.docker.io
  • X. Li, P. Garraghan, X. Jiang, Z. Wu, and J. Xu, “Holistic Virtual Machine Scheduling in Cloud Datacenters towards Minimizing Total Energy,” IEEE Trans. Parallel Distrib. Syst., vol. 29, no. 6, pp. 1317–1331, Jun. 2018, doi: 10.1109/TPDS.2017.2688445.
  • M. Lin, J. Xi, W. Bai, and J. Wu, “Ant Colony Algorithm for Multi-Objective Optimization of Container-Based Microservice Scheduling in Cloud,” IEEE Access, vol. 7, pp. 83088–83100, 2019, doi: 10.1109/ACCESS.2019.2924414.
  • B. Liu, J. Li, W. Lin, W. Bai, P. Li, and Q. Gao, “K-PSO: An improved PSO-based container scheduling algorithm for big data applications,” Int. J. Netw. Manag., vol. 31, no. 2, p. e2092, Mar. 2021, doi: 10.1002/NEM.2092.
  • F. Chen, X. Zhou, and C. Shi, “The container scheduling method based on the min-min in edge computing,” ACM Int. Conf. Proceeding Ser., pp. 83–90, May 2019, doi: 10.1145/3335484.3335506.
  • A. R. Arunarani, D. Manjula, and V. Sugumaran, “Task scheduling techniques in cloud computing: A literature survey,” Futur. Gener. Comput. Syst., vol. 91, pp. 407–415, Feb. 2019, doi: 10.1016/J.FUTURE.2018.09.014.
  • P. J. Maenhaut, B. Volckaert, V. Ongenae, and F. De Turck, “Resource Management in a Containerized Cloud: Status and Challenges,” J. Netw. Syst. Manag. 2019 282, vol. 28, no. 2, pp. 197–246, Nov. 2019, doi: 10.1007/S10922-019-09504-0.
  • H. I. Christensen, A. Khan, S. Pokutta, and P. Tetali, “Approximation and online algorithms for multidimensional bin packing: A survey,” Comput. Sci. Rev., vol. 24, pp. 63–79, May 2017, doi: 10.1016/J.COSREV.2016.12.001.
  • K. Hussain, M. N. Mohd Salleh, S. Cheng, and Y. Shi, “Metaheuristic research: a comprehensive survey,” Artif. Intell. Rev. 2018 524, vol. 52, no. 4, pp. 2191–2233, Jan. 2018, doi: 10.1007/S10462-017-9605-Z.
  • S. Pouyanfar et al., “A Survey on Deep Learning,” ACM Comput. Surv., vol. 51, no. 5, Sep. 2018, doi: 10.1145/3234150.
  • W. Attaoui, E. Sabir, H. Elbiaze, and M. Guizani, “VNF and CNF Placement in 5G: Recent Advances and Future Trends,” IEEE Trans. Netw. Serv. Manag., vol. 20, no. 4, pp. 4698–4733, Dec. 2023, doi: 10.1109/TNSM.2023.3264005.
  • S. B. Nath, S. Chattopadhyay, R. Karmakar, S. K. Addya, S. Chakraborty, and S. K. Ghosh, “PTC: Pick-test-choose to place containerized micro-services in IoT,” Proc. - IEEE Glob. Commun. Conf. GLOBECOM, 2019, doi: 10.1109/GLOBECOM38437.2019.9013163.
  • H. M. R. Al-Khafaji, “Improving Quality Indicators of the Cloud-Based IoT Networks Using an Improved Form of Seagull Optimization Algorithm,” Futur. Internet 2022, Vol. 14, Page 281, vol. 14, no. 10, p. 281, Sep. 2022, doi: 10.3390/FI14100281.
  • P. Satyanarayana, G. Diwakar, B. V. Subbayamma, N. V. Phani Sai Kumar, M. Arun, and S. Gopalakrishnan, “Comparative analysis of new meta-heuristic-variants for privacy preservation in wireless mobile adhoc networks for IoT applications,” Comput. Commun., vol. 198, pp. 262–281, Jan. 2023, doi: 10.1016/J.COMCOM.2022.12.006.
  • D. Bhamare, M. Samaka, A. Erbad, R. Jain, L. Gupta, and H. A. Chan, “Multi-objective scheduling of micro-services for optimal service function chains,” IEEE Int. Conf. Commun., Jul. 2017, doi: 10.1109/ICC.2017.7996729.
  • C. Kaewkasi and K. Chuenmuneewong, “Improvement of container scheduling for Docker using Ant Colony Optimization,” 2017 9th Int. Conf. Knowl. Smart Technol. Crunching Inf. Everything, KST 2017, pp. 254–259, Mar. 2017, doi: 10.1109/KST.2017.7886112.
  • T. Shi, H. Ma, and G. Chen, “Energy-Aware Container Consolidation Based on PSO in Cloud Data Centers,” 2018 IEEE Congr. Evol. Comput. CEC 2018 - Proc., Sep. 2018, doi: 10.1109/CEC.2018.8477708.
  • B. Tan, H. Ma, and Y. Mei, “A Hybrid Genetic Programming Hyper-Heuristic Approach for Online Two-level Resource Allocation in Container-based Clouds,” 2019 IEEE Congr. Evol. Comput. CEC 2019 - Proc., pp. 2681–2688, Jun. 2019, doi: 10.1109/CEC.2019.8790220.
  • S. S. Gill and R. Buyya, “A Taxonomy and Future Directions for Sustainable Cloud Computing,” ACM Comput. Surv., vol. 51, no. 5, Dec. 2018, doi: 10.1145/3241038.
  • M. Dorigo, M. Birattari, and T. Stutzle, “Ant colony optimization,” IEEE Comput. Intell. Mag., vol. 1, no. 4, pp. 28–39, Nov. 2006, doi: 10.1109/MCI.2006.329691.
  • B. Burvall, “Improvement of Container Placement Using Multi-Objective Ant Colony Optimization,” DEGREE Proj. Comput. Sci. Eng., 2019, Accessed: May 10, 2023. [Online]. Available: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-249709
  • J. Yang, X. Shi, M. Marchese, and Y. Liang, “An ant colony optimization method for generalized TSP problem,” Prog. Nat. Sci., vol. 18, no. 11, pp. 1417–1422, Nov. 2008, doi: 10.1016/J.PNSC.2008.03.028.
  • C. Guerrero, I. Lera, and C. Juiz, “Genetic algorithm for multi-objective optimization of container allocation in cloud architecture,” J. Grid Comput., vol. 16, no. 1, pp. 113–135, Nov. 2017, doi: 10.1007/S10723-017-9419-X/METRICS.
  • C. Teixeira, J. A. Covas, T. Stützle, and A. Gaspar-Cunha, “Multi-objective ant colony optimization for the twin-screw configuration problem,” https://doi.org/10.1080/0305215X.2011.639370, vol. 44, no. 3, pp. 351–371, Mar. 2012, doi: 10.1080/0305215X.2011.639370.
  • R. Zhang, Y. Chen, B. Dong, F. Tian, and Q. Zheng, “A Genetic Algorithm-Based Energy-Efficient Container Placement Strategy in CaaS,” IEEE Access, vol. 7, pp. 121360–121373, 2019, doi: 10.1109/ACCESS.2019.2937553.
  • M. Imdoukh, I. Ahmad, and M. Alfailakawi, “Optimizing scheduling decisions of container management tool using many-objective genetic algorithm,” Concurr. Comput. Pract. Exp., vol. 32, no. 5, p. e5536, Mar. 2020, doi: 10.1002/CPE.5536.
  • A. Dhumal and D. Janakiram, “C-Balancer: A System for Container Profiling and Scheduling,” Sep. 2020, Accessed: May 10, 2023. [Online]. Available: https://arxiv.org/abs/2009.08912v1
  • L. Li, J. Chen, and W. Yan, “A particle swarm optimization-based container scheduling algorithm of docker platform,” ACM Int. Conf. Proceeding Ser., pp. 12–17, Nov. 2018, doi: 10.1145/3290420.3290432.
  • J. Kennedy and R. Eberhart, “Particle swarm optimization,” Proc. ICNN’95 - Int. Conf. Neural Networks, vol. 4, pp. 1942–1948, doi: 10.1109/ICNN.1995.488968.
  • L. J. Li, Z. B. Huang, F. Liu, and Q. H. Wu, “A heuristic particle swarm optimizer for optimization of pin connected structures,” Comput. Struct., vol. 85, no. 7–8, pp. 340–349, Apr. 2007, doi: 10.1016/J.COMPSTRUC.2006.11.020.
  • Y. Guo and W. Yao, “A container scheduling strategy based on neighborhood division in micro service,” IEEE/IFIP Netw. Oper. Manag. Symp. Cogn. Manag. a Cyber World, NOMS 2018, pp. 1–6, Jul. 2018, doi: 10.1109/NOMS.2018.8406285.
  • X. H. Shi, Y. C. Liang, H. P. Lee, C. Lu, and Q. X. Wang, “Particle swarm optimization-based algorithms for TSP and generalized TSP,” Inf. Process. Lett., vol. 103, no. 5, pp. 169–176, Aug. 2007, doi: 10.1016/J.IPL.2007.03.010.
  • M. Adhikari and S. N. Srirama, “Multi-objective accelerated particle swarm optimization with a container-based scheduling for Internet-of-Things in cloud environment,” J. Netw. Comput. Appl., vol. 137, pp. 35–61, Jul. 2019, doi: 10.1016/J.JNCA.2019.04.003.
  • G. Fan, L. Chen, H. Yu, and W. Qi, “Multi-objective optimization of container-based microservice scheduling in edge computing,” Comput. Sci. Inf., vol. 18, no. 1, pp. 23–42, 2021, Accessed: May 12, 2023. [Online]. Available: http://www.doiserbia.nb.rs/Article.aspx?id=1820-02142000041F
Toplam 38 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Yazılımı, Yazılım Mimarisi
Bölüm Makaleler
Yazarlar

Murat Koca 0000-0002-6048-7645

İsa Avcı 0000-0001-7032-8018

Proje Numarası FYD-2022-10337
Erken Görünüm Tarihi 23 Ağustos 2024
Yayımlanma Tarihi 31 Ağustos 2024
Gönderilme Tarihi 13 Mart 2024
Kabul Tarihi 28 Haziran 2024
Yayımlandığı Sayı Yıl 2024Cilt: 7 Sayı: 2

Kaynak Göster

IEEE M. Koca ve İ. Avcı, “Optimization Planning Techniques with Meta-Heuristic Algorithms in IoT: Performance and QoS Evaluation”, SAUCIS, c. 7, sy. 2, ss. 173–186, 2024, doi: 10.35377/saucis...1452049.
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