survey

Resource Management in Cloud and Cloud-influenced Technologies for Internet of Things Applications

Authors:

Rathinaraja Jeyaraj,

Anandkumar Balasubramaniam,

Ajay Kumara M.A.,

Anand PaulAuthors Info & Claims

ACM Computing Surveys, Volume 55, Issue 12

Article No.: 242, Pages 1 - 37

https://doi.org/10.1145/3571729

Published: 02 March 2023 Publication History

Abstract

The trend of adopting Internet of Things (IoT) in healthcare, smart cities, Industry 4.0, and so on is increasing by means of cloud computing, which provides on-demand storage and computation facilities over the Internet. To meet specific requirements of IoT applications, the cloud has also shifted its service offering platform to its next-generation models, such as fog, mist, and dew computing. As a result, the cloud and IoT have become part and parcel of smart applications that play significant roles in improving the quality of human life. In addition to the inherent advantages of advanced cloud models, to improve the performance of IoT applications further, it is essential to understand how the resources in the cloud and cloud-influenced platforms are managed to support various phases in the end-to-end IoT deployment. Considering this importance, in this article, we provide a brief description, a systematic review, and possible research directions on every aspect of resource management tasks, such as workload modeling, resource provisioning, workload scheduling, resource allocation, load balancing, energy management, and resource heterogeneity in such advanced platforms, from a cloud perspective. The primary objective of this article is to help early researchers gain insight into the underlying concepts of resource management tasks in the cloud for IoT applications.

References

[1]

Yongrui Qin, Quan Z. Sheng, Nickolas J. G. Falkner, Schahram Dustdar, Hua Wang, and Athanasios V. Vasilakos. 2016. When things matter: A survey on data-centric internet of things. J. Netw. Comput. Appl. 64, (2016), 137–153. DOI:

Digital Library

[2]

Alessio Botta, Walter De Donato, Valerio Persico, and Antonio Pescapé. 2016. Integration of cloud computing and internet of things: A survey. Futur. Gener. Comput. Syst. 56, (2016), 684–700. DOI:

Digital Library

[3]

Hanan Elazhary. 2019. Internet of things (IoT), mobile cloud, cloudlet, mobile IoT, IoT cloud, fog, mobile edge, and edge emerging computing paradigms: Disambiguation and research directions. J. Netw. Comput. Appl. 128, (2019), 105–140. DOI:

[4]

Anand Paul and Rathinaraja Jeyaraj. 2019. Internet of things: A primer. Hum. Behav. Emerg. Technol. 1, 1 (2019), 37–47. DOI:

[5]

Hongming Cai, Boyi Xu, Lihong Jiang, and Athanasios V. Vasilakos. 2017. IoT-based big data storage systems in cloud computing: Perspectives and challenges. IEEE Internet Things J. 4, 1 (2017), 75–87. DOI:

[6]

Azure IoT Hub. 2022. Retrieved 11 October 2022 from https://azure.microsoft.com/en-us/services/iot-hub/#overview.

[7]

Amazon IoT. 2022. Retrieved 11 October 2022 from https://aws.amazon.com/iot/.

[8]

Cisco IoT. 2022. Retrieved 11 October 2022 from https://www.cisco.com/c/en/us/solutions/internet-of-things/iot-control-center.html.

[9]

IBM IoT. 2022. Retrieved 11 October 2022 from https://internetofthings.ibmcloud.com/.

[10]

Google IoT. 2022. Retrieved 11 October 2022 from https://cloud.google.com/solutions/iot.

[11]

Karrar Hameed Abdulkareem, Mazin Abed Mohammed, Saraswathy Shamini Gunasekaran, Mohammed Nasser Al-Mhiqani, Ammar Awad Mutlag, Salama A. Mostafa, Nabeel Salih Ali, and Dheyaa Ahmed Ibrahim. 2019. A review of fog computing and machine learning: Concepts, applications, challenges, and open issues. IEEE Access 7, (2019), 153123–153140. DOI:

[12]

Md Asif-Ur-Rahman, Fariha Afsana, Mufti Mahmud, M. Shamim Kaiser, Muhammad R. Ahmed, Omprakash Kaiwartya, and Anne James-Taylor. 2019. Toward a heterogeneous mist, fog, and cloud-based framework for the internet of healthcare things. IEEE Internet Things J. 6, 3 (2019), 4049–4062. DOI:

[13]

Yaqiong Liu, Mugen Peng, Guochu Shou, Yudong Chen, and Siyu Chen. 2020. Toward edge intelligence: multiaccess edge computing for 5G and internet of things. IEEE Internet Things J. 7, 8 (2020), 6722–6747. DOI:

[14]

Mohammed Laroui, Boubakr Nour, Hassine Moungla, Moussa A. Cherif, Hossam Afifi, and Mohsen Guizani. 2021. Edge and fog computing for IoT: A survey on current research activities & future directions. Comput. Commun. 180, (2021), 210–231. DOI:

Digital Library

[15]

Partha Pratim Ray. 2017. An introduction to dew computing: Definition, concept and implications. IEEE Access 6, (2017), 723–737. DOI:

[16]

J. Ren, D. Zhang, S. He, Y. Zhang, and T. Li. 2020. A survey on end-edge-cloud orchestrated network computing paradigms: Transparent computing, mobile edge computing, fog computing, and cloudlet. ACM Comput. Surv. 52, 6, Article 125 (November 2020), 36.

[17]

D. R. Vasconcelos, R. M. C. Andrade, V. Severino, and J. N. De Souza. 2019. Cloud, fog, or mist in IoT? That is the qestion. ACM Trans. Internet Technol. 19, 2, Article 25 (May 2019), 20.

[18]

Nam Yong Kim, Jung Hyun Ryu, Byoung Wook Kwon, Yi Pan, and Jong Hyuk Park. 2018. CF-CloudOrch: Container fog node-based cloud orchestration for IoT networks. J. Supercomput. 74, 12 (2018), 7024–7045. DOI:

Digital Library

[19]

Yajing Xu, Junnan Li, Zhihui Lu, Jie Wu, Patrick C. K. Hung, and Abdulhameed Alelaiwi. 2020. ARVMEC: Adaptive recommendation of virtual machines for IoT in edge-cloud environment. J. Parallel Distrib. Comput. 141, (2020), 23–34. DOI:

[20]

G. A. S. Cassel, V. F. Rodrigues, R. da Rosa Righi, M. R. Bez, A. C. Nepomuceno, and C. André da Costa. 2022. Serverless computing for internet of things: A systematic literature review. Futur. Gener. Comput. Syst. 128, (2022), 299–316. DOI:https://doi.org/10.1016/j.future.2021.10.020

[21]

B. Jennings and R. Stadler. 2015. Resource management in clouds: Survey and research challenges. J. Netw. Syst. Manag. 23, 3 (2015), 567–619. DOI:https://doi.org/10.1007/s10922-014-9307-7

[22]

Maggi Bansal, Inderveer Chana, and Siobhán Clarke. 2021. A survey on IoT big data: Current status, 13 V’s Challenges, and future directions. ACM Comput. Surv. 53, 6, Article 131 (November 2021), 59.

[23]

Xiang Fei, Nazaraf Shah, Nandor Verba, Kuo Ming Chao, Victor Sanchez-Anguix, Jacek Lewandowski, Anne James, and Zahid Usman. 2019. CPS data streams analytics based on machine learning for cloud and fog computing: A survey. Futur. Gener. Comput. Syst. 90, (2019), 435–450. DOI:

[24]

S. K. Lo, Q. Lu, C. Wang, H. Y. Paik, and L. Zhu. 2019. A systematic literature review on federated machine learning: From a sofware engineering perspective. ACM Comput. Surv. 54, 5, Article 95 (May 2019), 39.

[25]

Jie Zhang, Zhihao Qu, Chenxi Chen, Haozhao Wang, Yufeng Zhan, Baoliu Ye, and Song Guo. 2022. Edge learning: The enabling technology for distributed big data analytics in the edge. ACM Comput. Surv. 54, 7, Article 151 (September 2022), 36.

[26]

Safa Ben Atitallah, Maha Driss, Wadii Boulila, and Henda Ben Ghezala. 2020. Leveraging deep learning and IoT big data analytics to support the smart cities development: Review and future directions. Comput. Sci. Rev. 38, (2020). DOI:

[27]

Sven Groppe. 2020. Emergent models, frameworks, and hardware technologies for big data analytics. J. Supercomput. 76, 3 (2020), 1800–1827. DOI:

[28]

Hadoop. 2022. Retrieved 11 October 2022 from https://hadoop.apache.org/.

[29]

Spark. 2022. Retrieved 11 October 2022 from https://spark.apache.org/.

[30]

Storm. 2022. Retrieved 11 October 2022 from https://storm.apache.org/.

[31]

Kafka. 2022. Retrieved 11 October 2022 from https://kafka.apache.org/.

[32]

Yasir Arfat, Sardar Usman, Rashid Mehmood, and Iyad Katib. 2020. Big data for smart infrastructure design: Opportunities and challenges. EAI/Springer Innov. Commun. Comput. (2020), 491–518. DOI:

[33]

Muhammad H. Hilman, Maria A. Rodriguez, and Rajkumar Buyya. 2021. Multiple workflows scheduling in multi-tenant distributed systems: A taxonomy and future directions. ACM Comput. Surv. 53, 1, Article 10 (January 2021), 39.

[34]

T. Ben-Nun and T. Hoefler. 2020. Demystifying parallel and distributed deep learning. ACM Comput. Surv. 52, 4 (2020), 1–43. DOI:https://doi.org/10.1145/3320060

[35]

R. Kang, A. Guo, G. Laput, Y. Li, and X. A. Chen. 2019. Minuet: Multimodal interaction with an internet of things. Proc. SUI. ACM Conf. Spat. User Interact. Article 2, (2019), 1–10. DOI:https://doi.org/10.1145/3357251.3357581

[36]

Redowan Mahmud, Kotagiri Ramamohanarao, and Rajkumar Buyya. 2021. Application management in fog computing environments: A taxonomy, review and future directions. ACM Comput. Surv. 53, 4, Article 88 (July 2021), 43.

[37]

Mainak Adhikari, Tarachand Amgoth, and Satish Narayana Srirama. 2020. A survey on scheduling strategies for workflows in cloud environment and emerging trends. ACM Comput. Surv. 52, 4, Article 68 (July 2020), 36. DOI:

Digital Library

[38]

Muhammad H. Hilman, Maria A. Rodriguez, and Rajkumar Buyya. 2021. Workflow-as-a-service cloud platform and deployment of bioinformatics workflow applications. Knowl. Manag. Dev. Data-Intensive Syst. (2021), 205–226. DOI:

[39]

Xiaolong Xu, Wanchun Dou, Xuyun Zhang, and Jinjun Chen. 2016. EnReal: An energy-aware resource allocation method for scientific workflow executions in cloud environment. IEEE Trans. Cloud Comput. 4, 2 (2016), 166–179. DOI:

[40]

Abdulsalam Yassine, Shailendra Singh, M. Shamim Hossain, and Ghulam Muhammad. 2019. IoT big data analytics for smart homes with fog and cloud computing. Futur. Gener. Comput. Syst. 91, (2019), 563–573. DOI:

Digital Library

[41]

Daniele Tessera Maria Carla Calzarossa, Luisa Massari. 2016. Workload characterization: A survey revisited. ACM Comput. Surv. 48, 3 (2016), 1–43. DOI:

Digital Library

[42]

Anil Kashyap. 2018. Workload characterization for enterprise disk drives. ACM Trans. Storage 14, 2 (2018). DOI:

Digital Library

[43]

Hani Nemati, Seyed Vahid Azhari, Mahsa Shakeri, and Michel Dagenais. 2021. Host-based virtual machine workload characterization using hypervisor trace mining. ACM Trans. Model. Perform. Eval. Comput. Syst. 6, 1 (2021). DOI:

Digital Library

[44]

Y. Wen, G. Cheng, S. Deng, and J. Yin. 2022. Characterizing and synthesizing the workflow structure of microservices in bytedance cloud. J. Softw. Evol. Process (2022), 1–18. DOI:https://doi.org/10.1002/smr.2467

[45]

Maria Malik, Katayoun Neshatpour, Setareh Rafatirad, and Houman Homayoun. 2018. Hadoop workloads characterization for performance and energy efficiency optimizations on microservers. IEEE Trans. Multi-Scale Comput. Syst. 4, 3 (2018), 355–368. DOI:

[46]

Zhibin Yu, Wen Xiong, Lieven Eeckhout, Zhendong Bei, Avi Mendelson, and Chengzhong Xu. 2018. MIA: Metric importance analysis for big data workload characterization. IEEE Trans. Parallel Distrib. Syst. 29, 6 (2018), 1371–1384. DOI:

[47]

Bumjoon Seo, Sooyong Kang, Jongmoo Choi, Jaehyuk Cha, Youjip Won, and Sungroh Yoon. 2014. IO workload characterization revisited: A data-mining approach. IEEE Trans. Comput. 63, 12 (2014), 3026–3038. DOI:

Digital Library

[48]

A. Mahgoub et al. 2022. WiseFuse: Workload characterization and DAG transformation for serverless workflows. Proc. ACM Meas. Anal. Comput. Syst. 6, 2 (2022), 1–28. DOI:https://doi.org/10.1145/3530892

[49]

Ismael Solis Moreno, Peter Garraghan, Paul Townend, and Jie Xu. 2014. Analysis, modeling and simulation of workload patterns in a large-scale utility cloud. IEEE Trans. Cloud Comput. 2, 2 (2014), 208–221. DOI:

[50]

Blesson Varghese, Ozgur Akgun, Ian Miguel, Long Thai, and Adam Barker. 2019. Cloud benchmarking for maximising performance of scientific applications. IEEE Trans. Cloud Comput. 7, 1 (2019), 170–182. DOI:

[51]

Klervie Toczé, Johan Lindqvist, and Simin Nadjm-Tehrani. 2020. Characterization and modeling of an edge computing mixed reality workload. J. Cloud Comput. 9, 1 (2020), 1–24. DOI:

Digital Library

[52]

Kai Hwang, Xiaoying Bai, Yue Shi, Muyang Li, Wen Guang Chen, and Yongwei Wu. 2016. Cloud performance modeling with benchmark evaluation of elastic scaling strategies. IEEE Trans. Parallel Distrib. Syst. 27, 1 (2016), 130–143. DOI:

Digital Library

[53]

Xiaolin Chang, Ruofan Xia, Jogesh K. Muppala, Kishor S. Trivedi, and Jiqiang Liu. 2018. Effective modeling approach for iaas data center performance analysis under heterogeneous workload. IEEE Trans. Cloud Comput. 6, 4 (2018), 991–1003. DOI:

[54]

Hosein Mohamamdi Makrani, Hossein Sayadi, Najmeh Nazari, Sai Mnoj Pudukotai Dinakarrao, Avesta Sasan, Tinoosh Mohsenin, Setareh Rafatirad, and Houman Homayoun. 2020. Adaptive performance modeling of data-intensive workloads for resource provisioning in virtualized environment. ACM Trans. Model. Perform. Eval. Comput. Syst. 5, 4, Article 18 (December 2020), 24.

[55]

Jun Zhou, Bowei Cen, Zexiang Cai, Yuanju Chen, Yuyan Sun, Hongli Xue, and Weiha O. Tan. 2021. Workload modeling for microservice-based edge computing in power internet of things. IEEE Access 9, (2021), 76205–76212. DOI:

[56]

Boyun Liu, Jingjing Guo, Chunlin Li, and Youlong Luo. 2020. Workload forecasting based elastic resource management in edge cloud. Comput. Ind. Eng. 139 (2020), 12. DOI:

Digital Library

[57]

S. Narasimha Swamy and Solomon Raju Kota. 2020. An empirical study on system level aspects of Internet of Things (IoT). IEEE Access 8, (2020), 188082–188134. DOI:

[58]

Cheol-Ho Hong and Blesson Varghese. 2020. Resource management in fog/edge computing. ACM Comput. Surv. 52, 5, Article 97 (September 2020), 37.

[59]

Mostafa Ghobaei-Arani, Alireza Souri, and Ali A. Rahmanian. 2020. Resource management approaches in fog computing: A comprehensive review. J. Grid Comput. 18, 1 (2020), 1–42. DOI:

[60]

Ola Salman, Imad Elhajj, Ali Chehab, and Ayman Kayssi. 2018. IoT survey: An SDN and fog computing perspective. Comput. Networks 143, (2018), 221–246. DOI:

Digital Library

[61]

Nguyen Dinh Nguyen, Linh An Phan, Dae Heon Park, Sehan Kim, and Taehong Kim. 2020. ElasticFog: Elastic resource provisioning in container-based fog computing. IEEE Access 8, (2020), 183879–183890. DOI:

[62]

Tian Wang, Yuzhu Liang, Weijia Jia, Muhammad Arif, Anfeng Liu, and Mande Xie. 2019. Coupling resource management based on fog computing in smart city systems. J. Netw. Comput. Appl. 135, (2019), 11–19. DOI:

Digital Library

[63]

Xiang Sun, Nirwan Ansari, and Ruopeng Wang. 2016. Optimizing resource utilization of a data center. IEEE Commun. Surv. Tutorials 18, 4 (2016), 2822–2846. DOI:

Digital Library

[64]

Misbah Liaqat, Victor Chang, Abdullah Gani, Siti Hafizah Ab Hamid, Muhammad Toseef, Umar Shoaib, and Rana Liaqat Ali. 2017. Federated cloud resource management: Review and discussion. J. Netw. Comput. Appl. 77, (2017), 87–105. DOI:

Digital Library

[65]

Juliana Oliveira de Carvalho, Fernando Trinta, Dario Vieira, and Omar Andres Carmona Cortes. 2018. Evolutionary solutions for resources management in multiple clouds: State-of-the-art and future directions. Futur. Gener. Comput. Syst. 88, (2018), 284–296. DOI:

[66]

K. Hemant Kumar Reddy, Ranjit Kumar Behera, Alok Chakrabarty, and Diptendu Sinha Roy. 2020. A service delay minimization scheme for QoS-constrained, context-aware unified IoT applications. IEEE Internet Things J. 7, 10 (2020), 10527–10534. DOI:

[67]

Giovanni Merlino, Rustem Dautov, Salvatore Distefano, and Dario Bruneo. 2019. Enabling workload engineering in edge, fog, and cloud computing through openstack-based middleware. ACM Trans. Internet Technol. 19, 2, Article 28 (May 2019), 22.

[68]

Enis Afgan, Andrew Lonie, James Taylor, and Nuwan Goonasekera. 2019. CloudLaunch: Discover and deploy cloud applications. Futur. Gener. Comput. Syst. 94, (2019), 802–810. DOI:

Digital Library

[69]

Azure resource type. 2022. Retrieved 11 October 2022 from https://docs.microsoft.com/en-us/azure/virtual-machines/sizes.

[70]

Azure service plan. 2022. Retrieved 11 October 2022 from https://azure.microsoft.com/en-us/pricing/details/virtual-machines/windows/.

[71]

P. Ta-Shma, A. Akbar, G. Gerson-Golan, G. Hadash, F. Carrez, and K. Moessner. 2018. An ingestion and analytics architecture for IoT applied to smart city use cases. IEEE Internet Things J. 5, 2 (2018), 765–774. DOI:https://doi.org/10.1109/JIOT.2017.2722378

[72]

Alessandro Bocci, Stefano Forti, Gian Luigi Ferrari, and Antonio Brogi. 2021. Secure FaaS orchestration in the fog: How far are we? Computing 103, 5 (2021), 1025–1056. DOI:

[73]

L. Lin, L. Pan, and S. Liu. 2020. Backup or not: An online cost optimal algorithm for data analysis jobs using spot instances. IEEE Access 8, (2020), 144945–144956. DOI:https://doi.org/10.1109/ACCESS.2020.3014978

[74]

Spot instances in AWS. 2022. Retrieved 11 October 2022 from https://aws.amazon.com/blogs/compute/running-high-scale-web-on-spot-instances/.

[75]

Jitendra Kumar and Ashutosh Kumar Singh. 2021. Performance evaluation of metaheuristics algorithms for workload prediction in cloud environment. Appl. Soft Comput. 113, (2021), 107895. DOI:

Digital Library

[76]

Masoumeh Etemadi, Mostafa Ghobaei-Arani, and Ali Shahidinejad. 2020. Resource provisioning for IoT services in the fog computing environment: An autonomic approach. Comput. Commun. 161, March (2020), 109–131. DOI:

[77]

Maryam Amiri, Leyli Mohammad-Khanli, and Raffaela Mirandola. 2018. A sequential pattern mining model for application workload prediction in cloud environment. J. Netw. Comput. Appl. 105, (2018), 21–62. DOI:

[78]

Ilia Pietri and Rizos Sakellariou. 2017. Mapping virtual machines onto physical machines in cloud computing: A survey. ACM Comput. Surv. 49, 3, Article 49 (September 2017), 30.

[79]

Shvan Omer, Sadoon Azizi, Mohammad Shojafar, and Rahim Tafazolli. 2021. A priority, power and traffic-aware virtual machine placement of IoT applications in cloud data centers. J. Syst. Archit. 115 (2021), 14. DOI:

Digital Library

[80]

Guangyao Zhou, Wenhong Tian, and Rajkumar Buyya. 2021. Deep reinforcement learning-based methods for resource scheduling in cloud computing: A review and future directions. Association for Computing Machinery. arXiv:2105.04086. Retrieved from http://arxiv.org/abs/2105.04086.

[81]

Syed Hamid Hussain Madni, Muhammad Shafie Abd Latiff, Yahaya Coulibaly, and Shafi’i Muhammad Abdulhamid. 2016. Resource scheduling for infrastructure as a service (IaaS) in cloud computing: Challenges and opportunities. J. Netw. Comput. Appl. 68, (2016), 173–200. DOI:

Digital Library

[82]

Mohammad Masdari, Sima ValiKardan, Zahra Shahi, and Sonay Imani Azar. 2016. Towards workflow scheduling in cloud computing: A comprehensive analysis. J. Netw. Comput. Appl. 66, (2016), 64–82. DOI:

Digital Library

[83]

Heena Wadhwa and Rajni Aron. 2022. TRAM: Technique for resource allocation and management in fog computing environment. J. Supercomput. 78, (2022), 667–690. DOI:

Digital Library

[84]

R. Madhura, B. Lydia Elizabeth, and V. Rhymend Uthariaraj. 2021. An improved list-based task scheduling algorithm for fog computing environment. Springer Vienna 103, (2021), 1353–1389. DOI:

Digital Library

[85]

Mohammad Goudarzi, Huaming Wu, Marimuthu Palaniswami, and Rajkumar Buyya. 2021. An application placement technique for concurrent IoT applications in edge and fog computing environments. IEEE Trans. Mob. Comput. 20, 4 (2021), 1298–1311. DOI:

[86]

Randa M. Abdelmoneem, Abderrahim Benslimane, and Eman Shaaban. 2020. Mobility-aware task scheduling in cloud-Fog IoT-based healthcare architectures. Comput. Networks 179, (2020), 107348. DOI:

[87]

Jiuyun Xu, Zhuangyuan Hao, Ruru Zhang, and Xiaoting Sun. 2019. A method based on the combination of laxity and ant colony system for cloud-fog task scheduling. IEEE Access 7, (2019), 116218–116226. DOI:

[88]

Hina Rafique, Munam Ali Shah, Saif Ul Islam, Tahir Maqsood, Suleman Khan, and Carsten Maple. 2019. A novel bio-inspired hybrid algorithm (NBIHA) for efficient resource management in fog computing. IEEE Access 7, (2019), 115760–115773. DOI:

[89]

Mohamed Abd Elaziz, Laith Abualigah, and Ibrahim Attiya. 2021. Advanced optimization technique for scheduling IoT tasks in cloud-fog computing environments. Futur. Gener. Comput. Syst. 124, (2021), 142–154. DOI:

Digital Library

[90]

Minhaj Ahmad Khan. 2022. A cost-effective power-aware approach for scheduling cloudlets in cloud computing environments. J. Supercomput. 78, (2022), 471–496. DOI:

Digital Library

[91]

Huaiying Sun, Huiqun Yu, and Guisheng Fan. 2020. Contract-based resource sharing for time effective task scheduling in fog-cloud environment. IEEE Trans. Netw. Serv. Manag. 17, 2 (2020), 1040–1053. DOI:

Digital Library

[92]

Samia Ijaz, Ehsan Ullah Munir, Saima Gulzar Ahmad, M. Mustafa Rafique, and Omer F. Rana. 2021. Energy-makespan optimization of workflow scheduling in fog-cloud computing. Computing 103, 9 (2021), 2033–2059. DOI:

Digital Library

[93]

Sarhad Arisdakessian, Omar Abdel Wahab, Azzam Mourad, Hadi Otrok, and Nadjia Kara. 2020. FoGMatch: An intelligent multi-criteria IoT-fog scheduling approach using game theory. IEEE/ACM Trans. Netw. 28, 4 (2020), 1779–1789. DOI:

Digital Library

[94]

Laith Abualigah, Ali Diabat, and Mohamed Abd Elaziz. 2021. Intelligent workflow scheduling for big data applications in IoT cloud computing environments. Cluster Comput. 24, 4 (2021), 2957–2976. DOI:

Digital Library

[95]

Khawar Hasham, Kamran Munir, and Richard McClatchey. 2018. Cloud infrastructure provenance collection and management to reproduce scientific workflows execution. Futur. Gener. Comput. Syst. 86, (2018), 799–820. DOI:

Digital Library

[96]

Vincenzo De Maio and Dragi Kimovski. 2020. Multi-objective scheduling of extreme data scientific workflows in fog. Futur. Gener. Comput. Syst. 106, (2020), 171–184. DOI:

Digital Library

[97]

Raafat O. Aburukba, Mazin AliKarrar, Taha Landolsi, and Khaled El-Fakih. 2020. Scheduling internet of things requests to minimize latency in hybrid fog-cloud computing. Futur. Gener. Comput. Syst. 111, (2020), 539–551. DOI:

[98]

Yi Wei, Li Pan, Shijun Liu, Lei Wu, and Xiangxu Meng. 2018. DRL-Scheduling: An intelligent QoS-Aware job scheduling framework for applications in clouds. IEEE Access 6, (2018), 55112–55125. DOI:

[99]

Samodha Pallewatta, Vassilis Kostakos, and Rajkumar Buyya. 2019. Microservices-based IoT application placement within heterogeneous and resource constrained fog computing environments. UCC 2019 - Proc. 12th IEEE/ACM Int. Conf. Util. Cloud Comput. (2019), 71–81. DOI:

Digital Library

[100]

Juan Luo, Luxiu Yin, Jinyu Hu, Chun Wang, Xuan Liu, Xin Fan, and Haibo Luo. 2019. Container-based fog computing architecture and energy-balancing scheduling algorithm for energy IoT. Futur. Gener. Comput. Syst. 97, (2019), 50–60. DOI:

Digital Library

[101]

Chu Ge Wu, Wei Li, Ling Wang, and Albert Y. Zomaya. 2021. Hybrid evolutionary scheduling for energy-efficient fog-enhanced internet of things. IEEE Trans. Cloud Comput. 9, 2 (2021), 641–653. DOI:

[102]

Djabir Abdeldjalil Chekired, Lyes Khoukhi, and Hussein T. Mouftah. 2018. Industrial IoT data scheduling based on hierarchical fog computing: A key for enabling smart factory. IEEE Trans. Ind. Informatics 14, 10 (2018), 4590–4602. DOI:

[103]

Ahmed A. A. Gad-Elrab and Amin Y. Noaman. 2020. A two-tier bipartite graph task allocation approach based on fuzzy clustering in cloud-fog environment. Futur. Gener. Comput. Syst. 103, (2020), 79–90. DOI:

Digital Library

[104]

Zahra Ghanbari, Nima Jafari Navimipour, Mehdi Hosseinzadeh, and Aso Darwesh. 2019. Resource allocation mechanisms and approaches on the Internet of Things. Cluster Comput. 22, 4 (2019), 1253–1282. DOI:

Digital Library

[105]

Akos Recse, Robert Szabo, and Balazs Nemeth. 2020. Elastic resource management and network slicing for IoT over edge clouds. In Proceedings of the 10th International Conference on the Internet of Things. Article 24 (October 2020), 8. DOI:

Digital Library

[106]

Tariq Qayyum, Zouheir Trabelsi, Asad Waqar Malik, and Kadhim Hayawi. 2021. Multi-level resource sharing framework using collaborative fog environment for smart cities. IEEE Access 9, (2021), 21859–21869. DOI:

[107]

Duong Tung Nguyen, Long Bao Le, and Vijay K. Bhargava. 2019. A market-based framework for multi-resource allocation in fog computing. IEEE/ACM Trans. Netw. 27, 3 (2019), 1151–1164. DOI:

Digital Library

[108]

Ranesh Kumar Naha, Saurabh Garg, Andrew Chan, and Sudheer Kumar Battula. 2020. Deadline-based dynamic resource allocation and provisioning algorithms in fog-cloud environment. Futur. Gener. Comput. Syst. 104, (2020), 131–141. DOI:

Digital Library

[109]

Wiem Matoussi and Tarek Hamrouni. 2022. A new temporal locality-based workload prediction approach for saas services in a cloud environment. J. King Saud Univ. - Comput. Inf. Sci. 34, 7 (2022), 3973–3987. DOI:

Digital Library

[110]

Lina Ni, Jinquan Zhang, and Jiguo Yu. 2018. Priced timed petri nets based resource allocation strategy for fog computing. Proc. - 2016 Int. Conf. Identification, Inf. Knowl. Internet Things, IIKI 2016 2018-Janua, 5 (2018), 39–44. DOI:

[111]

Lei Wei, Chuan Heng Foh, Bingsheng He, and Jianfei Cai. 2018. Towards efficient resource allocation for heterogeneous workloads in iaas clouds. IEEE Trans. Cloud Comput. 6, 1 (2018), 264–275. DOI:

[112]

Jing Bi, Shuang Li, Haitao Yuan, and Meng Chu Zhou. 2021. Integrated deep learning method for workload and resource prediction in cloud systems. Neurocomputing 424, (2021), 35–48. DOI:

[113]

Mirza Abdur Razzaq, Javed Ahmed Mahar, Muneer Ahmad, Najia Saher, Arif Mehmood, and Gyu Sang Choi. 2021. Hybrid auto-scaled service-cloud-based predictive workload modeling and analysis for smart campus system. IEEE Access 9, (2021), 42081–42089. DOI:

[114]

Irfan Mohiuddin and Ahmad Almogren. 2019. Workload aware VM consolidation method in edge/cloud computing for IoT applications. J. Parallel Distrib. Comput. 123, (2019), 204–214. DOI:

[115]

Mohamed K. Hussein and Mohamed H. Mousa. 2020. Efficient task offloading for IoT-Based applications in fog computing using ant colony optimization. IEEE Access 8, (2020), 37191–37201. DOI:

[116]

Pawan Kumar and Rakesh Kumar. 2019. Issues and challenges of load balancing techniques in cloud computing: A survey. ACM Comput. Surv. 51, 6, Article 120 (November 2019), 35.

[117]

Mandeep Kaur and Rajni Aron. 2021. A systematic study of load balancing approaches in the fog computing environment. J. Supercomput. 77, 8 (2021), 9202–9247. DOI:

Digital Library

[118]

Kaouther Gasmi, Selma Dilek, Suleyman Tosun, and Suat Ozdemir. 2022. A survey on computation offloading and service placement in fog computing-based IoT. Springer US. 78, (2022), 1983–2014. DOI:

Digital Library

[119]

Nisha Chaurasia, Mohit Kumar, Rashmi Chaudhry, and Om Prakash Verma. 2021. Comprehensive survey on energy-aware server consolidation techniques in cloud computing. J. Supercomput. 77, 10 (2021), 11682–11737. DOI:

Digital Library

[120]

Chu ge Wu, Wei Li, Ling Wang, and Albert Y. Zomaya. 2021. An evolutionary fuzzy scheduler for multi-objective resource allocation in fog computing. Futur. Gener. Comput. Syst. 117, (2021), 498–509. DOI:

[121]

Jiafu Wan, Baotong Chen, Shiyong Wang, Min Xia, Di Li, and Chengliang Liu. 2018. Fog computing for energy-aware load balancing and scheduling in smart factory. IEEE Trans. Ind. Informatics 14, 10 (2018), 4548–4556. DOI:

[122]

Ernando Batista, Gustavo Figueiredo, and Cassio Prazeres. 2022. Load balancing between fog and cloud in fog of things based platforms through software-defined networking. J. King Saud Univ. - Comput. Inf. Sci. 34, 9 (2022), 7111–7125. DOI:

Digital Library

[123]

Om Kolsoom Shahryari, Hossein Pedram, Vahid Khajehvand, and Mehdi Dehghan TakhtFooladi. 2020. Energy-efficient and delay-guaranteed computation offloading for fog-based IoT networks. Comput. Networks 182, August (2020), 1–16. DOI:

[124]

Rathinaraja Jeyaraj, V. S. Ananthanarayana, and Anand Paul. 2020. Improving mapreduce scheduler for heterogeneous workloads in a heterogeneous environment. Concurr. Comput. Pract. Exp. 32, 7 (2020), 1–10. DOI:

[125]

A. H. T. Dias, L. H. A. Correia, and N. Malheiros. 2022. A systematic literature review on virtual machine consolidation. ACM Comput. Surv. 54, 8, Article 176 (November 2022), 38.

[126]

Runqun Xiong, Xiuyang Li, Jiyuan Shi, Zhiang Wu, and Jiahui Jin. 2018. HirePool: Optimizing resource reuse based on a hybrid resource pool in the cloud. IEEE Access 6, (2018), 74376–74388. DOI:

[127]

Forbes. 2022. Retrieved 11 October 2022 from https://www.forbes.com/sites/forbestechcouncil/2021/05/03/renewable-energy-alone-cant-address-data-centers-adverse-environmental-impact/?sh=729ab68e5ddc.

[128]

Zhiming He, Yin Zhang, Byungchul Tak, and Limei Peng. 2020. Green fog planning for optimal internet-of-thing task scheduling. IEEE Access 8, (2020), 1224–1234. DOI:

[129]

Ehsan Ataie, Reza Entezari-Maleki, Sayed Ehsan Etesami, Bernhard Egger, Danilo Ardagna, and Ali Movaghar. 2018. Power-aware performance analysis of self-adaptive resource manage ment in iaas clouds. Futur. Gener. Comput. Syst. 86 (2018), 134–144. DOI:

Digital Library

[130]

M. Abbasi, E. Mohammadi-Pasand, and M. R. Khosravi. 2021. Intelligent workload allocation in IoT-Fog-cloud architecture towards mobile edge computing. Comput. Commun. 169, (2021), 71–80. DOI:

[131]

Wenyu Zhang, Zhenjiang Zhang, Sherali Zeadally, Han Chieh Chao, and Victor C. M. Leung. 2020. Energy-efficient workload allocation and computation resource configuration in distributed cloud/edge computing systems with stochastic workloads. IEEE J. Sel. Areas Commun. 38, 6 (2020), 1118–1132. DOI:

[132]

P. P. Ray. 2018. A survey on internet of things architectures. J. King Saud Univ. - Comput. Inf. Sci. 30, 3 (2018), 291–319. DOI:

[133]

Farooq Hoseiny, Sadoon Azizi, Mohammad Shojafar, and Rahim Tafazolli. 2021. Joint QoS-aware and cost-efficient task scheduling for fog-cloud resources in a volunteer computing system. ACM Trans. Internet Technol. 21, 4 (2021). DOI:

Digital Library

[134]

Xiaoli Wang and Bharadwaj Veeravalli. 2017. Performance characterization on handling large-scale partitionable workloads on heterogeneous networked compute platforms. IEEE Trans. Parallel Distrib. Syst. 28, 10 (2017), 2925–2938. DOI:

Digital Library

[135]

Chinmaya Kumar Swain and Aryabartta Sahu. 2022. Interference aware workload scheduling for latency sensitive tasks in cloud environment. Computing 104, (2022), 925–950. DOI:

Digital Library

[136]

R. Jeyaraj and A. Paul. 2022. Optimizing mapreduce task scheduling on virtualized heterogeneous environments using ant colony optimization. IEEE Access 10, (2022), 55842–55855. DOI:https://doi.org/10.1109/access.2022.3176729

[137]

Redowan Mahmud and Rajkumar Buyya. 2019. Modeling and simulation of fog and edge computing environments using ifogsim toolkit. Fog Edge Comput. Princ. Paradig. (2019), 433–165. DOI:

[138]

Timothy Wood, Ludmila Cherkasova, Kivanc Ozonat, and Prashant Shenoy. 2008. Profiling and modeling resource usage of virtualized applications. ACM/IFIP/USENIX International Conference on Distributed Systems Platforms and Open Distributed Processing (2008), 366–387. DOI:

Digital Library

[139]

Francisco Airton Silva, Iure Fé, and Glauber Gonçalves. 2021. Stochastic models for performance and cost analysis of a hybrid cloud and fog architecture. J. Supercomput. 77, 2 (2021), 1537–1561. DOI:

[140]

Ilyas Bambrik. 2020. A survey on cloud computing simulation and modeling. Springer Singapore SN COMPUT. SCI. 1, 249 (2020), 1–34.DOI:

[141]

N. Mansouri, R. Ghafari, and B. Mohammad Hasani Zade. 2020. Cloud computing simulators: A comprehensive review. Simul. Model. Pract. Theory 104, (2020). DOI:

[142]

Tariq Qayyum, Asad Waqar Malik, Muazzam A. Khan Khattak, Osman Khalid, and Samee U. Khan. 2018. FogNetSim++: A toolkit for modeling and simulation of distributed fog environment. IEEE Access 6, (2018), 63570–63583. DOI:

[143]

Harshit Gupta, Amir Vahid Dastjerdi, Soumya K. Ghosh, and Rajkumar Buyya. 2017. iFogSim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Softw. - Pract. Exp. 47, 9 (2017), 1275–1296. DOI:

Cited By

Wang SJiang XKhaskheli M(2024)The Role of Technology in the Digital Economy’s Sustainable Development of Hainan Free Trade Port and Genetic Testing: Cloud Computing and Digital LawSustainability10.3390/su1614602516:14(6025)Online publication date: 15-Jul-2024
https://doi.org/10.3390/su16146025
Hosseinzadeh MArabi ZAli SMin HMalik M(2024)Enhancing healthcare IoT systems for diabetic patient monitoring: Integration of Harris Hawks and grasshopper optimization algorithmsPLOS ONE10.1371/journal.pone.030152119:5(e0301521)Online publication date: 29-May-2024
https://doi.org/10.1371/journal.pone.0301521
Manocha ASood SBhatia M(2024)IoT-Dew Computing-Inspired Real-Time Monitoring of Indoor Environment for Irregular Health PredictionIEEE Transactions on Engineering Management10.1109/TEM.2023.333845871(1669-1682)Online publication date: 2024
https://doi.org/10.1109/TEM.2023.3338458
Show More Cited By

Index Terms

Resource Management in Cloud and Cloud-influenced Technologies for Internet of Things Applications
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
2. Networks
  1. Network properties
    1. Network reliability

Recommendations

Service priority queuing model-based internet of things middleware for load balancing among fog computing centres

The fog computing architectural component meets the major requirements of the internet of things (IoT) applications. Fog computing nodes are mainly used to handle delay sensitive requests generated by IoT devices. An increase in the service arrival rate ...
Cloud, Fog, or Mist in IoT? That Is the Question
Special Issue on Fog, Edge, and Cloud Integration

Internet of Things (IoT) has been commercially explored as Platforms as a Services (PaaS). The standard solution for this kind of service is to combine the Cloud computing infrastructure with IoT software, services, and protocols also known as CoT (...
Cloud resource provisioning: survey, status and future research directions

Cloud resource provisioning is a challenging job that may be compromised due to unavailability of the expected resources. Quality of Service (QoS) requirements of workloads derives the provisioning of appropriate resources to cloud workloads. Discovery ...

Comments

Information & Contributors

Information

Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 55, Issue 12

December 2023

825 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3582891

Editor:
Albert Zomaya
University of Sydney, Australia

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 March 2023

Online AM: 16 December 2022

Accepted: 17 October 2022

Revised: 31 August 2022

Received: 07 February 2022

Published in CSUR Volume 55, Issue 12

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Survey

Funding Sources

National Research Foundation of Korea
School of Computer Science and Engineering, Ministry of Education, Kyungpook National University, South Korea, through the BK21 Four Project, AI-Driven Convergence Software Education Research Program

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

21
Total Citations
View Citations
1,859
Total Downloads

Downloads (Last 12 months)949
Downloads (Last 6 weeks)49

Reflects downloads up to 09 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Wang SJiang XKhaskheli M(2024)The Role of Technology in the Digital Economy’s Sustainable Development of Hainan Free Trade Port and Genetic Testing: Cloud Computing and Digital LawSustainability10.3390/su1614602516:14(6025)Online publication date: 15-Jul-2024
https://doi.org/10.3390/su16146025
Hosseinzadeh MArabi ZAli SMin HMalik M(2024)Enhancing healthcare IoT systems for diabetic patient monitoring: Integration of Harris Hawks and grasshopper optimization algorithmsPLOS ONE10.1371/journal.pone.030152119:5(e0301521)Online publication date: 29-May-2024
https://doi.org/10.1371/journal.pone.0301521
Manocha ASood SBhatia M(2024)IoT-Dew Computing-Inspired Real-Time Monitoring of Indoor Environment for Irregular Health PredictionIEEE Transactions on Engineering Management10.1109/TEM.2023.333845871(1669-1682)Online publication date: 2024
https://doi.org/10.1109/TEM.2023.3338458
Li GTan HZhang XZhang CZhou RHan ZChen G(2024)Online Container Caching with Late-Warm for IoT Data Processing2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00127(1547-1560)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00127
Guo CPeng TZhang JYang YLuo YZhu G(2024)A Lightweight Pairing-Free Multi-Authority CP-ABE Scheme for Cloud-Edge-Assisted IoT2024 9th International Conference on Computer and Communication Systems (ICCCS)10.1109/ICCCS61882.2024.10602895(991-996)Online publication date: 19-Apr-2024
https://doi.org/10.1109/ICCCS61882.2024.10602895
Jeyaraj RSubramanian BYogesh KJin AGohel H(2024)YSAF: Yolo with Spatial Attention and FFT to Detect Face Spoofing Attacks2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC)10.1109/ICAIC60265.2024.10433802(1-6)Online publication date: 7-Feb-2024
https://doi.org/10.1109/ICAIC60265.2024.10433802
Malhotra AElsayed ATorres RVenkatraman S(2024)Evaluate Canary Deployment Techniques Using Kubernetes, Istio, and Liquibase for Cloud Native Enterprise Applications to Achieve Zero Downtime for Continuous DeploymentsIEEE Access10.1109/ACCESS.2024.341608712(87883-87899)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3416087
Marques GSenna CSargento SCarvalho LPereira LMatos R(2024)Proactive resource management for cloud of services environmentsFuture Generation Computer Systems10.1016/j.future.2023.08.005150:C(90-102)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.future.2023.08.005
Maldonado-Carrascosa FGarcía-Galán SValverde-Ibáñez MMarciniak TSzczerska MRuiz-Reyes N(2024)Game theory-based virtual machine migration for energy sustainability in cloud data centersApplied Energy10.1016/j.apenergy.2024.123798372(123798)Online publication date: Oct-2024
https://doi.org/10.1016/j.apenergy.2024.123798
Zhang DWang MMango JLi XXu X(2024)A survey on applications of reinforcement learning in spatial resource allocationComputational Urban Science10.1007/s43762-024-00127-z4:1Online publication date: 7-Jun-2024
https://doi.org/10.1007/s43762-024-00127-z
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents