The Deadline- and Energy-Aware Resource Allocation using a Combination of Multi-Criteria Greedy Approach and Decision Tree in the IoT-Edge-Cloud Environment
Subject Areas : electrical and computer engineeringshiva Razzaghzadeh 1 * , Sara Hoseynpour 2
1 - Dept. of Com. Eng., Ardabil Branch, Islamic Azad University, Ardabil, Iran
2 - Dept. of Com. Eng., Ardabil Branch, Islamic Azad University, Ardabil, Iran
Keywords: Internet of things (IoT), resource allocation, scheduling, decision tree, multi-criteria greedy approach.,
Abstract :
With the rapid growth of the Internet of Things (IoT), the volume of data collected from sensors has increased significantly. As a result, there is a growing need to connect IoT devices to cloud servers to meet the demands of data storage, processing, and analysis. Furthermore, the emergence of intermediate technologies, such as fog computing, which performs initial computations on requests at the network edge, has reduced the computational load sent to the cloud. However, task scheduling in cloud resources remains a challenging problem. Resource scheduling, as an NP-Hard problem, involves the optimal and efficient allocation and distribution of resources (such as processors, memory, networks, etc.) to tasks in cloud servers. Therefore, many researchers have attempted to propose heuristic-based algorithms to find near-optimal solutions. In these approaches, the primary goal is to find the appropriate resource for task allocation, while the task’s execution deadline is not always considered. In IoT applications, the data may correspond to critical tasks that require quick responses, which has often been overlooked in previous methods. Therefore, this paper proposes a resource allocation approach using scheduling in the IoT-Fog-Cloud framework, based on a combination of decision trees for task prioritization and a multi-criteria greedy approach. Simulation results show that the proposed method, by prioritizing tasks and balancing multiple objectives using a multi-criteria greedy approach, performs near-optimally in terms of evaluation criteria such as cost and task completion time, and improves upon previous methods.
[1] E. S. Lee, J. H. Kim, and S. Y. Park, "Cloud-based Asthma monitoring system using IoT sensors," Healthcare Technology Letters, vol. 8, no. 4, pp. 123-128, Apr. 2021.
[2] M. A. Akkaş, R. Sokullu, and H. E. Cetin, "Healthcare and patient monitoring using IoT," Internet of Things, vol. 11, Article ID: 100173, Sept. 2020.
[3] N. Hossein Motlagh, M. Mohammadrezaei, J. Hunt, and B Zakeri, "Internet of Things (IoT) and the energy sector," Energies, vol. 13, no. 2, Article ID: 494, Jan.-2 2020.
[4] M. S. Farooq, S. Riaz, A. Abid, T. Umer, and Y.B. Zikria, "Role of IoT technology in agriculture: a systematic literature review," Electronics, vol. 9, no. 2, Article ID: 319, Feb. 2020.
[5] W. Almobaideen and M. Altarawneh, "Fog computing: survey on decoy information technology," International J. of Security and Networks, vol. 15, no. 2, pp. 111-121, 2020.
[6] G. Javadzadeh and A. M. Rahmani, "Fog computing applications in smart cities: a systematic survey," Wireless Networks, vol. 26, no. 2, pp. 1433-1457, Feb. 2020.
[7] P. Habibi, M. Farhoudi, S. Kazemian, S. Khorsandi, and A. Leon-Garcia, "Fog computing: a comprehensive architectural survey," IEEE Access, vol. 8, pp. 69105-69133, 2020.
[8] M. Haghi Kashani, A. M. Rahmani, and N. Jafari Navimipour, "Quality of service‐aware approaches in fog computing," International J. of Communication Systems, vol. 33, no. 8, Article ID: e4340, May 2020.
[9] J. Singh, P. Singh, and S. S. Gill, "Fog computing: a taxonomy, systematic review, current trends and research challenges," J. of Parallel and Distributed Computing, vol. 157, no. C, pp. 56-85, Nov. 2021.
[10] W. T. Vambe, C. Chang, and K. Sibanda, "A review of quality of service in fog computing for the internet of things," International J. of Fog Computing, vol. 3, no. 1, pp. 22-40, Jan. 2020.
[11] Y. Yang, "Multi-tier computing networks for intelligent IoT," Nature Electronics, vol. 2, no. 1, pp. 4-5, Jan. 2019.
[12] Z. Chang, Z. Zhou, T. Ristaniemi, and Z. Niu, "Energy efficient optimization for computation offloading in fog computing system," in Proc. 2017 IEEE Global Communications Conf., 6 pp., Singapore, 4-8 Dec. 2017.
[13] H. Sun, H. Yu, G. Fan, and L. Chen, "Energy and time efficient task offloading and resource allocation on the generic IoT-fog-cloud architecture," Peer-to-Peer Networking and Applications, vol. 13, pp. 548-563, 2020.
[14] R. K. Naha, S. Garg, A. H. Cheong Chan, and S. K. Battula, "Deadline-based dynamic resource allocation and provisioning algorithms in fog-cloud environment," Future Generation Computer Systems, vol. 104, pp. 131-141, Mar. 2020.
[15] S. Azizi, M. Shojafar, J. Abawajy, and R. Buyya, "Deadline-aware and energy-efficient IoT task scheduling in fog computing systems: a semi-greedy approach," J. of Network and Computer Applications, vol. 201, Article ID: 103333, May 2022.
[16] Z. Zhou, H. Xie, and F. Li, "A novel task scheduling algorithm integrated with priority and greedy strategy in cloud computing," J. of Intelligent & Fuzzy Systems, vol. 37, no. 4, pp. 4647-4655, Oct. 2019.
[17] I. Z. Yakubu, L. Muhammed, Z. A. Musa, Z. I. Matinja, and I. M. Adamu, "A multi agent based dynamic resource allocation in fog-cloud computing environment," Trends in Sciences, vol. 18, no. 22, pp. 413-413, 15 Nov. 2021.
[18] M. Sriraghavendra, et al., "DoSP: a deadline-aware dynamic service placement algorithm for workflow-oriented IoT applications in fog-cloud computing environments," Energy Conservation Solutions for Fog-Edge Computing Paradigms, vol. 54, no. 4, pp. 21-47, Apr. 2022.
[19] S. Singh, P. Singh, and S. Tanwar, "Energy aware resource allocationvia MS-SLnO in cloud data center," Multimedia Tools and Applications, vol. 82, no. 29, pp. 45541-45563, May 2023.
[20] E. Al-Masri, et al., "Energy-efficient cooperative resource allocation and task scheduling for Internet of Things environments," Internet of Things, vol. 23, Article ID: 100832, Oct. 2023.
[21] P. A. Malla and S. Sheikh, "Analysis of QoS aware energy‐efficient resource provisioning techniques in cloud computing," International J. of Communication Systems, vol. 36, no. 1, Article ID: e5359, 2023.
[22] K. Ajmera and T. K. Tewari, "Energy-efficient virtual machine scheduling in IaaS cloud environment using energy-aware green-particle swarm optimization," International J. of Information Technology, vol. 15, no. 4, pp. 1927-1935, 2023.
[23] R. Jeyaraj, et al., "Resource management in cloud and cloud-influenced technologies for internet of things applications," ACM Computing Surveys, vol. 55, no. 12, pp. 1-37, 2023.
[24] S. K. Chowdhary and A. L. N. Rao, "QoS and reliability aware matched bald eagle task scheduling framework based on IoT-cloud in educational applications," Cluster Computing, vol. 27, no. 6, pp. 8141-8158, 2024.
[25] M. Afzali, A. Mohammad Vali Samani, and H. R. Naji, "An efficient resource allocation of IoT requests in hybrid fog-cloud environment," The J. of Supercomputing, vol. 80, no. 4, pp. 4600-4624, 2024.
[26] M. Nematollahi, A. Ghaffari, and A. Mirzaei, "Task and resource allocation in the internet of things based on an improved version of the moth-flame optimization algorithm," Cluster Computing, vol. 27, no. 2, pp. 1775-1797, Apr. 2024.
[27] S. Rac and M. Brorsson, "Cost-aware service placement and scheduling in the edge-cloud continuum," ACM Trans. on Architecture and Code Optimization, vol. 21, no. 2, pp. 1-24, 2024.
[28] I. Kaur and P. Mann, "A hybrid cost-effective genetic and firefly algorithm for workflow scheduling in cloud," In: D. Gupta, et al., (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol. 1166, Springer, Singapore, pp. 33-45, 2021.
[29] M. Kumar, J. K. Samriya, K. Dubey, and S. S. Gill, "QoS‐aware resource scheduling using whale optimization algorithm for microservice applications," Software: Practice and Experience, vol. 54, no. 4, pp. 546-565, 2024.