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General Article

A Machine learning-based framework for energy-efficient load balancing in sustainable urban infrastructure and smart buildings
Aarti Sharma1
,
Naveen Kumar2
,
Chander Diwaker3*
,
Bandana Sharma4
,
Ritu Baniwal5
,
Shyama Barna Bhattacharjee6
,
Seema Rani7
1PhD Scholar, Department of Computer Science and Engineering, UIET, KU, Kurukshetra, India
2Software Engineer, Deapartent of Software Engineering, Salesforce Inc Dallas, Texas, United State of America
3Assistant Professor, Department of Computer Science and Engineering, UIET, KU, Kurukshetra, India
4Associate Professor, Department of Computer Science and Engineering, MMU, Mullana, India
5Assistant Professor, Department of Computer Science and Application, JPGC, Radaur, India
6Lecturer, Department of Science, Engineering and Technology, USTC, Chattogram, Bangladesh
7Assistant Professor, Ambala College of Engineering and Applied Research, Mithapur, Ambala, India
*Corresponding Author
31 December 2024. pp. 498-512
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Abstract

Efficiency of energy consumption is a key element in respect of the sustainable development of cities and smart buildings. Mobile Edge Computing (MEC) is already becoming one of the promising technologies to support application of smart cities that has higher data throughput, low latency and energy efficiency without depending on the centralized data center. Nevertheless, the concern of optimizing resource management in these environments continues to be complex. In this paper, a new machine learning scheme for energy-aware load distribution in MEC systems is proposed to support green infrastructure in smart cities, particularly for smart building applications. Our framework uses real-time monitoring data of resource usage, energy consumption, and service level agreement (SLA) breaches for dynamic load balancing for energy-efficient operations. The future work involves the application of the proposed model for optimizing response time, energy consumption, and SLA by 13.4%, 6.74% and 32.7% respectively than traditional methods, while enhancing model’s accuracy by 1.06%. This framework can be easily implemented into smart building management systems as well as into the infrastructure of smart cities to increase energy efficiency, decrease operating costs, and foster sustainable usage of urban services.

Keywords
energy efficiency
mobile edge computing (MEC)
smart buildings
sustainable urban infrastructure
resource utilization
reinforcement learning
smart city technologies
sustainable building management
References
1

Z. Ali, L. Jiao, T. Baker, G. Abbas, Z.H. Abbas, and S. Khaf, A deep learning approach for energy efficient computational offloading in mobile edge computing. IEEE Access. (7) (2019), pp. 149623-149633.

10.1109/ACCESS.2019.2947053
2

S. Tuli, S. Ilager, K. Ramamohanarao, and R. Buyya, Dynamic scheduling for stochastic edge-cloud computing environments using a3c learning and residual recurrent neural networks. IEEE transactions on Mobile Computing. 21(3) (2020), pp. 940-954.

10.1109/TMC.2020.3017079
3

D. Saxena, A.K. Singh, and R. Buyya, OP-MLB: an online VM prediction-based multi-objective load balancing framework for resource management at cloud data center. IEEE Transactions on Cloud Computing. 10(4) (2021), pp. 2804-2816.

10.1109/TCC.2021.3059096
4

S. Mohammed, O. Ma, S. Ansari, and S.K. Gupta, Collaboration of Drone and Internet of Public Safety Things in Smart Cities: An Overview of QoS and Network Performance Optimization. Drones, MDPI, 3(1) (2019), pp. 1-18.

10.3390/drones3010013
5

A. Shakarami, M. Arani, and A. Shahidinejad, A Survey on the Computation Offloading Approaches in Mobile Edge Computing: A Machine Learning-Based Perspective. Computer Network. 182 (2020), pp. 1-24.

10.1016/j.comnet.2020.107496
6

D. Sharma, S.K. Gupta, A. Rashid, S. Gupta, M. Rashid, and A. Srivastava, A novel approach for securing data against intrusion attacks in unmanned aerial vehicles integrated heterogeneous network using functional encryption technique. Transactions on Emerging Telecommunications Technologies, Wiley. 32(7) (2020), pp. 1-32.

10.1002/ett.4114
7

A. Gupta, S.K. Gupta, M. Rashid, A. Khan, and M. Manjul, Unmanned aerial vehicles integrated HetNet for smart dense urban area. Transactions on Emerging Telecommunications Technologies, Wiley. 33(10) (2020), pp. 1-22.

10.1002/ett.4123
8

S.K. Zaman, A.I. Jehangiri, T. Maqsood, N.U. Haq, A.I. Umar, J. Shuja, Z. Ahmad, I.B. Dhaou, and M.F. Alsharekh, LiMPO: Lightweight mobility prediction and offloading framework using machine learning for mobile edge computing. Cluster Computing. 26(1) (2023), pp. 99-117.

10.1007/s10586-021-03518-7
9

Y. Cho, M. Oh, S. Park, and S. Lee. Efficiency and possibility of data-driven smart cities. International Journal of Sustainable Building Technology and Urban Development. 12(4) (2021), pp. 323-334.

10

H. Gupta, A. Vahid Dastjerdi, S.K. Ghosh, and R. Buyya, ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience. 47(9) (2017), pp. 1275-1296.

10.1002/spe.2509
11

C. Kwon and Y. Ahn, Critical views on AI (Artificial Intelligence) in building design. International Journal of Sustainable Building Technology and Urban Development. 15(2) (2024), pp. 240-246.

12

I. Sharma, S.K. Gupta, A. Mishra, and S. Askar, Synchronous Federated Learning based Multi Unmanned Aerial Vehicles for Secure Applications. Scalable Computing: Practice and Experience, 24(3) (2023), pp. 191-201.

10.12694/scpe.v24i3.2136
13

T. Deepika and N.M. Dhanya, Multi-objective prediction-based optimization of power consumption for cloud data centers. Arabian Journal for Science and Engineering. 48(2) (2023), pp. 1173-1191.

10.1007/s13369-022-06694-9
14

V.D.A. Kumar, A. Kumar, R.S. Batth, M. Rashid, S.K. Gupta, and R. Manish, Efficient Data Transfer in Edge Envisioned Environment using Artificial Intelligence based Edge Node Algorithm. Transactions on Emerging Telecommunications Technologies, Wiley. 32(6) (2020), pp. 1-15.

10.1002/ett.4110
15

M. Dabbagh, B. Hamdaoui, M. Guizani, and A. Rayes, An energy-efficient VM prediction and migration framework for overcommitted clouds. IEEE Transactions on Cloud Computing. 6(4) (2016), pp. 955-966.

10.1109/TCC.2016.2564403
16

N.T. Hieu, M. Di Francesco, and A. Ylä-Jääski, Virtual machine consolidation with multiple usage prediction for energy-efficient cloud data centers. IEEE Transactions on Services Computing. 13(1) (2017), pp. 186-199.

10.1109/TSC.2017.2648791
17

P. Kaur, A.M. Mishra, N. Goyal, S.K. Gupta, A. Shankar, and W. Viriyasitavat, A Novel Hybrid CNN Methodology for Automated Leaf Disease Detection and Classification. Expert Systems, Wiley. 41(8) (2024), pp. 1-18.

10.1111/exsy.13543
18

M. Al-Khafajiy, T. Baker, A. Waraich, D. Al-Jumeily, and A. Hussain, IoT-fog optimal workload via fog offloading. In 2018 IEEE/ACM international conference on utility and cloud computing companion (UCC companion), IEEE. (2018), pp. 359-364.

10.1109/UCC-Companion.2018.00081
19

H. Mazouzi, N. Achir, and K. Boussetta, Dm2-ecop: An efficient computation offloading policy for multi-user multi-cloudlet mobile edge computing environment. ACM Transactions on Internet Technology (TOIT). 19(2) (2019), pp. 1-24.

10.1145/3241666
20

T.H. Nguyen, M. Di Francesco, and A. Yla-Jaaski, Virtual machine consolidation with multiple usage prediction for energy-efficient cloud data centers. IEEE Trans. On Serv. Comput. (2017).

21

L. Yu, L. Chen, Z. Cai, H. Shen, Y. Liang, and Y. Pan, Stochastic load balancing for virtual resource management in datacenters. IEEE Transactions on Cloud Computing. 8(2) (2016), pp. 459-472.

10.1109/TCC.2016.2525984
22

A. Gupta and S.K. Gupta, A Study on Secured Unmanned Aerial Vehicle-based Fog Computing Networks. SAE International Journal of Connected and Automated Vehicles. 7(2) (2024), pp. 1-11.

10.4271/12-07-02-0011
23

M. Dabbagh, B. Hamdaoui, M. Guizani, and A. Rayes, An energy efficient prediction and migration framework for overcommitted clouds. IEEE Trans. On Cloud Comput. (4) (2018), pp. 955-966.

10.1109/TCC.2016.2564403
24

S.K. Gupta, P. Gupta, and P. Singh, Enhancing UAV-HetNet Security Through Functional Encryption Framework. Concurrency and Computation: Practice and Experience, Wiley. 36(20) (2024), pp. 1-22.

10.1002/cpe.8206
25

F. Syed, S.H. Alsamhi, S.K. Gupta, and A. Saif, LSB-XOR Technique for Securing Captured Images from Disaster by UAVs in B5G Networks. Concurrency and Computation: Practice and Experience, Wiley. 36(12) (2024), pp. 1-13.

10.1002/cpe.8061
26

Q. Zhang, M. Lin, L.T. Yang, Z. Chen, S.U. Khan, and P. Li, A double deep Q learning model for energy-efficient edge scheduling. IEEE Transactions on Services Computing. (preprint). (2018).

27

I. Sharma and S.K. Gupta, Channel Tracking in IRS-based UAV Communication Systems using Federated Learning. Journal of Electrical Engineering. 74(6) (2023), pp. 521-531.

10.2478/jee-2023-0060
28

A. Mishra and S.K. Gupta, Intelligent Classification of Coal Seams Using Spontaneous Combustion Susceptibility in IoT Paradigm. International Journal of Coal Preparation and Utilization, Taylor & Francis. (2023), pp. 1-23.

10.1080/19392699.2023.2217747
29

D. Saxena and A.K. Singh, Security embedded dynamic resource allocation model for cloud data center. Electronics Letters. 56(20) (2020), pp. 1062-1065.

10.1049/el.2020.1736
30

L. Zhao, L. Lu, Z. Jin, and C. Yu, Online virtual machine placement for increasing cloud provider's revenue. IEEE Transactions on Services Computing. 10(2) (2015), pp. 273-285.

10.1109/TSC.2015.2447550
Information
  • Publisher :Sustainable Building Research Center (ERC) Innovative Durable Building and Infrastructure Research Center
  • Publisher(Ko) :건설구조물 내구성혁신 연구센터
  • Journal Title :International Journal of Sustainable Building Technology and Urban Development
  • Volume : 15
  • No :4
  • Pages :498-512
  • Received Date : 2024-11-01
  • Accepted Date : 2024-11-30
  • DOI :https://doi.org/10.22712/susb.20240035
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