International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

User-Centric Evaluation and Optimization of Resource Allocation in Edge Computing Environments

Abstract

The proliferation of mobile applications with intensive computational demands has necessitated the adoption of edge computing to reduce latency and energy consumption. However, edge servers face challenges such as limited resources, dynamic wireless conditions, and inefficient task offloading strategies, particularly in multi-user environments. This paper proposes a user-centric evaluation and optimization framework for resource allocation in edge computing, aiming to minimize latency and energy consumption while maximizing system efficiency. We introduce a greedy-competitive algorithm for dynamic task offloading and a joint communication-computation optimization model that adapts to real-time channel conditions and user requirements. The proposed approach leverages partial task offloading, dynamic voltage frequency scaling (DVFS), and optimal resource partitioning between edge and cloud servers. Simulation results demonstrate significant improvements in energy efficiency (up to 21.2% reduction) and latency reduction (up to 20% fewer task drops) compared to conventional greedy and local execution strategies. The study provides insights into optimal resource allocation, task scheduling, and energy-delay trade-offs in edge computing environments.

Country : West Africa / China

1 Mohamed Koroma (Ing)2 Alimamy Saidu konteh3 Yahya Labay Kamara4 Chernor Gurasiue Jalloh5 Justin Alhaji Conteh (Ing)

  1. Lecturer, School of Technology, Computer Science & I.T Department, Njala University, Sierra Leone, West Africa
  2. Lecturer, Milton Margai Technical University, Electrical & Electronics Department Sierra Leone, West Africa
  3. Lecturer, School of Technology, Computer Science & I.T Department, Njala University, Sierra Leone, West Africa
  4. Software Engineer, Nankai University, School of Software Engineering, Tianjin City, P.R. China
  5. MSc Scholar, Electrical Engineering, Xi’an Jiaotong University, Xi’an city, P.R. China

IRJIET, Volume 9, Issue 6, June 2025 pp. 80-100

doi.org/10.47001/IRJIET/2025.906010

Full Paper
Download

References

  1. T. Chen, Y. Zhang, X. Wang, and G. B. Giannakis, “Robust Workload and Energy Management for Sustainable Data Centers,” IEEE J. Sel. Areas Commun., vol. 34, no. 3, pp. 651–664, 2016, doi: 10.1109/JSAC.2016.2525618.
  2. V. Di Valerio and F. Lo Presti, “Optimal Virtual Machines allocation in mobile femto-cloud computing: An MDP approach,” 2014 IEEE Wirel. Commun. Netw. Conf. Work. WCNCW 2014, pp. 7–11, 2014, doi: 10.1109/WCNCW.2014.6934852.
  3. W. Xu, Wu, Daneshmand, Liu, “A data privacy protective mechanism for WBAN,” Wirel. Commun. Mob. Comput., no. February 2015, pp. 421–430, 2015, doi: 10.1002/wcm.
  4. L. Chen and H. Shen, “Consolidating complementary VMs with spatial/temporal-awareness in cloud datacenters,” Proc. - IEEE INFOCOM, pp. 1033–1041, 2014, doi: 10.1109/INFOCOM.2014.6848033.
  5. C. Dong, F. Kong, X. Liu, and H. Zeng, “Green power analysis for Geographical Load Balancing based datacenters,” 2013 Int. Green Comput. Conf. Proceedings, IGCC 2013, 2013, doi: 10.1109/IGCC.2013.6604504.
  6. T. Zhao, S. Zhou, X. Guo, Y. Zhao, and Z. Niu, “A cooperative scheduling scheme of local cloud and internet cloud for delay-aware mobile cloud computing,” 2015 IEEE Globecom Work. GC Wkshps 2015 - Proc., 2015, doi: 10.1109/GLOCOMW.2015.7414063.
  7. Y. Zhang, J. He, and S. Guo, “Energy-efficient dynamic task offloading for energy harvesting mobile cloud computing,” 2018 IEEE Int. Conf. Networking, Archit. Storage, NAS 2018 - Proc., pp. 1–4, 2018, doi: 10.1109/NAS.2018.8515736.
  8. Y. Ma and C. Lee, “Technology and Science InfiniBand Virtualization on KVM,” pp. 777–781, 2012.
  9. Z. Chang et al., “Energy Efficient Resource Allocation for Wireless Power Transfer Enabled Collaborative Mobile Clouds,” IEEE J. Sel. Areas Commun., vol. 34, no. 12, pp. 3438–3450, 2016, doi: 10.1109/JSAC.2016.2611843.
  10. Y. Zhang, H. Liu, L. Jiao, and X. Fu, “To offload or not to offload: An efficient code partition algorithm for mobile cloud computing,” 2012 1st IEEE Int. Conf. Cloud Networking, CLOUDNET 2012 - Proc., pp. 80–86, 2012, doi: 10.1109/CloudNet.2012.6483660.
  11. J. Xu, L. Chen, and S. Ren, “Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing,” IEEE Trans. Cogn. Commun. Netw., vol. 3, no. 3, pp. 361–373, 2017, doi: 10.1109/TCCN.2017.2725277.
  12. S. Cao, X. Tao, Y. Hou, and Q. Cui, “An energy-optimal offloading algorithm of mobile computing based on HetNets,” 2015 Int. Conf. Connect. Veh. Expo, ICCVE 2015 - Proc., pp. 254–258, 2016, doi: 10.1109/ICCVE.2015.68.
  13. A.Beloglazov and R. Buyya, “Energy efficient resource management in virtualized cloud data centers,” CCGrid 2010 - 10th IEEE/ACM Int. Conf. Clust. Cloud, Grid Comput., pp. 826–831, 2010, doi: 10.1109/CCGRID.2010.46.
  14. X. Sun, N. Ansari, and Q. Fan, “Green energy aware avatar migration strategy in green cloudlet networks,” Proc. - IEEE 7th Int. Conf. Cloud Comput. Technol. Sci. CloudCom 2015, pp. 139–146, 2016, doi: 10.1109/CloudCom.2015.23.
  15. S. Ulukus et al., “Energy Harvesting Wireless Communications: A Review of Recent Advances,” IEEE J. Sel. Areas Commun., vol. 33, no. 3, pp. 360–381, 2015, doi: 10.1109/JSAC.2015.2391531.
  16. S. Barbarossa, S. Sardellitti, and P. Di Lorenzo, “Communicating while computing: Distributed mobile cloud computing over 5G heterogeneous networks,” IEEE Signal Process. Mag., vol. 31, no. 6, pp. 45–55, 2014, doi: 10.1109/MSP.2014.2334709.
  17. S. Sudevalayam and P. Kulkarni, “Energy harvesting sensor nodes: Survey and implications,” IEEE Commun. Surv. Tutorials, vol. 13, no. 3, pp. 443–461, 2011, doi: 10.1109/SURV.2011.060710.00094.
  18. M. Galster, “Software reference architectures: Related architectural concepts and challenges,” CobRA 2015 - Proc. 1st Int. Work. Explor. Component-Based Tech. Constr. Ref. Archit. Part CompArch 2015, no. 1, pp. 5–8, 2015, doi: 10.1145/2755567.2755570.
  19. G. Sivakumar, F. Abrahams, K. Hogg, and J. Hartley, “SOI (Service Oriented Integration) and SIMM (Service Integration Maturity Model An Analysis,” Proc. - 2010 6th World Congr. Serv. Serv. 2010, pp. 178–182, 2010, doi: 10.1109/SERVICES.2010.55.
  20. R. Roman, J. Lopez, and M. Mambo, “Mobile edge computing, Fog et al.: A survey and analysis of security threats and challenges,” Futur. Gener. Comput. Syst., vol. 78, pp. 680–698, 2018, doi: 10.1016/j.future.2016.11.009.
  21. M. Satyanarayanan, P. Bahl, R. Cáceres, and N. Davies, “The case for VM-based cloudlets in mobile computing,” IEEE Pervasive Comput., vol. 8, no. 4, pp. 14–23, 2009, doi: 10.1109/MPRV.2009.82.
  22. C. Ragona, F. Granelli, C. Fiandrino, D. Kliazovich, and P. Bouvry, “Energy-efficient computation offloading for wearable devices and smartphones in mobile cloud computing,” 2015 IEEE Glob. Commun. Conf. GLOBECOM 2015, 2015, doi: 10.1109/GLOCOM.2014.7417039.
  23. Y. Mao, Y. Luo, J. Zhang, and K. B. Letaief, “Energy harvesting small cell networks: Feasibility, deployment, and operation,” IEEE Commun. Mag., vol. 53, no. 6, pp. 94–101, 2015, doi: 10.1109/MCOM.2015.7120023.
  24. S. E. Mahmoodi, R. N. Uma, and K. P. Subbalakshmi, “Optimal joint scheduling and cloud offloading for mobile applications,” IEEE Trans. Cloud Comput., vol. 7, no. 2, pp. 301–313, 2019, doi: 10.1109/TCC.2016.2560808.
  25. G. A. Zhang, J. Y. Gu, Z. H. Bao, C. Xu, and S. B. Zhang, “Joint routing and channel assignment algorithms in cognitive wireless mesh networks,” Trans. Emerg. Telecommun. Technol., vol. 25, no. 3, pp. 294–307, 2014, doi: 10.1002/ett.
  26. Y. Luo, J. Zhang, and K. B. Letaief, “Transmit Power Minimization for Wireless Networks with Energy Harvesting Relays,” IEEE Trans. Commun., vol. 64, no. 3, pp. 987–1000, 2016, doi: 10.1109/TCOMM.2016.2519418.
  27. W. Lee, R. Panda, D. Sunwoo, J. Joao, A. Gerstlauer, and L. K. John, “BUQS: Battery- and user-aware QoS scaling for interactive mobile devices,” Proc. Asia South Pacific Des. Autom. Conf. ASP-DAC, vol. 2018-Janua, pp. 64–69, 2018, doi: 10.1109/ASPDAC.2018.8297284.
  28. X. Li, J. Wu, S. Tang, and S. Lu, “Let’s stay together: Towards traffic aware virtual machine placement in data centers,” Proc. - IEEE INFOCOM, pp. 1842–1850, 2014, doi: 10.1109/INFOCOM.2014.6848123.
  29. X. Guo, R. Singh, T. Zhao, and Z. Niu, “An index based task assignment policy for achieving optimal power-delay tradeoff in edge cloud systems,” 2016 IEEE Int. Conf. Commun. ICC 2016, 2016, doi: 10.1109/ICC.2016.7511147.
  30. Z. Han, H. Tan, G. Chen, R. Wang, Y. Chen, and F. C. M. Lau, “Dynamic virtual machine management via approximate Markov decision process,” Proc. - IEEE INFOCOM, vol. 2016-July, 2016, doi: 10.1109/INFOCOM.2016.7524384.
  31. S. Patole, “A Survey of Mobile Cloud Computing,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 7, no. 6, pp. 2438–2441, 2019, doi: 10.22214/ijraset.2019.6411.
  32. W. Labidi, M. Sarkiss, and M. Kamoun, “Joint multi-user resource scheduling and computation offloading in small cell networks,” 2015 IEEE 11th Int. Conf. Wirel. Mob. Comput. Netw. Commun. WiMob 2015, no. Ict, pp. 794–801, 2015, doi: 10.1109/WiMOB.2015.7348043.
  33. A.Ahmed and E. Ahmed, “A Survey on Mobile Edge Computing.”
  34. K. N. Pappi, G. K. Karagiannidis, and R. Schober, “How Sensitive is Compute-and-Forward to Channel Estimation Errors ?,” no. 1, pp. 3110–3114, 2013.
  35. F. Wang and X. Zhang, “Dynamic Computation Offloading and Resource Allocation Over Mobile Edge Computing Networks,” 2018 IEEE Int. Conf. Commun., pp. 1–6.
  36. L. Zhang, Z. Zhao, Q. Wu, H. Zhao, H. Xu, and X. Wu, “Energy-Aware Dynamic Resource Allocation in UAV Assisted Mobile Edge Computing over Social Internet of Vehicles,” IEEE Access, vol. PP, no. c, p. 1, 2018, doi: 10.1109/ACCESS.2018.2872753.
  37. J. Ren, G. Yu, S. Member, Y. He, and G. Y. Li, “Collaborative Cloud and Edge Computing for Latency Minimization,” vol. XX, no. X, pp. 1–14, 2019, doi: 10.1109/TVT.2019.2904244.
  38. P. Mach and Z. Becvar, “Mobile Edge Computing : A Survey on Architecture and Computation Offloading,” vol. 19, no. 3, pp. 1628–1656, 2017.
  39. D. Wang et al., “Adaptive Wireless Video Streaming based on Edge Computing : Opportunities and Approaches,” vol. 1374, no. c, pp. 1–12, 2018, doi: 10.1109/TSC.2018.2828426.

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies