A Review on Performance Analysis of Deep Learning for Task Offloading in Edge Computing
Main Article Content
Abstract
Edge computing is a type of distributed computing that was designed especially for Internet of Things (IoT) users to provide computational resources and data management nearby to users' devices. Introducing edge computing for IoT networks has reduced the bandwidth and latency issue while handling real-time applications. The major benefit of edge computing is that it reduces the communication overhead between IoT user and server. Integrating IoT in our daily life has attracted researchers towards its performance management such as complexity minimization, latency minimization, memory management, energy consumption minimization, etc. The paper focuses on deep reinforcement learning to minimize the computational complexity at IoT user end. The task offloading decision process is designed using Q-Learning that minimizes the system cost and curse of high dimensional data.
Downloads
Download data is not yet available.
Article Details
How to Cite
Almelu, S., Veenadhari, S., & Maheshwar, K. (2022). A Review on Performance Analysis of Deep Learning for Task Offloading in Edge Computing. SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology, 14(04), 159-164. https://doi.org/10.18090/samriddhi.v14i04.26
Section
Review Article
References
[1] Alfakih, T., Hassan, M. M., Gumaei, A., Savaglio, C., & Fortino, G. (2020). Task offloading and resource allocation for mobile edge computing by deep reinforcement learning based on
SARSA. IEEE Access, 8, 54074–54084. https://doi.org/10.1109/ ACCESS.2020.2981434
[2] Alelaiwi, A. (2019). An efficient method of computation offloading in an edge cloud platform. Journal of Parallel and Distributed Computing, 127, 58–64. https://doi.org/10.1016/J.
JPDC.2019.01.003
[3] Chen, J., Chen, S., Luo, S., Wang, Q., Cao, B., & Li, X. (2020).
An intelligent task offloading algorithm (iTOA) for UAV edge computing network. Digital Communications and Networks,
6(4), 433–443. https://doi.org/10.1016/J.DCAN.2020.04.008 [4] Chen, J., Chen, S., Wang, Q., Cao, B., Feng, G., & Hu, J. (2019). IRAF: A Deep Reinforcement Learning Approach for Collaborative Mobile Edge Computing IoT Networks. IEEE Internet of
Things Journal, 6(4), 7011–7024. https://doi.org/10.1109/ JIOT.2019.2913162
[5] Chen, X., Zhang, H., Wu, C., Mao, S., Ji, Y., & Bennis, M. (2019).
Optimized computation offloading performance in virtual
edge computing systems via deep reinforcement learning.
IEEE Internet of Things Journal, 6(3), 4005–4018. https://doi.
org/10.1109/JIOT.2018.2876279
[6] Deng, S., Huang, L., Taheri, J., & Zomaya, A. Y. (2015). Computation Offloading for Service Workflow in Mobile Cloud Computing.
IEEE Transactions on Parallel and Distributed Systems, 26(12), 3317–3329. https://doi.org/10.1109/TPDS.2014.2381640
[7] Guo, H., Lv, J., & Liu, J. (2019). Smart Resource Configuration and Task Offloading with Ultra-Dense Edge Computing.
International Conference on Wireless and Mobile Computing, Networking and Communications, 2019-October. https://doi.
org/10.1109/WIMOB.2019.8923227
[8] Higuchi, T., Ucar, S., & Altintas, O. (2019). Offloading Tasks to Vehicular Virtual Edge Servers. Proceedings - 2019 IEEE 16th International Conference on Mobile Ad Hoc and Smart Systems Workshops, MASSW 2019, 162–163. https://doi.org/10.1109/ MASSW.2019.00040
[9] Jiang, C., Fan, T., Qiu, Y., Wu, H., Zhang, J., Xiong, N. N., & Wan, J. (2018). Interdomain I/O Optimization in Virtualized Sensor Networks. Sensors (Basel, Switzerland), 18(12). https://doi.
org/10.3390/S18124395
[10] Jiang, C., Han, G., Lin, J., Jia, G., Shi, W., & Wan, J. (2019).
Characteristics of Co-Allocated Online Services and Batch Tasks in Internet Data Centers: A Case Study from Alibaba Cloud. IEEE Access, 7, 22495–22508. https://doi.org/10.1109/ ACCESS.2019.2897898
[11] Jiang, C., Wang, Y., Ou, D., Li, Y., Zhang, J., Wan, J., Luo, B., & Shi, W. (2019). Energy efficiency comparison of hypervisors.
Sustainable Computing: Informatics and Systems, 22, 311–321.
https://doi.org/10.1016/J.SUSCOM.2017.09.005
[12] Khayyat, M., Elgendy, I. A., Muthanna, A., Alshahrani, A. S., Alharbi, S., & Koucheryavy, A. (2020). Advanced Deep Learning- Based
Computational Offloading for Multilevel Vehicular Edge- Cloud Computing Networks. IEEE Access, 8, 137052–137062.
https://doi.org/10.1109/ACCESS.2020.3011705
[13] Li, Y., Qi, F., Wang, Z., Yu, X., & Shao, S. (2020). Distributed Edge Computing Offloading Algorithm Based on Deep Reinforcement Learning. IEEE Access, 8, 85204–85215. https://
doi.org/10.1109/ACCESS.2020.2991773
[14] Liu, X., Qin, Z., & Gao, Y. (2019). Resource Allocation for Edge Computing in IoT Networks via Reinforcement Learning. IEEE International Conference on Communications, 2019-May.
https://doi.org/10.1109/ICC.2019.8761385
[15] Liu, X., Yu, J., Wang, J., & Gao, Y. (2020). Resource Allocation With Edge Computing in IoT Networks via Machine Learning. IEEE Internet of Things Journal, 7(4), 3415–3426. https://doi.
org/10.1109/JIOT.2020.2970110
[16] Min, M., Xiao, L., Chen, Y., Cheng, P., Wu, D., & Zhuang, W. (2019).
Learning-Based Computation Offloading for IoT Devices with Energy Harvesting. IEEE Transactions on Vehicular Technology, 68(2), 1930– 1941. https://doi.org/10.1109/TVT.2018.2890685
[17] Nalluri, S. K., Parasaram, V. K. B., & Bathini, V. T. (2021). Autonomous Manufacturing Operations Using Intelligent MES and Cloud-Native Analytics. Journal of Multidisciplinary Knowledge, 1(1), 45–55.
Retrieved from
https://jmk.datatablets.com/index.php/j/article/view/127 [18] Qiu, Y., Jiang, C., Wang, Y., Ou, D., Li, Y., & Wan, J. (2019). Energy Aware Virtual Machine Scheduling in Data Centers. Energies 2019, Vol. 12, Page 646, 12(4), 646. https://doi.org/10.3390/ EN12040646 [19] Wang, C., & Li, Z. (2004). A computation offloading scheme on handheld devices. Journal of Parallel and Distributed Computing, 64(6), 740 –746. https://doi.org/10.1016/J.
JPDC.2003.10.005
[20] Wang, X., Han, Y., Wang, C., Zhao, Q., Chen, X., & Chen, M. (2019). In-edge AI: Intelligentizing mobile edge computing, caching and
communication by federated learning. IEEE Network, 33(5), 156–165.
https://doi.org/10.1109/MNET.2019.1800286
[21] Wang, X., Wang, J., Wang, X., & Chen, X. (2017). Energy and Delay Tradeoff for Application Offloading in Mobile Cloud Computing. IEEE Systems Journal, 11(2), 858–867. https://doi.org/10.1109/ JSYST.2015.2466617
[22] Wang, X., Wei, X., & Wang, L. (2019). A deep learning based energyefficient computational offloading method in Internet of vehicles.
China Communications, 16(3), 81–91. https://doi.
org/10.12676/J.CC.2019.03.008
[23] Wei, F., Chen, S., & Zou, W. (2018). A greedy algorithm for task offloading in mobile edge computing system. China Communications, 15(11), 158–170. https://doi.org/10.1109/ CC.2018.8543056
[24] Zhang, C., Liu, Z., Gu, B., Yamori, K., & Tanaka, Y. (2018). A Deep Reinforcement Learning Based Approach for Cost- and EnergyAware Multi-Flow Mobile Data Offloading. IEICE Transactions on Communications, E101B(7), 1625–1634. https://doi.org/10.1587/ transcom.2017CQP0014
SARSA. IEEE Access, 8, 54074–54084. https://doi.org/10.1109/ ACCESS.2020.2981434
[2] Alelaiwi, A. (2019). An efficient method of computation offloading in an edge cloud platform. Journal of Parallel and Distributed Computing, 127, 58–64. https://doi.org/10.1016/J.
JPDC.2019.01.003
[3] Chen, J., Chen, S., Luo, S., Wang, Q., Cao, B., & Li, X. (2020).
An intelligent task offloading algorithm (iTOA) for UAV edge computing network. Digital Communications and Networks,
6(4), 433–443. https://doi.org/10.1016/J.DCAN.2020.04.008 [4] Chen, J., Chen, S., Wang, Q., Cao, B., Feng, G., & Hu, J. (2019). IRAF: A Deep Reinforcement Learning Approach for Collaborative Mobile Edge Computing IoT Networks. IEEE Internet of
Things Journal, 6(4), 7011–7024. https://doi.org/10.1109/ JIOT.2019.2913162
[5] Chen, X., Zhang, H., Wu, C., Mao, S., Ji, Y., & Bennis, M. (2019).
Optimized computation offloading performance in virtual
edge computing systems via deep reinforcement learning.
IEEE Internet of Things Journal, 6(3), 4005–4018. https://doi.
org/10.1109/JIOT.2018.2876279
[6] Deng, S., Huang, L., Taheri, J., & Zomaya, A. Y. (2015). Computation Offloading for Service Workflow in Mobile Cloud Computing.
IEEE Transactions on Parallel and Distributed Systems, 26(12), 3317–3329. https://doi.org/10.1109/TPDS.2014.2381640
[7] Guo, H., Lv, J., & Liu, J. (2019). Smart Resource Configuration and Task Offloading with Ultra-Dense Edge Computing.
International Conference on Wireless and Mobile Computing, Networking and Communications, 2019-October. https://doi.
org/10.1109/WIMOB.2019.8923227
[8] Higuchi, T., Ucar, S., & Altintas, O. (2019). Offloading Tasks to Vehicular Virtual Edge Servers. Proceedings - 2019 IEEE 16th International Conference on Mobile Ad Hoc and Smart Systems Workshops, MASSW 2019, 162–163. https://doi.org/10.1109/ MASSW.2019.00040
[9] Jiang, C., Fan, T., Qiu, Y., Wu, H., Zhang, J., Xiong, N. N., & Wan, J. (2018). Interdomain I/O Optimization in Virtualized Sensor Networks. Sensors (Basel, Switzerland), 18(12). https://doi.
org/10.3390/S18124395
[10] Jiang, C., Han, G., Lin, J., Jia, G., Shi, W., & Wan, J. (2019).
Characteristics of Co-Allocated Online Services and Batch Tasks in Internet Data Centers: A Case Study from Alibaba Cloud. IEEE Access, 7, 22495–22508. https://doi.org/10.1109/ ACCESS.2019.2897898
[11] Jiang, C., Wang, Y., Ou, D., Li, Y., Zhang, J., Wan, J., Luo, B., & Shi, W. (2019). Energy efficiency comparison of hypervisors.
Sustainable Computing: Informatics and Systems, 22, 311–321.
https://doi.org/10.1016/J.SUSCOM.2017.09.005
[12] Khayyat, M., Elgendy, I. A., Muthanna, A., Alshahrani, A. S., Alharbi, S., & Koucheryavy, A. (2020). Advanced Deep Learning- Based
Computational Offloading for Multilevel Vehicular Edge- Cloud Computing Networks. IEEE Access, 8, 137052–137062.
https://doi.org/10.1109/ACCESS.2020.3011705
[13] Li, Y., Qi, F., Wang, Z., Yu, X., & Shao, S. (2020). Distributed Edge Computing Offloading Algorithm Based on Deep Reinforcement Learning. IEEE Access, 8, 85204–85215. https://
doi.org/10.1109/ACCESS.2020.2991773
[14] Liu, X., Qin, Z., & Gao, Y. (2019). Resource Allocation for Edge Computing in IoT Networks via Reinforcement Learning. IEEE International Conference on Communications, 2019-May.
https://doi.org/10.1109/ICC.2019.8761385
[15] Liu, X., Yu, J., Wang, J., & Gao, Y. (2020). Resource Allocation With Edge Computing in IoT Networks via Machine Learning. IEEE Internet of Things Journal, 7(4), 3415–3426. https://doi.
org/10.1109/JIOT.2020.2970110
[16] Min, M., Xiao, L., Chen, Y., Cheng, P., Wu, D., & Zhuang, W. (2019).
Learning-Based Computation Offloading for IoT Devices with Energy Harvesting. IEEE Transactions on Vehicular Technology, 68(2), 1930– 1941. https://doi.org/10.1109/TVT.2018.2890685
[17] Nalluri, S. K., Parasaram, V. K. B., & Bathini, V. T. (2021). Autonomous Manufacturing Operations Using Intelligent MES and Cloud-Native Analytics. Journal of Multidisciplinary Knowledge, 1(1), 45–55.
Retrieved from
https://jmk.datatablets.com/index.php/j/article/view/127 [18] Qiu, Y., Jiang, C., Wang, Y., Ou, D., Li, Y., & Wan, J. (2019). Energy Aware Virtual Machine Scheduling in Data Centers. Energies 2019, Vol. 12, Page 646, 12(4), 646. https://doi.org/10.3390/ EN12040646 [19] Wang, C., & Li, Z. (2004). A computation offloading scheme on handheld devices. Journal of Parallel and Distributed Computing, 64(6), 740 –746. https://doi.org/10.1016/J.
JPDC.2003.10.005
[20] Wang, X., Han, Y., Wang, C., Zhao, Q., Chen, X., & Chen, M. (2019). In-edge AI: Intelligentizing mobile edge computing, caching and
communication by federated learning. IEEE Network, 33(5), 156–165.
https://doi.org/10.1109/MNET.2019.1800286
[21] Wang, X., Wang, J., Wang, X., & Chen, X. (2017). Energy and Delay Tradeoff for Application Offloading in Mobile Cloud Computing. IEEE Systems Journal, 11(2), 858–867. https://doi.org/10.1109/ JSYST.2015.2466617
[22] Wang, X., Wei, X., & Wang, L. (2019). A deep learning based energyefficient computational offloading method in Internet of vehicles.
China Communications, 16(3), 81–91. https://doi.
org/10.12676/J.CC.2019.03.008
[23] Wei, F., Chen, S., & Zou, W. (2018). A greedy algorithm for task offloading in mobile edge computing system. China Communications, 15(11), 158–170. https://doi.org/10.1109/ CC.2018.8543056
[24] Zhang, C., Liu, Z., Gu, B., Yamori, K., & Tanaka, Y. (2018). A Deep Reinforcement Learning Based Approach for Cost- and EnergyAware Multi-Flow Mobile Data Offloading. IEICE Transactions on Communications, E101B(7), 1625–1634. https://doi.org/10.1587/ transcom.2017CQP0014