AI Workload Allocation Methods for Edge-Cloud Computing: A Review


  • Sarah Ammar Rafea College of Information Eng., Al-Nahrain University, Baghdad, Iraq
  • Ammar Dawood Jasim College of Information Eng., Al-Nahrain University, Baghdad, Iraq



Cloud computing, Edge computing, Internet of Things, Artificial intelligence, Workload allocation


Edge computing is used with cloud computing as an extension to increase the performance of delay-sensitive applications such as autonomous vehicles, healthcare systems, video surveillance systems, ..etc. The fast increase in the Internet of Things (IoT) devices increases the amount of data transferred in the network. IoT devices are resource-constrained in terms of energy consumption and computation capability. Data processing near IoT devices enabled by edge devices. Hence reduces the transmission power of sending data to the cloud and causes delays due to the cloud being placed far away from the devices. Most real-time applications depend on artificial intelligence (AI) techniques, increasing the computations on IoT-edge devices. Conversely, if this AI workload is executed on the cloud, the delay increase causes degradation in application performance. How to decide where the computation is done in an IoT, edge and cloud network is an important issue. The purpose of optimizing the workload allocation decision is to increase the application performance in terms of Quality of Experience (QoE) and Quality of Service (QoS); hence, the major goal is to reduce the delay time while maintaining the accuracy of the AI systems. As presented in this review, many researchers focus on proposing a workload allocation decision based on AI techniques. In contrast, other research focuses on the AI workload, hence presenting a method for partitioning the AI model to increase the system's accuracy in the resource constraint devices (end device and edge server). Many other researches also used the AI model for resource allocation and provisioning between edge servers and the cloud. In this review, the integration between AI and edge–cloud environment is investigated, the AI workload allocation methods are presented and analyzed, a brief overview of the application of deep learning in edge-cloud computing is also presented, and many challenges that need to be addressed for the AI application are discussed. Many issues and challenges are also presented for optimizing the edge.


G. Premsankar, M. Di Francesco, and T. Taleb, “Edge Computing for the Internet of Things: A Case Study,” IEEE Internet Things J., vol. 5, no. 2, pp. 1275–1284, 2018, doi: 10.1109/jiot.2018.2805263.

A. Mahmood, W. E. Zhang, and Q. Z. Sheng, “Software-defined heterogeneous vehicular networking: The architectural design and open challenges,” Futur. Internet, vol. 11, no. 3, 2019, doi: 10.3390/fi11030070.

B. Ji et al., “Survey on the Internet of Vehicles: Network Architectures and Applications,” IEEE Commun. Stand. Mag., vol. 4, no. 1, pp. 34–41, 2020, doi: 10.1109/MCOMSTD.001.1900053.

W. E. Zhang et al., “The 10 Research Topics in the Internet of Things,” Proc. - 2020 IEEE 6th Int. Conf. Collab. Internet Comput. CIC 2020, pp. 34–43, 2020, doi: 10.1109/CIC50333.2020.00015.

P. McEnroe ,S. Wang , and M. Liyanage "A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges", IEEE Internet of Things Journal, 9(17), pp. 15435–15459, 2022, doi:10.1109/jiot.2022.3176400.

J. Chen and X. Ran, “Deep Learning With Edge Computing: A Review,” Proc. IEEE, vol. 107, no. 8, 2019, doi: 10.1109/JPROC.2019.2921977.

W. Yu et al., “A Survey on the Edge Computing for the Internet of Things,” IEEE Access, vol. 6, pp. 6900–6919, 2017, doi: 10.1109/ACCESS.2017.2778504.

W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge Computing: Vision and Challenges,” IEEE Internet Things J., vol. 3, no. 5, pp. 637–646, 2016, doi: 10.1109/JIOT.2016.2579198.

A. Greenberg, J. Hamilton, D. A. Maltz, and P. Patel, “The cost of a cloud,” ACM SIGCOMM Comput. Commun. Rev., vol. 39, no. 1, pp. 68–73, 2008, doi: 10.1145/1496091.1496103.

E. Cuervoy et al., “MAUI: Making smartphones last longer with code offload,” MobiSys’10 - Proc. 8th Int. Conf. Mob. Syst. Appl. Serv., pp. 49–62, 2010, doi: 10.1145/1814433.1814441.

M. Satyanarayanan, P. Bahl, R. Caceres, and N. Davies, “The Case for VM-based Cloudlets in Mobile Computing,” IEEE pervasive Comput., vol. 1, pp. 1–9, 2006.

F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, “Fog computing and its role in the internet of things,” MCC’12 - Proc. 1st ACM Mob. Cloud Comput. Work., pp. 13–15, 2012, doi: 10.1145/2342509.2342513.

S. Yi, Z. Hao, Z. Qin, and Q. Li, “Fog computing: Platform and applications,” Proc. - 3rd Work. Hot Top. Web Syst. Technol. HotWeb 2015, pp. 73–78, 2016, doi: 10.1109/HotWeb.2015.22.

K. Ha, Z. Chen, W. Hu, W. Richter, P. Pillai, and M. Satyanarayanan, “Towards wearable cognitive assistance,” MobiSys 2014 - Proc. 12th Annu. Int. Conf. Mob. Syst. Appl. Serv., no. June 2014, pp. 68–81, 2014, doi: 10.1145/2594368.2594383.

B. G. Chun, S. Ihm, P. Maniatis, M. Naik, and A. Patti, “CloneCloud: Elastic execution between mobile device and cloud,” EuroSys’11 - Proc. EuroSys 2011 Conf., pp. 301–314, 2011, doi: 10.1145/1966445.1966473.

F. Liu, G. Tang, Y. Li, Z. Cai, X. Zhang, and T. Zhou, “A Survey on Edge Computing Systems and Tools,” Proc. IEEE, pp. 1–24, 2019, doi: 10.1109/JPROC.2019.2920341.

U. Drolia, K. Guo, J. Tan, R. Gandhi, and P. Narasimhan, “Cachier: Edge-Caching for Recognition Applications,” Proc. - Int. Conf. Distrib. Comput. Syst., pp. 276–286, 2017, doi: 10.1109/ICDCS.2017.94.

K. Bhardwaj, M. W. Shih, P. Agarwal, A. Gavrilovska, T. Kim, and K. Schwan, “Fast, scalable and secure onloading of edge functions using Airbox,” Proc. - 1st IEEE/ACM Symp. Edge Comput. SEC 2016, pp. 14–27, 2016, doi: 10.1109/SEC.2016.15.

S. H. Mortazavi, M. Salehe, C. S. Gomes, C. Phillips, and E. De Lara, “CloudPath: A multi-Tier cloud computing framework,” 2017 2nd ACM/IEEE Symp. Edge Comput. SEC 2017, 2017, doi: 10.1145/3132211.3134464.

M. Jang, K. Schwan, K. Bhardwaj, A. Gavrilovska, and A. Avasthi, “Personal clouds: Sharing and integrating networked resources to enhance end user experiences,” Proc. - IEEE INFOCOM, pp. 2220–2228, 2014, doi: 10.1109/INFOCOM.2014.6848165.

P. Liu, D. Willis, and S. Banerjee, “ParaDrop: Enabling lightweight multi-tenancy at the network’s extreme edge,” Proc. - 1st IEEE/ACM Symp. Edge Comput. SEC 2016, pp. 1–13, 2016, doi: 10.1109/SEC.2016.39.

S. Engineering, “Cloud Computing,” no. August, 2017. [Online]. Available:

H. P. Sajjad, K. Danniswara, A. Al-Shishtawy, and V. Vlassov, “SpanEdge: Towards unifying stream processing over central and near-the-edge data centers,” Proc. - 1st IEEE/ACM Symp. Edge Comput. SEC 2016, pp. 168–178, 2016, doi: 10.1109/SEC.2016.17.

Q. Zhang, X. Zhang, Q. Zhang, W. Shi, and H. Zhong, “Firework: Big data sharing and processing in collaborative edge environment,” Proc. - 4th IEEE Work. Hot Top. Web Syst. Technol. HotWeb 2016, pp. 20–25, 2016, doi: 10.1109/HotWeb.2016.12.

Z. W. Xu, “Cloud-sea computing systems: Towards thousand-fold improvement in performance per watt for the coming zettabyte era,” J. Comput. Sci. Technol., vol. 29, no. 2, pp. 177–181, 2014, doi: 10.1007/s11390-014-1420-2.

P. Zhou, W. Chen, S. Ji, H. Jiang, L. Yu, and D. Wu, “Privacy-preserving Online Task Allocation in Edge-Computing-Enabled Massive Crowdsensing,” IEEE Internet Things J., vol. 6, no. 5, pp. 7773–7787, 2019, doi: 10.1109/jiot.2019.2903515.

E. I. Gaura, J. Brusey, M. Allen, R. Wilkins, D. Goldsmith, and R. Rednic, “Edge mining the Internet of Things,” no. May 2014, 2013, doi: 10.1109/JSEN.2013.2266895.

Z. Xu, W. Liu, J. Huang, C. Yang, J. Lu, and H. Tan, “Artificial Intelligence for Securing IoT Services in Edge Computing : A Survey,” Secur. Commun. Networks, vol. 1, no. 3, 2020, doi: 10.1155/2020/8872586.

C. Savaglio and G. Fortino, “A Simulation-driven Methodology for IoT Data Mining Based on Edge Computing,” ACM Trans. Internet Technol., vol. 21, no. 2, pp. 1–22, 2021.

A. Abeshu and N. Chilamkurti, “Deep Learning: The Frontier for Distributed Attack Detection in Fog-To-Things Computing,” IEEE Commun. Mag., vol. 56, no. 2, pp. 169–175, 2018, doi: 10.1109/MCOM.2018.1700332.

H. Li, K. Ota, and M. Dong, “Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing,” IEEE Netw., vol. 32, no. 1, pp. 96–101, 2018, doi: 10.1109/MNET.2018.1700202.

J. Zhu, Q. Jiang, Y. Shen, C. Qian, F. Xu, and Q. Zhu, “Application of recurrent neural network to mechanical fault diagnosis: a review,” J. Mech. Sci. Technol., vol. 36, no. 2, pp. 527–542, 2022, doi: 10.1007/s12206-022-0102-1.

D. Ghimire, D. Kil, and S. Kim, “A Survey on Efficient Convolutional Neural Networks and Hardware Acceleration,” Electronics, vol. 11, no. 6, p. 945, 2022, doi: 10.3390/electronics11060945.

A. J. Moshayedi, A. S. Roy, A. Kolahdooz, and Y. Shuxin, “Deep Learning Application Pros And Cons Over Algorithm,” EAI Endorsed Trans. AI Robot., vol. 1, pp. 1–13, 2022, doi: 10.4108/airo.v1i.19.

Y. Matsuo et al., “Deep learning, reinforcement learning, and world models,” Neural Networks, vol. 152, pp. 267–275, 2022, doi: 10.1016/j.neunet.2022.03.037.

H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng, “Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations,” Proc. 26th Int. Confer- ence Mach. Learn., p. 8, 2009, doi: 10.1145/1553374.1553453.

H. Khelifi, S. Luo, B. Nour, A. Sellami, and H. Moungla, “Bringing Deep Learning at The Edge of Information-Centric Internet of Things,” IEEE Commun. Lett., vol. PP, no. XX, p. 1, 2018, doi: 10.1109/LCOMM.2018.2875978.

A. Murali, N. N. Das, S. S. Sukumaran, K. Chandrasekaran, C. Joseph, and J. P. Martin, “Machine Learning Approaches for Resource Allocation in the Cloud: Critical Reflection,” 2018 Int. Conf. Adv. Comput. Commun. Informatics, ICACCI 2018, pp. 2073–2079, 2018, doi: 10.1109/ICACCI.2018.8554703.

S. Jayaprakash, M. D. Nagarajan, R. P. de Prado, S. Subramanian, and P. B. Divakarachari, “A systematic review of energy management strategies for resource allocation in the cloud: Clustering, optimization and machine learning,” Energies, vol. 14, no. 17, 2021, doi: 10.3390/en14175322.

“AWS DeepLens – Deep learning enabled video camera for developers - AWS.” (accessed Mar. 07, 2023).

S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 6, pp. 1137–1149, 2020, doi: 10.4324/9780080519340-12.

T. Zhang, A. Chowdhery, P. Bahl, K. Jamieson, and S. Banerjee, “The design and implementation of a wireless video surveillance system,” Proc. Annu. Int. Conf. Mob. Comput. Networking, MOBICOM, vol. 2015-Septe, pp. 426–438, 2015, doi: 10.1145/2789168.2790123.

C. C. Hung et al., “VideoEdge: Processing camera streams using hierarchical clusters,” Proc. - 2018 3rd ACM/IEEE Symp. Edge Comput. SEC 2018, pp. 115–131, 2018, doi: 10.1109/SEC.2018.00016.

T. Young, D. Hazarika, S. Poria, and E. Cambria, “Recent trends in deep learning based natural language processing [Review Article],” IEEE Comput. Intell. Mag., vol. 13, no. 3, pp. 55–75, 2018, doi: 10.1109/MCI.2018.2840738.

“Deep Learning for Siri’s Voice: On-device Deep Mixture Density Networks for Hybrid Unit Selection Synthesis - Apple Machine Learning Research.” (accessed Mar. 09, 2023).

G. Lample, M. Ballesteros, S. Subramanian, K. Kawakami, and C. Dyer, “Neural architectures for named entity recognition,” 2016 Conf. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol. NAACL HLT 2016 - Proc. Conf., pp. 260–270, 2016, doi: 10.18653/v1/n16-1030.

“Google Duplex: An AI System for Accomplishing Real-World Tasks Over the Phone – Google AI Blog.” (accessed Mar. 09, 2023).

J. Rowsell, “Working with multimodality: Rethinking literacy in a digital age,” Work. with Multimodality Rethink. Lit. a Digit. Age, pp. 1–173, 2013, doi: 10.4324/9780203071953.

“Improve Server Response Time | PageSpeed Insights | Google Developers.” (accessed Mar. 09, 2023).

“Hey Siri: An On-device DNN-powered Voice Trigger for Apple’s Personal Assistant - Apple Machine Learning Research.” (accessed Mar. 09, 2023).

A. Kusupati, M. Singh, K. Bhatia, A. Kumar, P. Jain, and M. Varma, “FastGRNN: A fast, accurate, stable and tiny kilobyte sized gated recurrent neural network,” Adv. Neural Inf. Process. Syst., vol. 2018-Decem, no. NeurIPS, pp. 9017–9028, 2018.

A. De’ Faveri Tron, “Intrusion Detection with Neural Networks,” Optim. Mach. Learn., pp. 201–232, 2022, doi: 10.1002/9781119902881.ch8.

Y. Mirsky, T. Doitshman, Y. Elovici, and A. Shabtai, “Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection,” no. February, pp. 18–21, 2018, doi: 10.14722/ndss.2018.23204.

S. Chinchali et al., “Cellular network traffic scheduling with deep reinforcement learning,” 32nd AAAI Conf. Artif. Intell. AAAI 2018, pp. 766–774, 2018, doi: 10.1609/aaai.v32i1.11339.

N. Tsikoudis, A. Papadogiannakis, and E. P. Markatos, “LEoNIDS: A Low-Latency and Energy-Efficient Network-Level Intrusion Detection System,” IEEE Trans. Emerg. Top. Comput., vol. 4, no. 1, pp. 142–155, 2016, doi: 10.1109/TETC.2014.2369958.

H. Zhu, Y. Cao, W. Wang, T. Jiang, and S. Jin, “Deep Reinforcement Learning for Mobile Edge Caching: Review, New Features, and Open Issues,” IEEE Netw., vol. 32, no. 6, pp. 50–57, 2018, doi: 10.1109/MNET.2018.1800109.

Y. M. Saputra, D. T. Hoang, D. N. Nguyen, E. Dutkiewicz, D. Niyato, and D. I. Kim, “Distributed Deep Learning at the Edge: A Novel Proactive and Cooperative Caching Framework for Mobile Edge Networks,” IEEE Wirel. Commun. Lett., vol. 8, no. 4, pp. 1220–1223, 2019, doi: 10.1109/LWC.2019.2912365.

S. Yao, S. Hu, Y. Zhao, A. Zhang, and T. Abdelzaher, “DeepSense: A unified deep learning framework for time-series mobile sensing data processing,” 26th Int. World Wide Web Conf. WWW 2017, pp. 351–360, 2017, doi: 10.1145/3038912.3052577.

W. Ouyang and X. Wang, “Joint deep learning for pedestrian detection,” Proc. IEEE Int. Conf. Comput. Vis., pp. 2056–2063, 2013, doi: 10.1109/ICCV.2013.257.

L. Li, K. Ota, and M. Dong, “When Weather Matters: IoT-Based Electrical Load Forecasting for Smart Grid,” IEEE Commun. Mag., vol. 55, no. 10, pp. 46–51, 2017, doi: 10.1109/MCOM.2017.1700168.

Z. Zhao, K. M. Barijough, and A. Gerstlauer, “DeepThings: Distributed adaptive deep learning inference on resource-constrained IoT edge clusters,” IEEE Trans. Comput. Des. Integr. Circuits Syst., vol. 37, no. 11, pp. 2348–2359, 2018, doi: 10.1109/TCAD.2018.2858384.

J. Jiang, G. Ananthanarayanan, P. Bodik, S. Sen, and I. Stoica, “Chameleon : Scalable Adaptation of Video Analytics,” pp. 253–266, 2018, doi: 10.1145/3230543.3230574.

M. Mohammadi, A. Al-Fuqaha, S. Sorour, and M. Guizani, “Deep learning for IoT big data and streaming analytics: A survey,” IEEE Commun. Surv. Tutorials, vol. 20, no. 4, pp. 2923–2960, 2018, doi: 10.1109/COMST.2018.2844341.

“Hudson Yards Data | Sidewalk Labs | Data Privacy.” (accessed Mar. 09, 2023).

X. Hou, S. Dey, J. Zhang, and M. Budagavi, “Predictive view generation to enable mobile 360-degree and VR experiences,” VR/AR Netw. 2018 - Proc. 2018 Morning Work. Virtual Real. Augment. Real. Network, Part SIGCOMM 2018, pp. 20–26, 2018, doi: 10.1145/3229625.3229629.

S. Afzal, J. Chen, and K. K. Ramakrishnan, “Characterization of 360-degree videos,” VR/AR Netw. 2017 - Proc. 2017 Work. Virtual Real. Augment. Real. Network, Part SIGCOMM 2017, pp. 1–6, 2017, doi: 10.1145/3097895.3097896.

L. Liu, H. Li, and M. Gruteser, “Edge assisted real-time object detection for mobile augmented reality,” 25th Annu. Int. Conf. Mob. Comput. Netw., 2019, doi: 10.1145/3300061.3300116.

“Enabling full body AR with Mask R-CNN2Go - Meta Research | Meta Research.” (accessed Mar. 09, 2023).

C. Machover and S. E. Tice, “Virtual Reality - Virtual Reality,” IEEE Comput. Graph. Appl., vol. 1, no. January, pp. 15–16, 2014.

Z. Chen et al., “An empirical study of latency in an emerging class of edge computing applications for wearable cognitive assistance,” 2017 2nd ACM/IEEE Symp. Edge Comput. SEC 2017, 2017, doi: 10.1145/3132211.3134458.

K. Ha, Z. Chen, W. Hu, W. Richter, P. Pillai, and M. Satyanarayanan, “Towards wearable cognitive assistance,” Proc. 12th Annu. Int. Conf. Mob. Syst. Appl. Serv. - MobiSys ’14, no. December, pp. 68–81, 2014, [Online]. Available:

A. G. Howard et al., “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” 2017, [Online]. Available:

W. Liu et al., “SSD: Single shot multibox detector,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9905 LNCS, pp. 21–37, 2016, doi: 10.1007/978-3-319-46448-0_2.

J. Redmon and A. Farhadi, “Yolo V2.0,” Cvpr2017, no. April, pp. 187–213, 2016, [Online]. Available:

F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer, “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size,” pp. 1–13, 2016, [Online]. Available:

“TensorFlow.” (accessed Mar. 09, 2023).

“Caffe2 and PyTorch join forces to create a Research + Production platform PyTorch 1.0 | Caffe2.” (accessed Mar. 09, 2023).

S. Han, H. Mao, and W. J. Dally, “Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding,” 4th Int. Conf. Learn. Represent. ICLR 2016 - Conf. Track Proc., pp. 1–14, 2016.

S. Yao, Y. Zhao, A. Zhang, L. Su, and T. Abdelzaher, “DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems with a Compressor-Critic Framework,” SenSys 2017 - Proc. 15th ACM Conf. Embed. Networked Sens. Syst., vol. 2017-Janua, 2017, doi: 10.1145/3131672.3131675.

L. Lai and N. Suda, “Enabling deep learning at the IoT edge,” IEEE/ACM Int. Conf. Comput. Des. Dig. Tech. Pap. ICCAD, 2018, doi: 10.1145/3240765.3243473.

S. Han et al., “ESE: Efficient speech recognition engine with sparse LSTM on FPGA,” FPGA 2017 - Proc. 2017 ACM/SIGDA Int. Symp. Field-Programmable Gate Arrays, pp. 75–84, 2017, doi: 10.1145/3020078.3021745.

S. Bhattacharya and N. D. Lane, “Sparsification and separation of deep learning layers for constrained resource inference on wearables,” Proc. 14th ACM Conf. Embed. Networked Sens. Syst. SenSys 2016, pp. 176–189, 2016, doi: 10.1145/2994551.2994564.

G. Hinton, O. Vinyals, and J. Dean, “Distilling the Knowledge in a Neural Network,” pp. 1–9, 2015, [Online]. Available:

S. Teerapittayanon, B. McDanel, and H. T. Kung, “BranchyNet: Fast inference via early exiting from deep neural networks,” Proc. - Int. Conf. Pattern Recognit., vol. 0, pp. 2464–2469, 2016, doi: 10.1109/ICPR.2016.7900006.

S. Liu, K. Nan, Y. Lin, H. Liu, Z. Zhou, and J. Du, “On-demand deep model compression for mobile devices: A usage-driven model selection framework,” MobiSys 2018 - Proc. 16th ACM Int. Conf. Mob. Syst. Appl. Serv., pp. 389–400, 2018, doi: 10.1145/3210240.3210337.

L. N. Huynh, Y. Lee, and R. K. Balan, “DeepMon: Mobile GPU-based deep learning framework for continuous vision applications,” MobiSys 2017 - Proc. 15th Annu. Int. Conf. Mob. Syst. Appl. Serv., pp. 82–95, 2017, doi: 10.1145/3081333.3081360.

“Edge TPU - Run Inference at the Edge | Google Cloud.” (accessed Mar. 09, 2023).

Z. Du et al., “ShiDianNao: Shifting vision processing closer to the sensor,” Proc. - Int. Symp. Comput. Archit., vol. 13-17-June, pp. 92–104, 2015, doi: 10.1145/2749469.2750389.

Y. Chen, T. Chen, Z. Xu, N. Sun, and O. Temam, “DianNao Family: Energy-Efficient Hardware Accelerators for Machine Learning,” Commun. ACM, vol. 59, no. 11, pp. 105–112, 2016, doi: 10.1145/2996864.

S. Rivas-Gomez, A. J. Pena, D. Moloney, E. Laure, and S. Markidis, “Exploring the vision processing unit as co-processor for inference,” Proc. - 2018 IEEE 32nd Int. Parallel Distrib. Process. Symp. Work. IPDPSW 2018, pp. 589–598, 2018, doi: 10.1109/IPDPSW.2018.00098.

“Intel® MovidiusTM Vision Processing Units (VPUs).” (accessed Mar. 09, 2023).

“NVIDIA EGX: Accelerating Edge Computing for AI at the Edge | NVIDIA.” (accessed Mar. 09, 2023).

“Qualcomm Neural Processing SDK for AI - Qualcomm Developer Network.” (accessed Mar. 09, 2023).

M. Alzantot, Y. Wang, Z. Ren, and M. B. Srivastava, “RSTensorFlow: GPU enabled TensorFlow for deep learning on commodity android devices,” EMDL 2017 - Proc. 1st Int. Work. Deep Learn. Mob. Syst. Appl. co-located with MobiSys 2017, pp. 7–12, 2017, doi: 10.1145/3089801.3089805.

N. D. Lane et al., “DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices,” 2016 15th ACM/IEEE Int. Conf. Inf. Process. Sens. Networks, IPSN 2016 - Proc., no. 1, 2016, doi: 10.1109/IPSN.2016.7460664.

V. Sze, Y. H. Chen, T. J. Yang, and J. S. Emer, “Efficient Processing of Deep Neural Networks: A Tutorial and Survey,” Proc. IEEE, vol. 105, no. 12, pp. 2295–2329, 2017, doi: 10.1109/JPROC.2017.2761740.

Q. Wang, G. Jin, Q. Li, K. Wang, Z. Yang, and H. Wang, “Industrial Edge Computing: Vision and Challenges,” Inf. Control, vol. 50, no. 3, pp. 257–274, 2021, doi: 10.13976/j.cnki.xk.2021.1030.

X. Wang, X. Wang, and S. Mao, “RF Sensing in the Internet of Things: A General Deep Learning Framework,” IEEE Commun. Mag., vol. 56, no. 9, pp. 62–67, 2018, doi: 10.1109/MCOM.2018.1701277.

T. Y. Chen, “Glimpse : Continuous , Real-Time Object Recognition on Mobile Devices Categories and Subject Descriptors,” SenSys ’15 Proc. 13th ACM Conf. Embed. Networked Sens. Syst., vol. 15, pp. 155–168, 2015.

C. Liu et al., “A New Deep Learning-Based Food Recognition System for Dietary Assessment on An Edge Computing Service Infrastructure,” IEEE Trans. Serv. Comput., vol. 11, no. 2, pp. 249–261, 2018, doi: 10.1109/TSC.2017.2662008.

H. Zhang, G. Ananthanarayanan, P. Bodik, M. Philipose, P. Bahl, and M. J. Freedman, “Live video analytics at scale with approximation and delay-tolerance,” Proc. 14th USENIX Symp. Networked Syst. Des. Implementation, NSDI 2017, pp. 377–392, 2017.

J. Jiang, G. Ananthanarayanan, P. Bodik, S. Sen, and I. Stoica, “Chameleon: Scalable adaptation of video analytics,” SIGCOMM 2018 - Proc. 2018 Conf. ACM Spec. Interes. Gr. Data Commun., pp. 253–266, 2018, doi: 10.1145/3230543.3230574.

A. H. Jiang et al., “Mainstream : Dynamic Stem-Sharing for Multi-Tenant Video Processing,” 2018 USENIX Annu. Tech. Conf. (USENIX ATC ’18), 2018.

X. Ran, H. Chen, X. Zhu, Z. Liu, and J. Chen, “DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics,” Proc. - IEEE INFOCOM, vol. 2018-April, pp. 1421–1429, 2018, doi: 10.1109/INFOCOM.2018.8485905.

X. Ran, H. Chen, Z. Liu, and J. Chen, “Delivering deep learning to mobile devices via offloading,” VR/AR Netw. 2017 - Proc. 2017 Work. Virtual Real. Augment. Real. Network, Part SIGCOMM 2017, pp. 42–47, 2017, doi: 10.1145/3097895.3097903.

S. Han, H. Shen, M. Philipose, S. Agarwal, A. Wolman, and A. Krishnamurthy, “MCDNN: An approximation-based execution framework for deep stream processing under resource constraints,” MobiSys 2016 - Proc. 14th Annu. Int. Conf. Mob. Syst. Appl. Serv., pp. 123–136, 2016, doi: 10.1145/2906388.2906396.

S. Yi, Z. Hao, Q. Zhang, Q. Zhang, W. Shi, and Q. Li, “LAVEA: Latency-Aware video analytics on edge computing platform,” 2017 2nd ACM/IEEE Symp. Edge Comput. SEC 2017, 2017, doi: 10.1145/3132211.3134459.

Y. Kang et al., “Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge,” ACM SIGARCH Comput. Archit. News, vol. 45, no. 1, pp. 615–629, 2017, doi: 10.1145/3093337.3037698.

J. Mao, X. Chen, K. W. Nixon, C. Krieger, and Y. Chen, “MoDNN: Local distributed mobile computing system for Deep Neural Network,” Proc. 2017 Des. Autom. Test Eur. DATE 2017, pp. 1396–1401, 2017, doi: 10.23919/DATE.2017.7927211.

M. R. Ra, A. Sheth, L. Mummert, P. Pillai, D. Wetherall, and R. Govindan, “Odessa: Enabling interactive perception applications on mobile devices,” MobiSys’11 - Compil. Proc. 9th Int. Conf. Mob. Syst. Appl. Serv. Co-located Work., pp. 43–56, 2011, doi: 10.1145/1999995.2000000.

S. Teerapittayanon, B. McDanel, and H. T. Kung, “Distributed Deep Neural Networks over the Cloud, the Edge and End Devices,” Proc. - Int. Conf. Distrib. Comput. Syst., pp. 328–339, 2017, doi: 10.1109/ICDCS.2017.226.

U. Drolia, K. Guo, and P. Narasimhan, “Precog: Prefetching for image recognition applications at the edge,” 2017 2nd ACM/IEEE Symp. Edge Comput. SEC 2017, 2017, doi: 10.1145/3132211.3134456.

L. Huang, X. Feng, A. Feng, Y. Huang, and L. P. Qian, “Distributed Deep Learning-based Offloading for Mobile Edge Computing Networks,” Mob. Networks Appl., vol. 27, no. 3, pp. 1123–1130, 2022, doi: 10.1007/s11036-018-1177-x.

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, vol. 7, pp. 149623–149633, 2019, doi: 10.1109/ACCESS.2019.2947053.

Y. Miao, G. Wu, M. Li, A. Ghoneim, M. Al-Rakhami, and M. S. Hossain, "Intelligent task prediction and computation offloading based on mobile-edge cloud computing", Future Generation Computer Systems, 102, pp. 925–931., 2020, doi:10.1016/j.future.2019.09.035.

M. Guo, L. Li, and Q. Guan, “Energy-efficient and delay-guaranteed workload allocation in iot-edge-cloud computing systems,” IEEE Access, vol. 7, pp. 78685–78697, 2019, doi: 10.1109/ACCESS.2019.2922992.

G. Wang, X. Yang, W. Cai, and Y. Zhang, “Event-triggered online energy flow control strategy for regional integrated energy system using Lyapunov optimization,” Int. J. Electr. Power Energy Syst., vol. 125, no. August 2020, p. 106451, 2021, doi: 10.1016/j.ijepes.2020.106451.

L. Chen, S. Zhou, and J. Xu, “Computation Peer Offloading for Energy-Constrained Mobile Edge Computing in Small-Cell Networks,” IEEE/ACM Trans. Netw., vol. 26, no. 4, pp. 1619–1932, 2018, doi: 10.1109/TNET.2018.2841758.

C. F. Liu, M. Bennis, M. Debbah, and H. Vincent Poor, “Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing,” IEEE Trans. Commun., vol. 67, no. 6, pp. 4132–4150, 2019, doi: 10.1109/TCOMM.2019.2898573.

Y. H. Chiang, T. Zhang, and Y. Ji, “Joint Cotask-Aware Offloading and Scheduling in Mobile Edge Computing Systems,” IEEE Access, vol. 7, pp. 105008–105018, 2019, doi: 10.1109/ACCESS.2019.2931336.

M. Chen and Y. Hao, “Task Offloading for Mobile Edge Computing in Software Defined Ultra-Dense Network,” IEEE J. Sel. Areas Commun., vol. 36, no. 3, pp. 587–597, 2018, doi: 10.1109/JSAC.2018.2815360.

Z. Ning, P. Dong, X. Kong, and F. Xia, “A cooperative partial computation offloading scheme for mobile edge computing enabled internet of things,” IEEE Internet Things J., vol. 6, no. 3, pp. 4804–4814, 2019, doi: 10.1109/JIOT.2018.2868616.

J. Du, L. Zhao, J. Feng, and X. Chu, “Computation Offloading and Resource Allocation in Mixed Fog/Cloud Computing Systems with Min-Max Fairness Guarantee,” IEEE Trans. Commun., vol. 66, no. 4, pp. 1594–1608, 2018, doi: 10.1109/TCOMM.2017.2787700.

Y. Wang, X. Tao, X. Zhang, P. Zhang, and Y. T. Hou, “Cooperative Task Offloading in Three-Tier Mobile Computing Networks: An ADMM Framework,” IEEE Trans. Veh. Technol., vol. 68, no. 3, pp. 2763–2776, 2019, doi: 10.1109/TVT.2019.2892176.

Z. Zheng, L. Song, Z. Han, G. Y. Li, and H. V. Poor, “A stackelberg game approach to proactive caching in large-scale mobile edge networks,” IEEE Trans. Wirel. Commun., vol. 17, no. 8, pp. 5198–5211, 2018, doi: 10.1109/TWC.2018.2839111.

T. Hao, J. Zhan, K. Hwang, W. Gao, and X. Wen, “AI-oriented workload allocation for cloud-edge computing,” Proc. - 21st IEEE/ACM Int. Symp. Clust. Cloud Internet Comput. CCGrid 2021, pp. 555–564, 2021, doi: 10.1109/CCGrid51090.2021.00065.

T. Hao, J. Zhan, K. Hwang, W. Gao, and X. Wen, “AI-oriented Medical Workload Allocation for Hierarchical Cloud/Edge/Device Computing,” 2020, [Online]. Available:

T. Hao et al., “Edge AIBench: Towards Comprehensive End-to-End Edge Computing Benchmarking,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11459 LNCS, pp. 23–30, 2019, doi: 10.1007/978-3-030-32813-9_3.

Y. W. Hung, Y. C. Chen, C. Lo, A. G. So, and S. C. Chang, “Dynamic Workload Allocation for Edge Computing,” IEEE Trans. Very Large Scale Integr. Syst., vol. 29, no. 3, pp. 519–529, 2021, doi: 10.1109/TVLSI.2021.3049520.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-Decem, pp. 770–778, 2016, doi: 10.1109/CVPR.2016.90.

S. Zagoruyko and N. Komodakis, “Wide Residual Networks,” Br. Mach. Vis. Conf. 2016, BMVC 2016, vol. 2016-Septe, pp. 87.1-87.12, 2016, doi: 10.5244/C.30.87.

A. Krizhevsky, “Learning Multiple Layers of Features from Tiny Images,” in Asha, 2009.

P. Panda, A. Sengupta, and K. Roy, “Energy-efficient and improved image recognition with conditional deep learning,” ACM J. Emerg. Technol. Comput. Syst., vol. 13, no. 3, pp. 1–21, 2017, doi: 10.1145/3007192.

C. Sonmez, C. Tunca, A. Ozgovde, and C. Ersoy, “Machine Learning-Based Workload Orchestrator for Vehicular Edge Computing,” IEEE Trans. Intell. Transp. Syst., vol. 22, no. 4, pp. 2239–2251, 2020, doi: 10.1109/tits.2020.3024233.

J. C. S. Dos Anjos, J. L. G. Gross, K. J. Matteussi, G. V. González, V. R. Q. Leithardt, and C. F. R. Geyer, “An algorithm to minimize energy consumption and elapsed time for iot workloads in a hybrid architecture,” Sensors, vol. 21, no. 9, pp. 1–20, 2021, doi: 10.3390/s21092914.

J. Long, Y. Luo, X. Zhu, E. Luo, and M. Huang, “Computation offloading through mobile vehicles in IoT-edge-cloud network,” Eurasip J. Wirel. Commun. Netw., vol. 2020, no. 1, 2020, doi: 10.1186/s13638-020-01848-5.

S. Yang, G. Lee, and L. Huang, “Deep Learning-Based Dynamic Computation Task Offloading for Mobile Edge Computing Networks,” Sensors, vol. 22, no. 11, pp. 1–18, 2022, doi: 10.3390/s22114088.

M. Gali and A. Mahamkali, “A Distributed Deep Meta Learning based Task Offloading Framework for Smart City Internet of Things with Edge-Cloud Computing,” vol. 4, pp. 224–237, 2022, doi: 10.58346/JISIS.2022.I4.016.

I. Ullah, H.-K. Lim, Y. J. Seok, and Y.-H. Han, “Optimizing Task Offloading and Resource Allocation in Edge-Cloud Networks : A DRL Approach,” pp. 1–28, 2023, doi: 10.21203/

A. Heidari, N. Jafari, M. Ali, and J. Jamali, “A green , secure , and deep intelligent method for dynamic IoT-edge-cloud offloading scenarios,” Sustain. Comput. Informatics Syst., vol. 38, no. February, p. 100859, 2023, doi: 10.1016/j.suscom.2023.100859.

J. Chen and X. Ran, “Deep Learning With Edge Computing: A Review,” Proc. IEEE, pp. 1–19, 2019, doi: 10.1109/JPROC.2019.2921977.

W. Wang, M. Zhang, G. Chen, H. V. Jagadish, B. C. Ooi, and K. L. Tan, “Database meets deep learning: Challenges and opportunities,” SIGMOD Rec., vol. 45, no. 2, pp. 17–22, 2016, doi: 10.1145/3003665.3003669.

I. Guyon, G. Dror, V. Lemaire, G. Taylor, and D. W. Aha, “Unsupervised and transfer learning challenge,” Proc. Int. Jt. Conf. Neural Networks, pp. 793–800, 2011, doi: 10.1109/IJCNN.2011.6033302.

X. Chen, S. Member, and X. Lin, “Big Data Deep Learning : Challenges and Perspectives,” IEEE Access, vol. 2, pp. 514–525, 2014, doi: 10.1109/ACCESS.2014.2325029.

A. Mohammad and M. Pradhan, “Machine learning with big data analytics for cloud security,” Comput. & Electr. Eng., vol. 96, p. 107527, 2021, doi: 10.1016/j.compeleceng.2021.107527.

U. A. Butt et al., “A review of machine learning algorithms for cloud computing security,” Electron., vol. 9, no. 9, pp. 1–25, 2020, doi: 10.3390/electronics9091379.

H. Wu, X. Li, and Y. Deng, “Deep learning-driven wireless communication for edge-cloud computing: opportunities and challenges,” J. Cloud Comput., vol. 9, no. 1, 2020, doi: 10.1186/s13677-020-00168-9.

T. K. Rodrigues, K. Suto, H. Nishiyama, J. Liu, and N. Kato, “Machine Learning Meets Computation and Communication Control in Evolving Edge and Cloud: Challenges and Future Perspective,” IEEE Commun. Surv. Tutorials, vol. 22, no. 1, pp. 38–67, 2020, doi: 10.1109/COMST.2019.2943405.

H.-A. Ounifi, A. Gherbi, and N. Kara, “Deep machine learning-based power usage effectiveness prediction for sustainable cloud infrastructures,” Sustain. Energy Technol. Assessments, vol. 52, p. 101967, 2022, doi: 10.1016/j.seta.2022.101967.




How to Cite

Ammar Rafea , S., & Dawood Jasim , A. (2023). AI Workload Allocation Methods for Edge-Cloud Computing: A Review. Al-Iraqia Journal for Scientific Engineering Research, 2(4), 115–132.