A comprehensive review of task offloading in edge computing
Resumo
Nowadays, mobile applications are demanding compute-intensive use, in addition to the need for lower latency and lower computational costs. Thus, researchers are proposing to bring the computation of these applications closer to the users by offloading these applications to the Edge. In this work, we carried out a comprehensive literature review with the primary objective of investigating the offloading strategies used in the Edge Computing scenario, which restrictions are considered, and the security aspects considered by the strategies. From the selected works, we describe the main optimization objectives of the strategies, which models and algorithms were implemented, which computational constraints were considered, which types of applications, and the security requirements. Finally, we discussed some opportunities and open challenges.
Palavras-chave:
Edge computing, offloading strategies, task offloading
Referências
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T. Zheng, J. Wan, J. Zhang, C. Jiang, and G. Jia, “A survey of computation offloading in edge computing,” in 2020 International Conference on Computer, Information and Telecommunication Systems (CITS), 2020, pp. 1–6.
A. Bhattcharya and P. De, “Computation offloading from mobile devices: Can edge devices perform better than the cloud?” in Proceedings of the Third International Workshop on Adaptive Resource Management and Scheduling for Cloud Computing, ser. ARMS-CC’16. New York, NY, USA: Association for Computing Machinery, 2016, p. 1–6. [Online]. Available: https://doi.org/10.1145/2962564.2962569
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B. Nour, S. Mastorakis, and A. Mtibaa, “Whispering: Joint service offloading and computation reuse in cloud-edge networks,” in ICC 2021 - IEEE International Conference on Communications, 2021, pp. 1–6.
R. Dautov and S. Distefano, “Stream processing on clustered edge devices,” IEEE Transactions on Cloud Computing, pp. 1–1, 2020.
B. Cao, L. Zhang, Y. Li, D. Feng, and W. Cao, “Intelligent offloading in multi-access edge computing: A state-of-the-art review and framework,” IEEE Communications Magazine, vol. 57, no. 3, pp. 56–62, 2019.
A. B. De Souza, P. A. L. Rego, T. Carneiro, J. D. C. Rodrigues, P. P. R. Filho, J. N. De Souza, V. Chamola, V. H. C. De Albuquerque, and B. Sikdar, “Computation offloading for vehicular environments: A survey,” IEEE Access, vol. 8, pp. 198 214–198 243, 2020.
A. K. Jha, M. Patel, and T. Pawar, “Fog offloading: Review, research opportunity and challenges,” in 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT), 2019, pp. 1224–1227.
S. Talal, W. S. M. Yousef, and B. Al-Fuhaidi, “Computation offloading algorithms in vehicular edge computing environment: A survey,” in 2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE), 2021, pp. 1–6.
X. Shan, H. Zhi, P. Li, and Z. Han, “A survey on computation offloading for mobile edge computing information,” in 2018 IEEE 4th International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing, (HPSC) and IEEE International Conference on Intelligent Data and Security (IDS), 2018, pp. 248–251.
M. Zamzam, T. El-Shabrawy, and M. Ashour, “Game theory for computation offloading and resource allocation in edge computing: A survey,” in 2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES), 2020, pp. 47–53.
B. Wang, C. Wang, W. Huang, Y. Song, and X. Qin, “A survey and taxonomy on task offloading for edge-cloud computing,” IEEE Access, vol. 8, pp. 186 080–186 101, 2020.
C. Jiang, X. Cheng, H. Gao, X. Zhou, and J. Wan, “Toward computation offloading in edge computing: A survey,” IEEE Access, vol. 7, pp. 131 543–131 558, 2019.
K. Petersen, R. Feldt, S. Mujtaba, and M. Mattsson, “Systematic mapping studies in software engineering,” ser. EASE’08. Swindon, GBR: BCS Learning amp; Development Ltd., 2008, p. 68–77.
C. Okoli, “A guide to conducting a standalone systematic literature review,” Communications of the Association for Information Systems, vol. 37, 11 2015.
X. Huang, Y. Yang, and X. Wu, “A meta-heuristic computation offloading strategy for iot applications in an edge-cloud framework,” in Proceedings of the 2019 3rd International Symposium on Computer Science and Intelligent Control. New York, NY, USA: Association for Computing Machinery, 2019. [Online]. Available: https://doi.org/10.1145/3386164.3390513
Y. Huang, B. Lin, Y. Zheng, J. Hu, Y. Mo, and X. Chen, “Cost efficient offloading strategy for dnn-based applications in edgecloud environment,” in 2019 IEEE Intl Conf on Parallel Distributed Processing with Applications, Big Data Cloud Computing, Sustainable Computing Communications, Social Computing Networking (ISPA/BDCloud/SocialCom/SustainCom), 2019, pp. 331–337.
T. Wang, Y. Liang, Y. Zhang, X. Zheng, M. Arif, J. Wang, and Q. Jin, “An intelligent dynamic offloading from cloud to edge for smart iot systems with big data,” IEEE Transactions on Network Science and Engineering, vol. 7, no. 4, pp. 2598–2607, 2020.
Y. Hao, Y. Jiang, T. Chen, D. Cao, and M. Chen, “itaskoffloading: Intelligent task offloading for a cloud-edge collaborative system,” IEEE Network, vol. 33, no. 5, pp. 82–88, 2019.
Y. Zhang, B. Di, Z. Zheng, J. Lin, and L. Song, “Joint data offloading and resource allocation for multi-cloud heterogeneous mobile edge computing using multi-agent reinforcement learning,” in 2019 IEEE Global Communications Conference (GLOBECOM), 2019, pp. 1–6.
R. Dautov, S. Distefano, D. Bruneo, F. Longo, G. Merlino, and A. Puliafito, “Data processing in cyber-physical-social systems through edge computing,” IEEE Access, vol. 6, pp. 29 822–29 835, 2018.
X. Yang, Z. Fei, J. Zheng, N. Zhang, and A. Anpalagan, “Joint multi-user computation offloading and data caching for hybrid mobile cloud/edge computing,” IEEE Transactions on Vehicular Technology, vol. 68, no. 11, pp. 11 018–11 030, 2019.
F. R. de Souza, M. Dias de Assunc¸ao, E. Caron, and A. da Silva Veith, “An optimal model for optimizing the placement and parallelism of data stream processing applications on cloud-edge computing,” in 2020 IEEE 32nd International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), 2020, pp. 59–66.
A. Qin, C. Cai, Q. Wang, Y. Ni, and H. Zhu, “Game theoretical multiuser computation offloading for mobile-edge cloud computing,” in 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), 2019, pp. 328–332.
L. Yang, Z. Dai, and K. Li, “An offloading strategy based on cloud and edge computing for industrial internet,” in 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), 2019, pp. 1666–1673.
Z. Wang, T. Wu, Z. Zhang, and H. Zhou, “A game theory-based computation offloading method in cloud-edge computing networks,” in 2021 International Conference on Computer Communications and Networks (ICCCN), 2021, pp. 1–6.
V. De Maio and I. Brandic, “First hop mobile offloading of dag computations,” in 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), 2018, pp. 83–92.
Y. Wu, G. Lu, N. Jin, L. Fu and J. Zhuan Zhao, "Trusted Fog Computing for Privacy Smart Contract Blockchain," 2021 IEEE 6th International Conference on Signal and Image Processing (ICSIP), 2021, pp. 1042-1047
W. Li, M. He, W. Zhu, and J. Zheng, “A study on lightweight and secure edge computing based blockchain,” in 2021 IEEE 12th International Conference on Software Engineering and Service Science (ICSESS), 2021, pp. 256–261.
K.-L. Wright, M. Martinez, U. Chadha, and B. Krishnamachari, “Smartedge: A smart contract for edge computing,” in 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2018, pp. 1685–1690.
S. Xu, C. Guo, R. Q. Hu, and Y. Qian, “Blockchain inspired secure computation offloading in a vehicular cloud network,” IEEE Internet of Things Journal, pp. 1–1, 2021.
S. Battula, S. Garg, R. Naha, M. Amin, B. Kang, and E. Aghasian, “A blockchain-based framework for automatic sla management in fog computing environments,” The Journal of Supercomputing, 05 2022.
X. Huang, X. Liu, Q. Chen, and J. Zhang, “Resource allocation and task offloading in blockchain-enabled fog computing networks,” in 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall), 2021, pp. 01–05.
J. Zhang, Y. Huang, F. Ye, and Y. Yang, “A novel proof-of-reputation consensus for storage allocation in edge blockchain systems,” in 2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS), 2021, pp. 1–10.
S. Iqbal, A. W. Malik, A. U. Rahman, and R. M. Noor, “Blockchainbased reputation management for task offloading in micro-level vehicular fog network,” IEEE Access, vol. 8, pp. 52 968–52 980, 2020.
Y. Du, Z. Wang, J. Li, L. Shi, D. N. K. Jayakody, Q. Chen, W. Chen, and Z. Han, “Blockchain-aided edge computing market: Smart contract and consensus mechanisms,” IEEE Transactions on Mobile Computing, pp. 1–1, 2022.
J. Wu, Z. Cao, Y. Zhang, and X. Zhang, “Edge-cloud collaborative computation offloading model based on improved partical swarm optimization in mec,” in 2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS), 2019, pp. 959–962.
T. Zheng, J. Wan, J. Zhang, C. Jiang, and G. Jia, “A survey of computation offloading in edge computing,” in 2020 International Conference on Computer, Information and Telecommunication Systems (CITS), 2020, pp. 1–6.
A. Bhattcharya and P. De, “Computation offloading from mobile devices: Can edge devices perform better than the cloud?” in Proceedings of the Third International Workshop on Adaptive Resource Management and Scheduling for Cloud Computing, ser. ARMS-CC’16. New York, NY, USA: Association for Computing Machinery, 2016, p. 1–6. [Online]. Available: https://doi.org/10.1145/2962564.2962569
A. Alelaiwi, “An efficient method of computation offloading in an edge cloud platform,” Journal of Parallel and Distributed Computing, vol. 127, 05 2019.
B. Nour, S. Mastorakis, and A. Mtibaa, “Whispering: Joint service offloading and computation reuse in cloud-edge networks,” in ICC 2021 - IEEE International Conference on Communications, 2021, pp. 1–6.
R. Dautov and S. Distefano, “Stream processing on clustered edge devices,” IEEE Transactions on Cloud Computing, pp. 1–1, 2020.
B. Cao, L. Zhang, Y. Li, D. Feng, and W. Cao, “Intelligent offloading in multi-access edge computing: A state-of-the-art review and framework,” IEEE Communications Magazine, vol. 57, no. 3, pp. 56–62, 2019.
A. B. De Souza, P. A. L. Rego, T. Carneiro, J. D. C. Rodrigues, P. P. R. Filho, J. N. De Souza, V. Chamola, V. H. C. De Albuquerque, and B. Sikdar, “Computation offloading for vehicular environments: A survey,” IEEE Access, vol. 8, pp. 198 214–198 243, 2020.
A. K. Jha, M. Patel, and T. Pawar, “Fog offloading: Review, research opportunity and challenges,” in 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT), 2019, pp. 1224–1227.
S. Talal, W. S. M. Yousef, and B. Al-Fuhaidi, “Computation offloading algorithms in vehicular edge computing environment: A survey,” in 2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE), 2021, pp. 1–6.
X. Shan, H. Zhi, P. Li, and Z. Han, “A survey on computation offloading for mobile edge computing information,” in 2018 IEEE 4th International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing, (HPSC) and IEEE International Conference on Intelligent Data and Security (IDS), 2018, pp. 248–251.
M. Zamzam, T. El-Shabrawy, and M. Ashour, “Game theory for computation offloading and resource allocation in edge computing: A survey,” in 2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES), 2020, pp. 47–53.
B. Wang, C. Wang, W. Huang, Y. Song, and X. Qin, “A survey and taxonomy on task offloading for edge-cloud computing,” IEEE Access, vol. 8, pp. 186 080–186 101, 2020.
C. Jiang, X. Cheng, H. Gao, X. Zhou, and J. Wan, “Toward computation offloading in edge computing: A survey,” IEEE Access, vol. 7, pp. 131 543–131 558, 2019.
K. Petersen, R. Feldt, S. Mujtaba, and M. Mattsson, “Systematic mapping studies in software engineering,” ser. EASE’08. Swindon, GBR: BCS Learning amp; Development Ltd., 2008, p. 68–77.
C. Okoli, “A guide to conducting a standalone systematic literature review,” Communications of the Association for Information Systems, vol. 37, 11 2015.
X. Huang, Y. Yang, and X. Wu, “A meta-heuristic computation offloading strategy for iot applications in an edge-cloud framework,” in Proceedings of the 2019 3rd International Symposium on Computer Science and Intelligent Control. New York, NY, USA: Association for Computing Machinery, 2019. [Online]. Available: https://doi.org/10.1145/3386164.3390513
Y. Huang, B. Lin, Y. Zheng, J. Hu, Y. Mo, and X. Chen, “Cost efficient offloading strategy for dnn-based applications in edgecloud environment,” in 2019 IEEE Intl Conf on Parallel Distributed Processing with Applications, Big Data Cloud Computing, Sustainable Computing Communications, Social Computing Networking (ISPA/BDCloud/SocialCom/SustainCom), 2019, pp. 331–337.
T. Wang, Y. Liang, Y. Zhang, X. Zheng, M. Arif, J. Wang, and Q. Jin, “An intelligent dynamic offloading from cloud to edge for smart iot systems with big data,” IEEE Transactions on Network Science and Engineering, vol. 7, no. 4, pp. 2598–2607, 2020.
Y. Hao, Y. Jiang, T. Chen, D. Cao, and M. Chen, “itaskoffloading: Intelligent task offloading for a cloud-edge collaborative system,” IEEE Network, vol. 33, no. 5, pp. 82–88, 2019.
Y. Zhang, B. Di, Z. Zheng, J. Lin, and L. Song, “Joint data offloading and resource allocation for multi-cloud heterogeneous mobile edge computing using multi-agent reinforcement learning,” in 2019 IEEE Global Communications Conference (GLOBECOM), 2019, pp. 1–6.
R. Dautov, S. Distefano, D. Bruneo, F. Longo, G. Merlino, and A. Puliafito, “Data processing in cyber-physical-social systems through edge computing,” IEEE Access, vol. 6, pp. 29 822–29 835, 2018.
X. Yang, Z. Fei, J. Zheng, N. Zhang, and A. Anpalagan, “Joint multi-user computation offloading and data caching for hybrid mobile cloud/edge computing,” IEEE Transactions on Vehicular Technology, vol. 68, no. 11, pp. 11 018–11 030, 2019.
F. R. de Souza, M. Dias de Assunc¸ao, E. Caron, and A. da Silva Veith, “An optimal model for optimizing the placement and parallelism of data stream processing applications on cloud-edge computing,” in 2020 IEEE 32nd International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), 2020, pp. 59–66.
A. Qin, C. Cai, Q. Wang, Y. Ni, and H. Zhu, “Game theoretical multiuser computation offloading for mobile-edge cloud computing,” in 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), 2019, pp. 328–332.
L. Yang, Z. Dai, and K. Li, “An offloading strategy based on cloud and edge computing for industrial internet,” in 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), 2019, pp. 1666–1673.
Z. Wang, T. Wu, Z. Zhang, and H. Zhou, “A game theory-based computation offloading method in cloud-edge computing networks,” in 2021 International Conference on Computer Communications and Networks (ICCCN), 2021, pp. 1–6.
V. De Maio and I. Brandic, “First hop mobile offloading of dag computations,” in 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), 2018, pp. 83–92.
Y. Wu, G. Lu, N. Jin, L. Fu and J. Zhuan Zhao, "Trusted Fog Computing for Privacy Smart Contract Blockchain," 2021 IEEE 6th International Conference on Signal and Image Processing (ICSIP), 2021, pp. 1042-1047
W. Li, M. He, W. Zhu, and J. Zheng, “A study on lightweight and secure edge computing based blockchain,” in 2021 IEEE 12th International Conference on Software Engineering and Service Science (ICSESS), 2021, pp. 256–261.
K.-L. Wright, M. Martinez, U. Chadha, and B. Krishnamachari, “Smartedge: A smart contract for edge computing,” in 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2018, pp. 1685–1690.
S. Xu, C. Guo, R. Q. Hu, and Y. Qian, “Blockchain inspired secure computation offloading in a vehicular cloud network,” IEEE Internet of Things Journal, pp. 1–1, 2021.
S. Battula, S. Garg, R. Naha, M. Amin, B. Kang, and E. Aghasian, “A blockchain-based framework for automatic sla management in fog computing environments,” The Journal of Supercomputing, 05 2022.
X. Huang, X. Liu, Q. Chen, and J. Zhang, “Resource allocation and task offloading in blockchain-enabled fog computing networks,” in 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall), 2021, pp. 01–05.
J. Zhang, Y. Huang, F. Ye, and Y. Yang, “A novel proof-of-reputation consensus for storage allocation in edge blockchain systems,” in 2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS), 2021, pp. 1–10.
S. Iqbal, A. W. Malik, A. U. Rahman, and R. M. Noor, “Blockchainbased reputation management for task offloading in micro-level vehicular fog network,” IEEE Access, vol. 8, pp. 52 968–52 980, 2020.
Y. Du, Z. Wang, J. Li, L. Shi, D. N. K. Jayakody, Q. Chen, W. Chen, and Z. Han, “Blockchain-aided edge computing market: Smart contract and consensus mechanisms,” IEEE Transactions on Mobile Computing, pp. 1–1, 2022.
Publicado
21/11/2022
Como Citar
KONZEN, Marcos P.; LORENZON, Arthur F.; LUIZELLI, Marcelo C.; ROSSI, Fábio D.; MATTOS, Júlio C. B..
A comprehensive review of task offloading in edge computing. In: TRABALHOS EM ANDAMENTO - SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SISTEMAS COMPUTACIONAIS (SBESC), 12. , 2022, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 62-67.
ISSN 2763-9002.
DOI: https://doi.org/10.5753/sbesc_estendido.2022.228130.
