Energy-SLA-aware genetic algorithm for edge–cloud integrated computation offloading in vehicular networks
Vehicular Ad Hoc Networks (VANET) is an emerging technology that enables a comfortable, safe, and efficient travel experience by providing mechanisms to execute applications related to traffic congestions, road accidents, autonomous driving, and entertainment. The mobile vehicles in VANET are charac...
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| Veröffentlicht in: | Future generation computer systems Jg. 135; S. 205 - 222 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.10.2022
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| ISSN: | 0167-739X, 1872-7115 |
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| Abstract | Vehicular Ad Hoc Networks (VANET) is an emerging technology that enables a comfortable, safe, and efficient travel experience by providing mechanisms to execute applications related to traffic congestions, road accidents, autonomous driving, and entertainment. The mobile vehicles in VANET are characterized by low computational and storage capabilities. In such scenarios, to meet applications’ performance requirements, requests from vehicles are offloaded to edge and cloud servers. The high energy consumption of these servers increases operating costs and threatens the environment. Energy-aware offloading strategies have been introduced to tackle this problem. Existing works on computation offloading focus on optimizing the energy consumption of either the IoT devices/mobile/vehicles and/or the edge servers. This paper proposes a novel offloading algorithm that optimizes the energy of edge–cloud integrated computing platforms based on Evolutionary Genetic Algorithm (EGA) while maintaining applications’ Service Level Agreement (SLA). The proposed algorithm employs an adaptive penalty function to incorporate the optimization constraints within EGA. Comparative analysis and numerical experiments are carried out between the proposed algorithm, random and genetic algorithm-based offloading, and no offloading baseline approaches. On average, the results show that the proposed algorithm saves 2.97 times and 1.37 times more energy than the random and no offloading algorithms respectively. Our algorithm has 0.3% of violations versus 52.8% and 62.8% by the random and no offloading approaches respectively. While the energy-non-SLA-aware genetic algorithm saves, on average, 1.22 times more energy than our approach, however, it violates SLAs by 159 times more than our proposed approach.
•Evolutionary genetic algorithm for computation offloading is proposed.•Integrated edge–cloud computing system for VANET is considered.•The objective is to reduce energy consumption of edge–cloud integrated platform.•QoS constraints are handled using adaptive penalty function.•Algorithm saves energy while preserving SLA. |
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| AbstractList | Vehicular Ad Hoc Networks (VANET) is an emerging technology that enables a comfortable, safe, and efficient travel experience by providing mechanisms to execute applications related to traffic congestions, road accidents, autonomous driving, and entertainment. The mobile vehicles in VANET are characterized by low computational and storage capabilities. In such scenarios, to meet applications’ performance requirements, requests from vehicles are offloaded to edge and cloud servers. The high energy consumption of these servers increases operating costs and threatens the environment. Energy-aware offloading strategies have been introduced to tackle this problem. Existing works on computation offloading focus on optimizing the energy consumption of either the IoT devices/mobile/vehicles and/or the edge servers. This paper proposes a novel offloading algorithm that optimizes the energy of edge–cloud integrated computing platforms based on Evolutionary Genetic Algorithm (EGA) while maintaining applications’ Service Level Agreement (SLA). The proposed algorithm employs an adaptive penalty function to incorporate the optimization constraints within EGA. Comparative analysis and numerical experiments are carried out between the proposed algorithm, random and genetic algorithm-based offloading, and no offloading baseline approaches. On average, the results show that the proposed algorithm saves 2.97 times and 1.37 times more energy than the random and no offloading algorithms respectively. Our algorithm has 0.3% of violations versus 52.8% and 62.8% by the random and no offloading approaches respectively. While the energy-non-SLA-aware genetic algorithm saves, on average, 1.22 times more energy than our approach, however, it violates SLAs by 159 times more than our proposed approach.
•Evolutionary genetic algorithm for computation offloading is proposed.•Integrated edge–cloud computing system for VANET is considered.•The objective is to reduce energy consumption of edge–cloud integrated platform.•QoS constraints are handled using adaptive penalty function.•Algorithm saves energy while preserving SLA. |
| Author | Buyya, Rajkumar Ismail, Leila Materwala, Huned Shubair, Raed M. |
| Author_xml | – sequence: 1 givenname: Huned surname: Materwala fullname: Materwala, Huned organization: Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory, Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain, Abu Dhabi 15551, United Arab Emirates – sequence: 2 givenname: Leila orcidid: 0000-0003-0946-1818 surname: Ismail fullname: Ismail, Leila email: leila@uaeu.ac.ae organization: Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory, Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain, Abu Dhabi 15551, United Arab Emirates – sequence: 3 givenname: Raed M. surname: Shubair fullname: Shubair, Raed M. organization: Department of Electrical and Computer Engineering, New York University (NYU), Abu Dhabi, United Arab Emirates – sequence: 4 givenname: Rajkumar surname: Buyya fullname: Buyya, Rajkumar organization: Cloud Computing and Distributed Systems (CLOUDS) Lab, School of Computing and Information Systems, The University of Melbourne, Australia |
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| Keywords | Vehicular Ad Hoc Networks (VANET) Quality of service (QoS) Edge–cloud computing Computation offloading Energy-efficiency Evolutionary genetic optimization algorithm |
| Language | English |
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| References | Ismail, Materwala (b31) 2021; 21 (b46) 2021 Li, Tao, Qin, Liu, Zhang, Zhang (b19) 2019; 7 (b47) 2021 Ismail, Materwala (b12) 2021; 191 Thakur, Meghwani, Jalota (b40) 2014; 235 Ismail, Materwala (b9) 2020 Mishra, Shinde (b37) 2021 Ismail, Abed (b38) 2019; 7 Zhang, Tian, Ren, Chen, Chao, Zhao, Dong, Wang (b33) 2018; 12 Zhai, Sun, Wu, Zhu, Shen, Du, Guizani (b23) 2020; 22 Mahmud, Srirama, Ramamohanarao, Buyya (b14) 2019; 132 Wiering (b36) 2004 (b44) 2021 H. Hao, C. Xu, L. Zhong, G.-M. Muntean, A multi-update deep reinforcement learning algorithm for edge computing service offloading, in: Proceedings of the 28th ACM International Conference on Multimedia, 2020, pp. 3256–3264. Khan, Ahmed, Hakak, Yaqoob, Ahmed (b8) 2019; 97 Mershad, Cheikhrouhou, Ismail (b4) 2021; 32 Ismail, Zhang (b6) 2018 Guo, Zhang, Liu, Zhang (b20) 2018; 6 Liu, Zhang, Buyya, Fan (b41) 2017; 29 Liu, Wei, Xiao, Liu, Xu, Tian (b17) 2020; 3 Materwala, Ismail (b32) 2022; 197 Ismail, Materwala (b39) 2020; 53 Ismail (b15) 2007 Auluck, Azim, Fizza (b51) 2019 Pu, Chen, Mao, Xie, Xu (b28) 2018; 6 (b43) 2009 Zhang, Hu, Ning, Ngai, Zhou, Wei, Cheng, Hu (b24) 2017; 5 Mell, Grance (b5) 2011 Almutairi, Aldossary (b53) 2021; 10 Akbari, Rashidi, Alizadeh (b42) 2017; 61 Ning, Huang, Wang, Rodrigues, Guo (b21) 2019; 33 Jaddoa, Sakellari, Panaousis, Loukas, Sarigiannidis (b52) 2020; 101 Sharma, Kaul (b1) 2021; 28 Goudarzi, Wu, Palaniswami, Buyya (b29) 2020; 20 Raza, Wang, Ahmed, Anwar (b7) 2019; 2019 Huang, Xu, Lai, Chen, Zhang (b27) 2020; 2020 Carlucci (b48) 2017 Ismail, Fardoun (b11) 2016; 13 Fang, Wang, He, Huang, Liu, Zhang (b34) 2018; 2018 Xu, Li, Huang, Xue, Peng, Qi, Dou (b22) 2019; 133 Peng, Zhu, Zhang, Liu, Zhang, Leung, Zheng (b30) 2019; 2019 Ismail, Materwala (b10) 2018; 135 Sookhak, Yu, He, Talebian, Safa, Zhao, Khan, Kumar (b3) 2017; 12 Yang, Li, Redmill, Özgüner (b50) 2019 Li, Chang, Ge, Pan, Hu, Huang (b25) 2021; 2021 Yangui, Goscinski, Drira, Tari, Benslimane (b16) 2021; 118 (b45) 2021 (b49) 2021 Ullah, Yaqoob, Imran, Ning (b2) 2018; 7 Ismail, Mills, Hennebelle (b13) 2008 Huang, He, Zhang (b26) 2020 Belkhir, Elmeligi (b18) 2018; 177 (10.1016/j.future.2022.04.009_b44) 2021 Ismail (10.1016/j.future.2022.04.009_b15) 2007 Almutairi (10.1016/j.future.2022.04.009_b53) 2021; 10 Ning (10.1016/j.future.2022.04.009_b21) 2019; 33 Jaddoa (10.1016/j.future.2022.04.009_b52) 2020; 101 (10.1016/j.future.2022.04.009_b47) 2021 Zhai (10.1016/j.future.2022.04.009_b23) 2020; 22 Huang (10.1016/j.future.2022.04.009_b26) 2020 Pu (10.1016/j.future.2022.04.009_b28) 2018; 6 Li (10.1016/j.future.2022.04.009_b25) 2021; 2021 Peng (10.1016/j.future.2022.04.009_b30) 2019; 2019 Thakur (10.1016/j.future.2022.04.009_b40) 2014; 235 Yang (10.1016/j.future.2022.04.009_b50) 2019 Ismail (10.1016/j.future.2022.04.009_b12) 2021; 191 Materwala (10.1016/j.future.2022.04.009_b32) 2022; 197 Huang (10.1016/j.future.2022.04.009_b27) 2020; 2020 Zhang (10.1016/j.future.2022.04.009_b24) 2017; 5 10.1016/j.future.2022.04.009_b35 Ismail (10.1016/j.future.2022.04.009_b31) 2021; 21 Mershad (10.1016/j.future.2022.04.009_b4) 2021; 32 Ismail (10.1016/j.future.2022.04.009_b38) 2019; 7 Sookhak (10.1016/j.future.2022.04.009_b3) 2017; 12 (10.1016/j.future.2022.04.009_b45) 2021 Belkhir (10.1016/j.future.2022.04.009_b18) 2018; 177 Yangui (10.1016/j.future.2022.04.009_b16) 2021; 118 (10.1016/j.future.2022.04.009_b43) 2009 Goudarzi (10.1016/j.future.2022.04.009_b29) 2020; 20 Ismail (10.1016/j.future.2022.04.009_b39) 2020; 53 Xu (10.1016/j.future.2022.04.009_b22) 2019; 133 Li (10.1016/j.future.2022.04.009_b19) 2019; 7 Guo (10.1016/j.future.2022.04.009_b20) 2018; 6 Zhang (10.1016/j.future.2022.04.009_b33) 2018; 12 Raza (10.1016/j.future.2022.04.009_b7) 2019; 2019 Fang (10.1016/j.future.2022.04.009_b34) 2018; 2018 Ismail (10.1016/j.future.2022.04.009_b11) 2016; 13 Ismail (10.1016/j.future.2022.04.009_b9) 2020 Ismail (10.1016/j.future.2022.04.009_b13) 2008 Mishra (10.1016/j.future.2022.04.009_b37) 2021 (10.1016/j.future.2022.04.009_b49) 2021 (10.1016/j.future.2022.04.009_b46) 2021 Liu (10.1016/j.future.2022.04.009_b17) 2020; 3 Mell (10.1016/j.future.2022.04.009_b5) 2011 Ismail (10.1016/j.future.2022.04.009_b10) 2018; 135 Ullah (10.1016/j.future.2022.04.009_b2) 2018; 7 Ismail (10.1016/j.future.2022.04.009_b6) 2018 Khan (10.1016/j.future.2022.04.009_b8) 2019; 97 Auluck (10.1016/j.future.2022.04.009_b51) 2019 Carlucci (10.1016/j.future.2022.04.009_b48) 2017 Akbari (10.1016/j.future.2022.04.009_b42) 2017; 61 Liu (10.1016/j.future.2022.04.009_b41) 2017; 29 Mahmud (10.1016/j.future.2022.04.009_b14) 2019; 132 Sharma (10.1016/j.future.2022.04.009_b1) 2021; 28 Wiering (10.1016/j.future.2022.04.009_b36) 2004 |
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