{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:19:19Z","timestamp":1760660359431,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T00:00:00Z","timestamp":1760572800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>With the rapid advancement of mobile edge computing and Internet of Things (IoT) technologies, device-to-device (D2D) cooperative computing has garnered significant attention due to its low latency and high resource utilization efficiency. However, workflow task scheduling in D2D networks poses considerable challenges, such as severe heterogeneity in device resources and complex inter-task dependencies, which may result in low resource utilization and inefficient scheduling, ultimately breaking the computational symmetry\u2014a balanced state of computational resource allocation among terminal devices and load balance across the network. To address these challenges and restore system-level symmetry, a novel workflow task scheduling method tailored for D2D cooperative environments is proposed. First, a Non-dominated Sorting Genetic Algorithm (NSGA) is employed to optimize the allocation of computational resources across terminal devices, maximizing the overall computing capacity while achieving a symmetrical and balanced resource distribution. A scoring mechanism and a normalization strategy are introduced to accurately assess the compatibility between tasks and processors, thereby enhancing resource utilization during scheduling. Subsequently, task priorities are determined based on the calculation of each task\u2019s Shapley value, ensuring that critical tasks are scheduled preferentially. Finally, a hybrid algorithm integrating Q-learning with Asynchronous Advantage Actor\u2013Critic (A3C) is developed to perform precise and adaptive task scheduling, improving system load balancing and execution efficiency. Extensive simulation results demonstrate that the proposed method outperforms state-of-art methods in both energy consumption and response time, with improvements of 26.34% and 29.98%, respectively, underscoring the robustness and superiority of the proposed method.<\/jats:p>","DOI":"10.3390\/sym17101746","type":"journal-article","created":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T12:17:02Z","timestamp":1760617022000},"page":"1746","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Achieving Computational Symmetry: A Novel Workflow Task Scheduling and Resource Allocation Method for D2D Cooperation"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7287-4361","authenticated-orcid":false,"given":"Xianzhi","family":"Cao","sequence":"first","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chang","family":"Lv","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiali","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0445-4750","authenticated-orcid":false,"given":"Jian","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,16]]},"reference":[{"key":"ref_1","unstructured":"Baghiani, R., Guezouli, L., Korichi, A., and Barka, K. (2022, January 30\u201331). Scalable mobile computing: From cloud computing to mobile edge computing. Proceedings of the 2022 5th International Conference on Networking, Information Systems and Security (NISS), Bandung, Indonesia."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.future.2021.01.021","article-title":"Security and energy-aware collaborative task offloading in D2D communication","volume":"118","author":"Li","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Dai, H., Liu, S., Liu, B., Fan, Z., and Wang, J. (2024, January 23\u201325). Technical Middleware Microservice Orchestration and Fault-Tolerant Mechanism Algorithms for Containerized Deployment. Proceedings of the 2024 IEEE 6th International Conference on Civil Aviation Safety and Information Technology (ICCASIT), Hangzhou, China.","DOI":"10.1109\/ICCASIT62299.2024.10828011"},{"key":"ref_4","unstructured":"Kang, K., Zhu, P., and Zhang, F. (2024, January 28\u201330). Research on the Resource Allocation Optimization Model of Automobile Based on Cloud Computing Resource Scheduling Algorithm. Proceedings of the 2024 4th International Signal Processing, Communications and Engineering Management Conference (ISPCEM), Montreal, QC, Canada."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TPDS.2024.3492210","article-title":"Real Relative Encoding Genetic Algorithm for Workflow Scheduling in Heterogeneous Distributed Computing Systems","volume":"36","author":"Jiang","year":"2025","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mishra, P.K., and Chaturvedi, A.K. (2023, January 19\u201320). State-of-the-art and research challenges in task scheduling and resource allocation methods for cloud-fog environment. Proceedings of the 2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT), Jaipur, India.","DOI":"10.1109\/ICCT56969.2023.10076030"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Xu, X., Tian, Q., Xing, Y., Yin, B., and Hu, A. (2022, January 27\u201329). Large-Scale Data Intensive Heterogeneous Task Scheduling Method Based on Parallel GATS-TS Algorithm. Proceedings of the 2022 4th International Conference on Communications, Information System and Computer Engineering (CISCE), Shenzhen, China.","DOI":"10.1109\/CISCE55963.2022.9851157"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Tan, Q., Chen, W., and Liu, D. (2024, January 19\u201321). A Deep Reinforcement Learning Scheduling Algorithm for Heterogeneous Tasks on Heterogeneous Multi-Core Processors. Proceedings of the 2024 6th International Conference on Electronics and Communication, Network and Computer Technology (ECNCT), Guangzhou, China.","DOI":"10.1109\/ECNCT63103.2024.10704560"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1007\/s10489-025-06524-z","article-title":"Dueling double deep Q-network-based stamping resources intelligent scheduling for automobile manufacturing in cloud manufacturing environment","volume":"55","author":"Hu","year":"2025","journal-title":"Appl. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Cao, Y. (2024, January 8\u201310). Optimization of Distributed Algorithms in Cloud Computing Environment. Proceedings of the 2024 5th International Conference on Artificial Intelligence and Computer Engineering (ICAICE), Wuhu, China.","DOI":"10.1109\/ICAICE63571.2024.10863955"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cao, X., Chen, C., Li, S., Lv, C., Li, J., and Wang, J. (2025). Research on computing task scheduling method for distributed heterogeneous parallel systems. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-94068-0"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"109832","DOI":"10.1016\/j.compeleceng.2024.109832","article-title":"Function offloading approaches in serverless computing: A Survey","volume":"120","author":"Ghorbian","year":"2024","journal-title":"Comput. Electr. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"103291","DOI":"10.1016\/j.sysarc.2024.103291","article-title":"Function placement approaches in serverless computing: A survey","volume":"157","author":"Ghorbian","year":"2024","journal-title":"J. Syst. Archit."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"134453","DOI":"10.1109\/ACCESS.2024.3462518","article-title":"Heuristic Scheduling Algorithm for Workflow Applications in Cloud-fog Computing Based on Realistic Client Port Communication","volume":"12","author":"Chongdarakul","year":"2024","journal-title":"IEEE Access"},{"key":"ref_15","unstructured":"Jayasena, K.P.N., and Thisarasinghe, B.S. (2019, January 16\u201318). Optimized task scheduling on fog computing environment using meta heuristic algorithms. Proceedings of the 2019 IEEE International Conference on Smart Cloud (SmartCloud), Tokyo, Japan."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1109\/TGCN.2022.3201615","article-title":"Auxiliary-task-based energy-efficient resource orchestration in mobile edge computing","volume":"7","author":"Zhu","year":"2022","journal-title":"IEEE Trans. Green Commun. Netw."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chelladurai, A., Deepak, M.D., Falkowski-Gilski, P., and Bidare Divakarachari, P. (2025). Multi-Joint Symmetric Optimization Approach for Unmanned Aerial Vehicle Assisted Edge Computing Resources in Internet of Things-Based Smart Cities. Symmetry, 17.","DOI":"10.3390\/sym17040574"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hu, L., Wu, X., and Che, X. (2025). HICA: A Hybrid Scientific Workflow Scheduling Algorithm for Symmetric Homogeneous Resource Cloud Environments. Symmetry, 17.","DOI":"10.3390\/sym17020280"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Priya, A., and Mandal, S. (2024). Parallel Artificial Bee Colony Algorithm for Solving Advance Industrial Productivity Problems. Handbook of Research on Innovative Approaches to Information Technology in Library and Information Science, IGI Global.","DOI":"10.4018\/979-8-3693-0807-3.ch002"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13677-021-00232-y","article-title":"Adaptive offloading in mobile-edge computing for ultra-dense cellular networks based on genetic algorithm","volume":"10","author":"Liao","year":"2021","journal-title":"J. Cloud Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1109\/TSUSC.2022.3217014","article-title":"Design and analysis of heuristic algorithms for energy-constrained task scheduling with device-edge-cloud fusion","volume":"8","author":"Li","year":"2022","journal-title":"IEEE Trans. Sustain. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sun, H. (2022, January 23\u201325). Resource Deployment and Task Scheduling Based on Cloud Computing. Proceedings of the 2022 IEEE 2nd International Conference on Computer Systems (ICCS), Qingdao, China.","DOI":"10.1109\/ICCS56273.2022.9988014"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Polireddi, N., Suryadevara, M., Venkata, S., Rangineni, S., Koduru, S.K.R., and Agal, S. (2023, January 11\u201313). A Novel Study on Data Science for Data Security and Data Integrity with Enhanced Heuristic Scheduling in Cloud. Proceedings of the 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS), Pudukkottai, India.","DOI":"10.1109\/ICACRS58579.2023.10404262"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liang, Y., Zheng, K., Mei, Y., Fan, X., Huang, H., Xu, C., and Zou, H. (2025, January 18\u201320). Dynamic Dependent Task Scheduling for Real-Time Multi-edge-node Collaboration Computing. Proceedings of the 2025 International Conference on Sensor-Cloud and Edge Computing System (SCECS), Zhuhai, China.","DOI":"10.1109\/SCECS65243.2025.11065044"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Rajak, N., and Rajak, R. (2021). Performance Metrics for Comparison of Heuristics Task Scheduling Algorithms in Cloud Computing Platform. Machine Learning Approach for Cloud Data Analytics in IoT, Wiley.","DOI":"10.1002\/9781119785873.ch9"},{"key":"ref_26","unstructured":"Ye, B., Li, F., and Zhang, X. (2022, January 4\u20136). Cloud computing task scheduling algorithm based on dynamic priority. Proceedings of the 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, L., and Wu, Y. (2024, January 15\u201317). Research on Fog Computing Task Scheduling Strategy with Deadline Constraints. Proceedings of the 2024 IEEE 7th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China.","DOI":"10.1109\/IAEAC59436.2024.10503702"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1109\/TSC.2024.3351622","article-title":"Scheduling workflow tasks with unknown task execution time by combining machine-learning and greedy-optimization","volume":"17","author":"Yang","year":"2024","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Shi, Z., Zhang, Z., Dai, M., Xia, Z., Wen, H., and Huang, F. (2024, January 3\u20135). Deep Reinforcement Learning-Based Task Offloading for Multi-User Distributed Edge Computing. Proceedings of the 2024 30th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), Leeds, UK.","DOI":"10.1109\/M2VIP62491.2024.10746091"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Xu, F., Yin, Z., Li, Y., Zhang, F., and Xu, G. (2023, January 21\u201323). The Task Scheduling Algorithm for Fog Computing in Intelligent Production Lines Based on DQN. Proceedings of the 2023 15th International Conference on Communication Software and Networks (ICCSN), Shenyang, China.","DOI":"10.1109\/ICCSN57992.2023.10297319"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1830","DOI":"10.1109\/TIV.2023.3321679","article-title":"EPtask: Deep reinforcement learning based energy-efficient and priority-aware task scheduling for dynamic vehicular edge computing","volume":"9","author":"Li","year":"2023","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"9916","DOI":"10.1007\/s10489-022-03963-w","article-title":"Deep reinforcement learning for fault-tolerant workflow scheduling in cloud environment","volume":"53","author":"Dong","year":"2023","journal-title":"Appl. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lu, H., Cheng, S., and Zhang, X. (2025). An Improved Whale Migration Algorithm for Global Optimization of Collaborative Symmetric Balanced Learning and Cloud Task Scheduling. Symmetry, 17.","DOI":"10.3390\/sym17060841"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Prasanna, S., and Gulati, A.S. (2024, January 8\u20139). Fairness in CPU Scheduling: A Probabilistic Algorithm. Proceedings of the 2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT), Kollam, India.","DOI":"10.1109\/ICCPCT61902.2024.10673307"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Jaaz, Z.A., Abdulrahman, S.A., and Mushgil, H.M. (2022, January 6\u20137). A dynamic task scheduling model for mobile cloud computing. Proceedings of the 2022 9th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), Jakarta, Indonesia.","DOI":"10.23919\/EECSI56542.2022.9946526"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Karthik, G.M., Gupta, A., Rajeshgupta, S., Jha, A., Sivasangari, A., and Mishra, B.P. (2023, January 27\u201329). Efficient Task Scheduling in Cloud Environment Based On Dynamic Priority and Optimized Technique. Proceedings of the 2023 International Conference on Artificial Intelligence and Smart Communication (AISC), Greater Noida, India.","DOI":"10.1109\/AISC56616.2023.10085447"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, C., Li, S., Wang, C., Cao, X., and Yang, L. (2024). Researching the CNN Collaborative Inference Mechanism for Heterogeneous Edge Devices. Sensors, 24.","DOI":"10.3390\/s24134176"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Liao, D., Chen, B., Pan, J., Huang, A., and Mo, X. (2023, January 17\u201319). Resilient scheduling of massive heterogeneous cloud resources considering energy consumption uncertainty. Proceedings of the 2023 5th International Conference on Frontiers Technology of Information and Computer (ICFTIC), Qiangdao, China.","DOI":"10.1109\/ICFTIC59930.2023.10455844"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/10\/1746\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T13:06:24Z","timestamp":1760619984000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/10\/1746"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,16]]},"references-count":38,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["sym17101746"],"URL":"https:\/\/doi.org\/10.3390\/sym17101746","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,16]]}}}