{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T16:11:26Z","timestamp":1774368686852,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T00:00:00Z","timestamp":1750118400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research and Development Program of Hubei Province","award":["2022BCA035"],"award-info":[{"award-number":["2022BCA035"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>With the demand for workflow processing driven by edge computing in the Internet of Things (IoT) and cloud computing growing at an exponential rate, task scheduling in heterogeneous distributed systems has become a key challenge to meet real-time constraints in resource-constrained environments. Existing studies now attempt to achieve the best balance in terms of time constraints, energy efficiency, and system reliability in Dynamic Voltage and Frequency Scaling environments. This study proposes a two-stage collaborative optimization strategy. With the help of an innovative algorithm design and theoretical analysis, the multi-objective optimization challenges mentioned above are systematically solved. First, based on a reliability-constrained model, we propose a topology-aware dynamic priority scheduling algorithm (EAWRS). This algorithm constructs a node priority function by incorporating in-degree\/out-degree weighting factors and critical path analysis to enable multi-objective optimization. Second, to address the time-varying reliability characteristics introduced by DVFS, we propose a Fibonacci search-based dynamic frequency scaling algorithm (SEFFA). This algorithm effectively reduces energy consumption while ensuring task reliability, achieving sub-optimal processor energy adjustment. The collaborative mechanism of EAWRS and SEFFA has well solved the dynamic scheduling challenge based on DAG in heterogeneous multi-core processor systems in the Internet of Things environment. Experimental evaluations conducted at various scales show that, compared with the three most advanced scheduling algorithms, the proposed strategy reduces energy consumption by an average of 14.56% (up to 58.44% under high-reliability constraints) and shortens the makespan by 2.58\u201356.44% while strictly meeting reliability requirements.<\/jats:p>","DOI":"10.3390\/bdcc9060160","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:08:06Z","timestamp":1750219686000},"page":"160","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Research on High-Reliability Energy-Aware Scheduling Strategy for Heterogeneous Distributed Systems"],"prefix":"10.3390","volume":"9","author":[{"given":"Ziyu","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China"},{"name":"Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan University of Science and Technology, Wuhan 430065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China"},{"name":"Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan University of Science and Technology, Wuhan 430065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2943-6013","authenticated-orcid":false,"given":"Lin","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China"},{"name":"Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan University of Science and Technology, Wuhan 430065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Tao","sequence":"additional","affiliation":[{"name":"Electronic Information School, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Rinaldi, M., Wang, S., Geronel, R.S., and Primatesta, S. (2024). Application of Task Allocation Algorithms in Multi-UAV Intelligent Transportation Systems: A Critical Review. Big Data Cogn. Comput., 8.","DOI":"10.3390\/bdcc8120177"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.17762\/turcomat.v12i4.612","article-title":"Task scheduling algorithms in cloud computing: A review","volume":"12","author":"Ibrahim","year":"2021","journal-title":"Turk. J. Comput. Math. Educ. TURCOMAT"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.neucom.2018.09.091","article-title":"Video steganography: A review","volume":"335","author":"Liu","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"22358","DOI":"10.1364\/OE.395866","article-title":"Ensemble model with cascade attention mechanism for high-resolution remote sensing image scene classification","volume":"28","author":"Li","year":"2020","journal-title":"Opt. Express"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.future.2019.01.059","article-title":"Task migration for mobile edge computing using deep reinforcement learning","volume":"96","author":"Zhang","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1007\/s11265-022-01765-4","article-title":"Optimization of Big Data Parallel Scheduling Based on Dynamic Clustering Scheduling Algorithm","volume":"94","author":"Liu","year":"2022","journal-title":"J. Signal Process. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.future.2015.05.012","article-title":"Energy-efficient scheduling of real-time tasks with shared resources","volume":"56","author":"Wu","year":"2016","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/s40747-019-00122-6","article-title":"A review of energy-efficient scheduling in intelligent production systems","volume":"6","author":"Gao","year":"2020","journal-title":"Complex Intell. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Casini, D., Biondi, A., Nelissen, G., and Buttazzo, G. (2020, January 21\u201324). A holistic memory contention analysis for parallel real-time tasks under partitioned scheduling. Proceedings of the 2020 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), Sydney, NSW, Australia.","DOI":"10.1109\/RTAS48715.2020.000-3"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/TSUSC.2021.3057983","article-title":"A survey of low-energy parallel scheduling algorithms","volume":"7","author":"Xie","year":"2021","journal-title":"IEEE Trans. Sustain. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1007\/s10586-021-03512-z","article-title":"Task scheduling algorithms for energy optimization in cloud environment: A comprehensive review","volume":"25","author":"Ghafari","year":"2022","journal-title":"Clust. Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"103333","DOI":"10.1016\/j.jnca.2022.103333","article-title":"Deadline-aware and energy-efficient IoT task scheduling in fog computing systems: A semi-greedy approach","volume":"201","author":"Azizi","year":"2022","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Baciu, M.D., Capota, E.A., St\u00e2ngaciu, C.S., Curiac, D.-I., and Micea, M.V. (2023). Multi-Core Time-Triggered OCBP-Based Scheduling for Mixed Criticality Periodic Task Systems. Sensors, 23.","DOI":"10.3390\/s23041960"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1007\/s00607-021-01030-9","article-title":"Energy-efficient workflow scheduling with budget-deadline constraints for cloud","volume":"104","author":"Pashazadeh","year":"2022","journal-title":"Computing"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"11643","DOI":"10.1007\/s11227-021-03764-x","article-title":"Reliability-aware task scheduling for energy efficiency on heterogeneous multiprocessor systems","volume":"77","author":"Deng","year":"2021","journal-title":"J. Supercomput."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wen, Y., Wang, Z., and O\u2019boyle, M.F.P. (2014, January 17\u201320). Smart multi-task scheduling for OpenCL programs on CPU\/GPU heterogeneous platforms. Proceedings of the 2014 21st International Conference on High Performance Computing (HiPC), Goa, India.","DOI":"10.1109\/HiPC.2014.7116910"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"110167","DOI":"10.1016\/j.epsr.2024.110167","article-title":"Cooperative energy scheduling of interconnected microgrid system considering renewable energy resources and electric vehicles","volume":"229","author":"Babaei","year":"2024","journal-title":"Electr. Power Syst. Res."},{"key":"ref_18","first-page":"155","article-title":"Execution-variance-aware task allocation for energy minimization on the big. little architecture","volume":"22","author":"Qin","year":"2019","journal-title":"Sustain. Comput. Inform. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"58948","DOI":"10.1109\/ACCESS.2020.2982956","article-title":"CPU\u2013GPU utilization aware energy-efficient scheduling algorithm on heterogeneous computing systems","volume":"8","author":"Tang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"106212","DOI":"10.1016\/j.cor.2023.106212","article-title":"Optimising makespan and energy consumption in task scheduling for parallel systems","volume":"154","author":"Stewart","year":"2023","journal-title":"Comput. Oper. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.jpdc.2020.04.008","article-title":"A new energy-aware tasks scheduling approach in fog computing using hybrid meta-heuristic algorithm","volume":"143","author":"Hosseinioun","year":"2020","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.knosys.2019.01.023","article-title":"Task scheduling in cloud computing based on hybrid moth search algorithm and differential evolution","volume":"169","author":"Xiong","year":"2019","journal-title":"Knowl.-Based Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"103141","DOI":"10.1016\/j.sysarc.2024.103141","article-title":"Energy-aware fault-tolerant scheduling for imprecise mixed-criticality systems with semi-clairvoyance Journal of Systems Architecture","volume":"151","author":"Zhang","year":"2024","journal-title":"J. Syst. Archit."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1109\/TPDS.2019.2959533","article-title":"Task scheduling for energy consumption constrained parallel applications on heterogeneous computing systems","volume":"31","author":"Quan","year":"2019","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1109\/71.993206","article-title":"Performance-effective and low-complexity task scheduling for heterogeneous computing","volume":"13","author":"Topcuoglu","year":"2002","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1109\/TSUSC.2017.2711362","article-title":"Energy-efficient fault-tolerant scheduling of reliable parallel applications on heterogeneous distributed embedded systems","volume":"3","author":"Xie","year":"2017","journal-title":"IEEE Trans. Sustain. Comput."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1109\/TSC.2017.2665552","article-title":"Minimizing redundancy to satisfy reliability requirement for a parallel application on heterogeneous service-oriented systems","volume":"13","author":"Xie","year":"2017","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3336","DOI":"10.1109\/TCAD.2020.3013045","article-title":"Dynamic DAG scheduling on multiprocessor systems: Reliability, energy, and makespan","volume":"39","author":"Huang","year":"2020","journal-title":"IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Schieber, B., Samineni, B., and Vahidi, S. (2023, January 22\u201324). Interweaving real-time jobs with energy harvesting to maximize throughput. Proceedings of the International Conference and Workshops on Algorithms and Computation, Hsinchu, Taiwan.","DOI":"10.21203\/rs.3.rs-3054888\/v1"},{"key":"ref_30","unstructured":"Ghajari, G., Ghajari, E., Mohammadi, H., and Amsaad, F. (2025). Intrusion Detection in IoT Networks Using Hyperdimensional Computing: A Case Study on the NSL-KDD Dataset. arXiv."},{"key":"ref_31","unstructured":"Ghajari, G., Ghimire, A., Ghajari, E., and Amsaad, F. (2025). Network Anomaly Detection for IoT Using Hyperdimensional Computing on NSL-KDD. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Rastgoo, S., Mahdavi, Z., Nasab, M.A., Zand, M., and Padmanaban, S. (2022). Using an intelligent control method for electric vehicle charging in microgrids. World Electr. Veh. J., 13.","DOI":"10.3390\/wevj13120222"}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/6\/160\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:53:56Z","timestamp":1760032436000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/6\/160"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,17]]},"references-count":32,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["bdcc9060160"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9060160","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,17]]}}}