پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 161 |
| تعداد شمارهها | 6,532 |
| تعداد مقالات | 70,500 |
| تعداد مشاهده مقاله | 124,086,640 |
| تعداد دریافت فایل اصل مقاله | 97,189,942 |
Performance Oriented Scheduling and Allocation Technique in Edge-Fog-Cloud Collaborative Environment | ||
| Advances in Industrial Engineering | ||
| دوره 58، شماره 2، اسفند 2024، صفحه 439-456 اصل مقاله (740.24 K) | ||
| شناسه دیجیتال (DOI): 10.22059/aie.2024.379026.1899 | ||
| نویسندگان | ||
| Fatemeh Ghayoor1؛ Donya Rahmani* 2؛ Amir Hossein Jafari Pozveh3 | ||
| 1Ph.D. Candidate, Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran. | ||
| 2Associate Professor, Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran. | ||
| 3Ph.D., Department of electrical Engineering, Iran university of science and technology, Mobile Communication Company of Iran (MCI), Tehran, Iran. | ||
| چکیده | ||
| Nowadays, smart devices are becoming more prevalent in industrialized countries generating requests that require computational processing. Recently, collaborative edge-fog-cloud computing networks have been developed to allocate users' requests to computing resources. However, scheduling these requests while accounting for user requirements and limited resources remains a challenge. This study proposes a structured planning at multiple levels in a collaborative edge-fog-cloud environment to allocate and schedule requests, aiming to reduce network latency. So, an Integer Programming (IP) formulation is developed to minimize network latency for users. Some network limitations are considered in the model, such as network logic for directing requests to computational resources, meeting deadline and nodes capacity constraints. Additionally, constraints related to processing allowable workload volume are integrated into the model. This strategy changes the workload distribution among the edge, fog, and cloud layers to approximately 24%, 27%, and 47%, respectively, creating a more balanced workload distribution and reducing workload traffic. Other results indicate that simply increasing the computational capacity of the fog nodes does not always improve network performance. This suggests the need for a more analytical approach, considering additional factors simultaneously in the underlying network. These outcomes underscore the efficiency and practical significance of the proposed model in a collaborative edge-fog-cloud computing landscape. The findings can help cloud service enterprises in providing efficient services for addressing the request scheduling and allocation challenges in edge-fog-cloud networks. | ||
| کلیدواژهها | ||
| Edge-Fog-Cloud Network؛ Request Scheduling؛ Offloading؛ Allocation؛ Deadline | ||
| مراجع | ||
|
[1] B.Li, P.Hou, H.Wu and F.Hou, Optimal edge server deployment and allocation strategy in 5G ultra-dense networking environments. Pervasive and Mobile Computing; 72:101312.2021.
[2] A.Ali-Eldin, B.Wang, P.Shenoy. The hidden cost of the edge: a performance comparison of edge and cloud latencies. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1-12, 2021.
[3] Ulrike, Hack. (2021), What’s the real story behind the explosive growth of data?, www.red-gate.com
[4] N.Kherraf, S.Sanaa, M.Chadi and Ali Ghrayeb. "Latency and reliability-aware workload
assignment in IoT networks with mobile edge clouds." IEEE Transactions on Network and
Service Management 16, no. 4, 1435-1449, 2019.
[5] Zheng T, Wan J, Zhang J, Jiang C. Deep reinforcement learning-based workload scheduling for edge computing. Journal of Cloud Computing. 2022;11(1):3.
[6] L.Liu, Z.Chang, X.Guo, T.Ristaniemi. Multi-objective optimization for computation offloading in mobile-edge computing. In2017 IEEE symposium on computers and communications (ISCC), pp. 832-837). IEEE, 2017.
[7] H.Birhanie, MO.Adem. Optimized request offloading strategy in IoT edge computing network. Journal of King Saud University-Computer and Information Sciences;36(2):101942, 2024.
[8] Y.Ma, Y.Han, J.Wang, Q.Zhao, A constrained static scheduling strategy in edge computing for industrial cloud systems. International Journal of Information Technologies and Systems Approach (IJITSA), 14(1):33-61, 2021.
[9] Apinaya Prethi KN, Sangeetha M, Nithya S. Optimized scheduling with prioritization to enhance network sustainability in edge-cloud environment. Journal of Intelligent & Fuzzy Systems. 2023;44(3):4323-34.
[10] Prabhu R, Rajesh S. An Advanced Dynamic Scheduling for Achieving Optimal Resource Allocation. Computer Systems Science & Engineering. 2023;44(1).
[11] Rahul S, Bhardwaj V. Optimization of Resource Scheduling and Allocation Algorithms. In2022 Second International Conference on Interdisciplinary Cyber Physical Systems (ICPS) 2022 May 9 (pp. 141-145). IEEE.
[12] Szalay, P.Mátray, L.Toka. Real-time request scheduling in a FaaS cloud. 2021 IEEE 14th International Conference on Cloud Computing (CLOUD), pp. 497-507. IEEE, 2021.
[13] X.Zhou, W.Liang, K.Yan, W.Li, I.Kevin, K.Wang, J.Ma, Q.Jin. Edge-enabled two-stage scheduling based on deep reinforcement learning for internet of everything. IEEE Internet of Things Journal,10(4):3295-304, 2022.
[14] S.Bebortta, SS.Tripathy, UM.Modibbo, I.Ali. An optimal fog-cloud offloading framework for big data optimization in heterogeneous IoT networks. Decision Analytics Journal,8:100295, 2023.
[15] Y.Mao, X.Shang, Y.Yang. Joint resource management and flow scheduling for SFC deployment in hybrid edge-and-cloud network. InIEEE INFOCOM 2022-IEEE Conference on Computer Communications. 170-179, IEEE,2022.
[16] I.Ullah, HY.Youn,. Request classification and scheduling based on K-means clustering for edge computing. Wireless Personal Communications, 113(4):2611-24, 2020.
[17] CF.Liu, M.Bennis,HV.Poor HV, Latency and reliability-aware request offloading and resource allocation for mobile edge computing, 2017 IEEE Globecom Workshops (GC Wkshps) pp. 1-7. IEEE. 2017
[18] Q.Gao, GU.Fu, L.Li , J.Guo. A framework of cloud-edge collaborated digital twin for flexible job shop scheduling with conflict-free routing. Robotics and Computer-Integrated Manufacturing,86:102672, 2024.
[19] Wen S, Jia F, Lijun W, Hui L, Yu W, Linhui M. Research on resource scheduling optimization technology for power cloud edge collaboration. InSixth International Conference on Intelligent Computing, Communication, and Devices (ICCD 2023). 2023. (Vol. 12703, pp. 223-229). SPIE.
[20] Apinaya Prethi KN, Sangeetha M. A multi-objective optimization of resource management and minimum batch VM migration for prioritized request allocation in fog-edge-cloud computing. Journal of Intelligent & Fuzzy Systems. 2022;43(5):5985-95.
[21] Yang C, Xu H, Fan S, Cheng X, Liu M, Wang X. Efficient resource allocation policy for cloud edge end framework by reinforcement learning. In2022 IEEE 8th International Conference on Computer and Communications (ICCC) 2022 (pp. 1363-1367). IEEE.
[22] Buschmann P, Shorim MH, Helm M, Bröring A, Carle G. Request allocation in industrial edge networks with particle swarm optimization and deep reinforcement learning. In Proceedings of the 12th International Conference on the Internet of Things 2022 Nov 7 (pp. 239-247).
[23] Bi R, Peng T, Ren J, Fang X, Tan G. Joint service placement and computation scheduling in edge clouds. In2022 IEEE International Conference on Web Services (ICWS) 2022 Jul 10 (pp. 47-56). IEEE.
[24] Hua W, Liu P, Huang L. Energy-efficient resource allocation for heterogeneous edge-cloud computing. IEEE Internet of Things Journal. 2023 Jul 7.
[25] Zhang J, Ning Z, Ali RH, Waqas M, Tu S, Ahmad I. A many-objective ensemble optimization algorithm for the edge cloud resource scheduling problem. IEEE Transactions on Mobile Computing. 2023 Jan 9;23(2):1330-46.
[26] Jin H, Ma S, Ding Y, Deng X, Yao Z, Zhao J. Edge cloud cooperation environment driven multi-level edge computing method for delay optimization. In Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023) 2024 May 22 (Vol. 13176, pp. 513-518). SPIE.
[27] Khaleel MI. A dynamic weight–assignment load balancing approach for workflow scheduling in edge-cloud computing using ameliorated moth flame and rock hyrax optimization algorithms. Future Generation Computer Systems. 2024 Jun 1;155: 465-85.
[28] Ji T, Wan X, Guan X, Zhu A, Ye F. Towards optimal application offloading in heterogeneous edge-cloud computing. IEEE Transactions on Computers. 2023 Jun 28.
[29] Lin B, Lin C, Chen X, Xiong NN, Shen Q. A Fuzzy Scheduling Strategy for Intelligent Workflow Decision Making in Uncertain Edge-Cloud Environments. 2021;1–15. Available from: http://arxiv.org/abs/2107.01405
[30] Abulizi J, Hu Q, Wang W. Delay Optimization of Power Internet of Things based on Edge-Cloud Collaboration. In: 2022 IEEE 22nd International Conference on Communication Technology (ICCT). 2022. p. 905–10.
[31] Ji T, Wan X, Guan X, Zhu A, Ye F. Towards optimal application offloading in heterogeneous edge-cloud computing. IEEE Transactions on Computers. 2023 Jun 28.
[32] Li Y, Dai W, Gan X, Jin H, Fu L, Ma H, Wang X. Cooperative service placement and scheduling in edge clouds: A deadline-driven approach. IEEE Transactions on Mobile Computing. 2021 Feb 23;21(10):3519-35.
[33] Aslanpour MS, Gill SS, Toosi AN. Performance evaluation metrics for cloud, fog and edge computing: A review, taxonomy, benchmarks and standards for future research. Internet of Things [Internet]. 2020;12: 100273.
[34] Taleb T, Samdanis K, Mada B, Flinck H, Dutta S, Sabella D. On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration. IEEE Commun Surv Tutorials. 2017;19(3):1657–81.
| ||
|
آمار تعداد مشاهده مقاله: 61 تعداد دریافت فایل اصل مقاله: 12 |
||