Abstract
In the current paper a question of the resource-saving tasks distribution is under consideration. The problem of computational resource saving is topical because of the enormous data volumes, which are preprocessed partially by the fog- and edge- network layers. In general, scheduling and resource allocation are modeled via combinatorial optimization problems without consideration of the fact that the computational environment is geographically distributed. The consequence of such distribution is that the tasks assigned to some nodes have to transmit the data through some transit network sections. As the data transmission produces workload and consumes time, which degrade the average residual time of the nodes, in this paper we propose the novel problem model, which is structural-parametric and focuses not only on the functional tasks assignment to the nodes, but to the data transmission workload, which disseminates through the data transmission routes. The generic solution method is proposed on the base of multiplicative convolution and random search. The produced results show the positive effect of the workload distribution on the nodes reliability function values.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
D’Amato, A., Andrade, W.: A resource allocation model driven through QoC for distributed systems (2022). https://doi.org/10.5772/intechopen.106458
Zhijun, G., Gang, C.: Distributed dynamic event-triggered and practical predefined-time resource allocation in cyber–physical systems. Automatica 142, 110390 (2022). https://doi.org/10.1016/j.automatica.2022.110390
Junbin, L., Jie, Z., Victor, L., Xu, W.: Distributed information exchange with low latency for decision making in vehicular fog computing. IEEE Internet Things J. 9, 18166–18181 (2022). https://doi.org/10.1109/JIOT.2021.3075516
Kanika, S., Bernard, B., Jennings, B.: Graph-based heuristic solution for placing distributed video processing applications on moving vehicle clusters. IEEE Trans. Netw. Serv. Manag. 19, 1 (2022). https://doi.org/10.1109/TNSM.2022.3173913
Haghi Kashani, M., Mahdipour, E:. Load balancing algorithms in fog computing, 1–18 (2022). https://doi.org/10.1109/TSC.2022.3174475
Singh, P., et al.: A fog-cluster based load-balancing technique. Sustainability 14, 1–14 (2022). https://doi.org/10.3390/su14137961
Mahmoudi, Z., Darbanian, E., Nickray, M.: Optimal energy consumption and cost performance solution with delay constraints on fog computing. Jordanian J. Comput. Inf. Technol., 1 (2023). https://doi.org/10.5455/jjcit.71-1667637331
Yao, J., Ansari, N.: Fog resource provisioning in reliability-aware IoT networks. IEEE Internet Things J., 1 (2019). https://doi.org/10.1109/JIOT.2019.2922585
Klimenko, A.: The basic elements of devices resource consumption decreasing metodology for distributed systems on the basis of fog- and edge-computing. Proc. Southwest State Univ. 26, 151–167 (2023). https://doi.org/10.21869/2223-15602022-26-3-151-167
Melnik, E., Korovin, I., Klimenko, A.: Improving dependability of reconfigurable robotic control system. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds.) ICR 2017. LNCS (LNAI), vol. 10459, pp. 144–152. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66471-2_16
Korovin, I., Melnik, E., Klimenko, A.: The fog-computing based reliability enhancement in the robot swarm. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds.) ICR 2019. LNCS (LNAI), vol. 11659, pp. 161–169. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26118-4_16
Klimenko, A.B., Melnik, E.V.: A method of improving the reliability of the nodes containing ledger replicas. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds.) CoMeSySo 2021. LNNS, vol. 232, pp. 584–592. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-90318-3_47
Klimenko, A., Kalyaev, I.: A technique to provide an efficient system recovery in the fog- and edge-environments of robotic systems. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds.) ICR 2021. LNCS (LNAI), vol. 12998, pp. 100–112. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87725-5_9
Salem, A., Algaphari, G.: Resource allocation in fog computing: a systematic review. J. Sci. Technol. 27, 9–31. https://doi.org/10.20428/jst.v27i2.2052
Gong, C., He, W., Wang, T., Gani, A., Qi, H.: Dynamic resource allocation scheme for mobile edge computing. J. Supercomput. (2023). https://doi.org/10.1007/s11227-023-05323-y
Liu, Z., Lan, Q., Huang, K.: Resource allocation for multiuser edge inference with batching and early exiting. IEEE J. Sel. Areas Commun., 1 (2023). https://doi.org/10.1109/JSAC.2023.3242724
Murhekar, A., Arbour, D., Mai, T., Rao, A.: Dynamic vector bin packing for online resource allocation in the cloud (2023)
Klimenko, A.: Model and method of resource-saving tasks distribution for the fog robotics. In: Ronzhin, A., Meshcheryakov, R., Xiantong, Z. (eds.) ICR 2022. LNCS, vol. 13719, pp. 210–222. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-23609-9_19
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Klimenko, A., Barinov, A. (2023). Multicriteria Task Distribution Problem for Resource-Saving Data Processing. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2023. Lecture Notes in Computer Science, vol 14098. Springer, Cham. https://doi.org/10.1007/978-3-031-41673-6_13
Download citation
DOI: https://doi.org/10.1007/978-3-031-41673-6_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-41672-9
Online ISBN: 978-3-031-41673-6
eBook Packages: Computer ScienceComputer Science (R0)