RESOURCE MANAGEMENT MODELS FOR ENSURING THE FUNCTIONAL SUSTAINABILITY OF THE DISTRIBUTED COMPUTING PROCESS
DOI:
https://doi.org/10.35546/kntu2078-4481.2023.4.28Keywords:
distributed computing, computer resources, models of distributed systems, functional stability, methods of distributing tasks by resources.Abstract
The article examines issues of increasing the efficiency of distributed data processing systems with support for the functional stability of the computing process. One of the main tasks that appears in the process of maintaining functional stability is the task of distributing tasks according to computing resources. The paper analyses modern models, methods and planners that perform the distribution of computational tasks according to distributed computer resources. Existing resource allocation methods typically use different criteria when selecting free resources to use. The main criteria are cost, execution time, percentage of resource utilization. Today, energy consumption, maintenance of functional stability and self-recovery time of systems are becoming important criteria. Resource management models for ensuring functional stability are proposed, namely: a modified set-theoretic model of a distributed computing process with support for functional stability, modified models for estimating execution time and energy consumption, a model of the process of supporting functional stability. The experiments described in the paper show that the use of the proposed models in standard methods of resource allocation, standard schedulers made it possible to reduce the time of computing tasks by 43%, and energy consumption by an average of 26%. The results of the research can be used in the development of new methods of resource allocation and technologies of distributed computing using the obtained models, which take into account the means of supporting the functional stability of the computing process.
References
I. Ruban, M. Volk, T. Filimonchuk, I. Ivanisenko, M. Risukhin, Y. Romanenkov. The Method for Ensuring the Survivability of Distributed Computing in Heterogeneous Computer Systems. 5th International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T), Kharkiv, Ukraine, October 9–12, 2018. pp. 1–7.
T. Filimonchuk, M. Volk, I. Ruban, V. Tkachov Development of information technology of tasks distribution for grid-systems using the GRASS simulation environment. Eastern-European Journal of Enterprise Technologies. Information and controlling system, 2016. Vol. 3/9 (81). pp. 45–53.
P. Wang, Y. Lei, P. R. Agbedanu, and Z. Zhang, “Makespandriven workflow scheduling in clouds using immunebased PSO algorithm,” IEEE Access, vol. 8, pp. 29281–29290, 2020.
N. Malik, M. Sardaraz, M. Tahir, B. Shah, G. Ali, and F. Moreira, “Energy-efficient load balancing algorithm for workflow scheduling in cloud data centers using queuing and thresholds,” Applied Sciences, vol. 11, no. 13, Article ID 5849, 2021.
P. Wang, Y. Lei, P. R. Agbedanu, and Z. Zhang, “Makespandriven workflow scheduling in clouds using immunebased PSO algorithm,” IEEE Access, vol. 8, pp. 29281–29290, 2020.
H. R. Faragardi,M. R. S. Sedghpour, S. Fazliahmadi, T. Fahringer, and N. Rasouli, “GRP-HEFT: a budgetconstrained resource provisioning scheme for workflow scheduling in IaaS clouds,” IEEE Transactions on Parallel and Distributed Systems, vol. 31, no. 6, pp. 1239–1254, 2020.
L. Zhang, L. Wang, Z. Wen, M. Xiao, and J. Man, “Minimizing energy consumption scheduling algorithm of workflows with cost budget constraint on heterogeneous cloud computing systems,” IEEE Access, vol. 8, pp. 205099–205110, 2020.
R. Anitha and C. Vidyaraj, “Workload and SLA violationprediction in cloud computing,” in 2019 Third International Conference on Inventive Systems and Control (ICISC), pp. 582–587, IEEE, Coimbatore, India, 2019.
Y. Hu, H. Wang, and W. Ma, “Intelligent cloud workflow management and scheduling method for big data applications,” Journal of Cloud Computing, vol. 9, Article ID 39, 2020.
Y. Cui and Z. Xiaoqing, “Workflow tasks scheduling optimization based on genetic algorithm in clouds,” in IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), pp. 6–10, IEEE, Chengdu, China, 2018.
M. N. Aktan and H. Bulut, “Metaheuristic task scheduling algorithms for cloud computing environments,” Concurrency and Computation: Practice and Experience, vol. 34, no. 9, Article ID e6513, 2022.
R. N. Talouki, M. H. Shirvani, and H. Motameni, “A heuristicbased task scheduling algorithm for scientific workflows in heterogeneous cloud computing platforms,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 8, Part A, pp. 4902–4913, 2022.
R. N. Talouki, M. H. Shirvani, and H. Motameni, “A hybrid meta-heuristic scheduler algorithm for optimization of workflow scheduling in cloud heterogeneous computing environment,” Journal of Engineering, Design and Technology, vol. 20, no. 6, pp. 1581–1605, 2022.
S. Mustafa, K. Bilal, S. U. R. Malik, and S. A. Madani, “SLA-aware energy efficient resource management for cloud environments,” IEEE Access, vol. 6, pp. 15004–15020, 2018.
WorkflowSim https://github.com/WorkflowSim/WorkflowSim-1.0
