A modified method of self-recovery of distributed software in heterogeneous computer systems
DOI:
https://doi.org/10.30837/ITSSI.2024.27.005Keywords:
self-healing methods; software; distributed computing; computer systems; cloud architectures; software agents.Abstract
The object of research is the distributed computing process in heterogeneous computer systems. The subject of the research is methods of self-healing for distributed software on heterogeneous computer systems. The goal is to increase the efficiency of distributed data processing systems with support for the functional stability of the computing process by developing a modified method of self-healing of distributed software. Tasks: to investigate the existing methods of restoring the distributed computing process, to draw conclusions about their advantages and disadvantages; on the basis of mathematical models of tasks, computing resources and existing methods of resource allocation, develop a modification of the method of self-recovery of distributed software taking into account management strategies, finding the best solution for the selected criteria, reducing energy consumption during the execution of tasks; conduct a number of experiments comparing the developed method with existing ones. Research methods are based on the use of set theory, general systems theory, and simulation modeling theory. The results of the experiments obtained during the simulation of the allocation of software tasks to computing resources in a simulated simulation environment and the simulation of the computing process during self-recovery in case of resource failures confirm the effectiveness of the proposed method. Conclusion: the application of the method in distributed computing control systems does not increase the time the system spends on performing the task in the absence of failures, at the same time, in the presence of failures, it allows to restore the functionality of the software task faster and reduces the execution time by 8–17%, and energy consumption by 7–12%. There is also an increase in efficiency with an increase in the size of the tasks and the probability of failures. The development of technologies for automated or automatic use of methods of resource allocation and self-recovery can be indicated as areas for future research.
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