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Evaluation of probabilistic-temporal characteristics of a computer system with container virtualization

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The dependence of request servicing delay on the number of deployed containers is investigated for computer systems with container virtualization. The sought-after dependency is due to the allocation of limited computational resources of the computer system between active and inactive containers loaded in the system. The conducted research proposes a comprehensive combination of analytical queuing model, simulation modeling, and natural experiments. The studied computer system is interpreted as a multi-channel queuing system with an unlimited queue. The peculiarity of the proposed approach is the study of the influence of the number of containers formed in the system on queue delays and request servicing rate. Each container is associated with a service channel, and for the operation of a container in active and inactive states, the use of part of the common resources of the computing system is required. When constructing the model, it is assumed that the input flow is simple, and the service is exponential. The service rate depends on the number of deployed containers and the number of requests in the system. The experimental dependence of service rate on the number of active containers has been established. The experimental study was carried out on a platform based on Proxmox virtualization technology with fixed resources. To study the influence of the number of active containers on service rate within the experiment, a single-threaded web server was deployed in the form of several containers managed using the portable extensible Kubernetes k3s platform. The results of calculations using the analytical model are confirmed by the results of simulation modeling implemented using the SimPy modeling library in the Python programming language. Based on the conducted research, the need to solve the optimization problem of the number of deployable containers in a computer system regarding the influence of this number on request servicing delays is shown. The conducted research can find application in the design of real-time cluster systems critical to acceptable wait service delays, ensuring the continuity of the computational process, and preserving unique data accumulated during the system operation. The proposed approaches can be applied in the creation of fault-tolerant distributed computer systems, including those operating with failure accumulation and system reconfiguration with load (request) redistribution during dynamic container migration and replication.

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