ntvНаучно-технический вестник информационных технологий, механики и оптикиScientific and Technical Journal of Information Technologies, Mechanics and Optics2226-14942500-0373Университет ИТМО10.17586/2226-1494-2023-23-3-608-617ntv-37Research ArticleМАТЕМАТИЧЕСКОЕ И КОМПЬЮТЕРНОЕ МОДЕЛИРОВАНИЕMODELING AND SIMULATIONОценка готовности компьютерной системы к своевременному обслуживанию запросов при его совмещении с информационным восстановлением памяти после отказовAssessment of the readiness of a computer system for timely servicing of requests when combined with information recovery of memory after failureshttps://orcid.org/0000-0003-0213-0223БогатыревВ. А.BogatyrevV. A.

Богатырев Владимир Анатольевич — доктор технических наук, профессор

sc 7006571069

Санкт-Петербург, 197101;  Санкт-Петербург, 190000

Vladimir A. Bogatyrev — D.Sc., Professor 

sc 7006571069 

Saint Petersburg, 197101; Saint Petersburg, 190000

vladimir.bogatyrev@gmail.comhttps://orcid.org/0000-0003-0836-8515БогатыревС. В.BogatyrevS. V.

Богатырев Станислав Владимирович — аспирант; инженер-консультант

sc 57183002200

Санкт-Петербург, 197101 ; Санкт-Петербург, 195027 

Stanislav V. Bogatyrev — PhD Student; Consulting Engineer 

sc 57183002200 

Saint Petersburg, 197101; Saint Petersburg, 195027 

realloc@gmail.comhttps://orcid.org/0000-0001-5447-7275БогатыревА. В.BogatyrevA. V.

Богатырев Анатолий Владимирович — кандидат технических наук, инженер-консультант

sc 56549712700 

Санкт-Петербург, 195027 

Anatoly V. Bogatyrev — PhD, Consulting Engineer 

sc 56549712700 

Saint Petersburg, 195027 

gangleon@gmail.comУниверситет ИТМО ; Санкт-Петербургский государственный университет аэрокосмического приборостроенияРоссияITMO University ; Saint Petersburg State University of Aerospace InstrumentationRussian FederationУниверситет ИТМО ; ООО «Центр разработки облачных хранилищ Ядро»РоссияITMO University ; Yadro Cloud Storage Development CenterRussian FederationООО «Центр разработки облачных хранилищ Ядро»РоссияYadro Cloud Storage Development CenterRussian Federation202311122024233608617Copyright © Богатырев В.А., Богатырев С.В., Богатырев А.В., 20242024Богатырев В.А., Богатырев С.В., Богатырев А.В.Bogatyrev V.A., Bogatyrev S.V., Bogatyrev A.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ntv.elpub.ru/jour/article/view/37

Введение. Исследованы возможности повышения готовности резервированной компьютерной системы к своевременному выполнению запросов, критичных к задержкам обслуживания. Рассмотрен отказоустойчивый компьютерный кластер, в котором узлы представляют собой дублированные вычислительные системы, объединяющие компьютерные узлы и узлы памяти. Предположено двухэтапное восстановление узлов памяти: физическое, а затем информационное, осуществляемое с использованием ресурсов вычислительных узлов. Метод. Предложены обоснования выбора дисциплин восстановления и обслуживания потока функциональных запросов на основе марковских моделей. Предложенные модели позволяют учесть влияние разделения вычислительных ресурсов на совместное выполнение требуемых функций и на информационное восстановление памяти, реализуемое после ее физического восстановления. Выбор дисциплин обслуживания компьютерной системы направлен на достижение компромисса между стремлением увеличить коэффициент готовности и вероятностью своевременного выполнения поступающего потока функциональных запросов. Рассмотрено доказательство выбора вариантов распределения (разделения) сохраненных после отказов вычислительных ресурсов на решение функциональных запросов (требуемых функций) и информационное восстановление памяти. Основные результаты. На основе предложенных марковских моделей исследована зависимость готовности системы к своевременному выполнению запросов от вариантов распределения сохраненных в системе вычислительных ресурсов на восстановление информации в памяти и выполнение функциональных задач. Исследование проведено в зависимости от допустимого времени ожидания функциональных запросов и интенсивности трафика. Проанализировано влияние готовности системы к своевременному выполнению запросов балансировки трафика функциональных задач между работоспособными вычислительными узлами. Показано существование оптимальной доли распределения трафика между вычислительными узлами. Обсуждение. Полученные результаты могут быть использованы при обосновании выбора дисциплин обслуживания функциональных запросов и восстановления после отказов отказоустойчивых кластерных систем, критичных к задержкам выполнения функциональных запросов.

The possibilities of increasing the readiness of a redundant computer system for the timely execution of requests critical to service delays are being investigated. A fault-tolerant computer cluster is considered in which nodes are duplicated computing systems that combine computer nodes and memory nodes. Two-stage recovery of memory nodes is assumed: first physical, and then informational, carried out using the resources of computing nodes. The novelty of the approach lies in the fact that for systems with a limitation of the allowable service time of functional requests, the impact of recovery disciplines on the readiness of the system with various options for dividing computing resources to restore information after memory failures and to perform the required functions is evaluated. At the same time, the reliability of the computer systems under study is assessed not only by the probability of their readiness to perform functional tasks (by the readiness coefficient), but also by the probability of the system readiness to perform tasks in a timely manner. Justification of the choice of disciplines for the restoration and maintenance of the flow of functional requests is carried out on the basis of Markov models. At the same time, models are proposed that allow taking into account the impact of the division of computing resources on the joint performance of the required functions and on the information recovery of memory, implemented after its physical recovery. The choice of computer system maintenance disciplines based on the proposed Markov model is aimed at achieving a compromise between the desire to increase the availability factor and the probability of timely execution of the incoming flow of functional requests. The justification of the choice of options for the distribution (separation) of computing resources stored after failures to solve functional queries (required functions) and information recovery of memory, implemented after its physical recovery, is carried out. Based on the proposed Markov models, the dependence of the system readiness for timely execution of requests on the distribution options of computing resources stored in the system for restoring information in memory and for performing functional tasks is investigated. The study was conducted depending on the allowable waiting time for functional requests and the intensity of their traffic. The influence on the system readiness for timely execution of traffic balancing requests of functional tasks between functional computing nodes is analyzed, taking into account the options for their possible joint use for information recovery of memory nodes after their physical recovery. The existence of an optimal share of traffic distribution between computing nodes is shown, taking into account the options for dividing their resources to service functional requests and to restore information in memory nodes after their physical recovery. The results obtained can be used to justify the choice of disciplines for servicing functional requests and recovery after failures of fault-tolerant cluster systems critical to delays in the execution of functional requests.

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The authors declare that there are no conflicts of interest present.