Using Predictive Monitoring Models in Cloud Computing Systems

Kucherova, Kristina; Mescheryakov, Serg; Shchemelinin, Dmitry

doi:10.1007/978-3-319-99447-5_29

Cited by 4 publications

(4 citation statements)

References 1 publication

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“…According to experts on predictive analysis, the scope of its application is quite wide: from political campaigns to research in medicine, etc. [11]. At production enterprises predictive analytics can be applied for introducing logistics innovations in the process of working out of new products, upgrading of product lines, selection of contractors (suppliers, agents, logistic providers, potential consumers and others) and optimization of production process itself.…”

Section: Ivdiscussionmentioning

confidence: 99%

Predictive analytics in management of enterprise logistics innovations

Ivanova¹

2019

Proceedings of the International Conference on Digital Technologies in Logistics and Infrastructure (ICDTLI 2019)

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This research explores a modern approach for defining innovation. Reference has been taken, in particular from the latest publication of Oslo Manual, where innovation has been divided into two parts: product innovation and innovation of business processes. A combined option for the dissemination and exchange of information and data processing has been suggested. It proposes the building up of information flows drawn towards each other and their consolidation into a single information resource. Finally, an applicable program-targeted approach is suggested for management of logistics innovations in enterprises. The importance of predictive analytics within the framework of program-targeted approach for management of logistics innovations in enterprises has been shown. The reason is the change in use of information by the enterprises and emergence of a large amount of data which is to be processed for taking management decisions. Three aspects of invariance in application of predictive analytics have been defined, while managing logistics innovations in micro logistics systems: from the viewpoint of type of business; from the viewpoint of task setting and solution and from the viewpoint of the nature of logistics innovations. A conclusion has been made for the need of dividing all data into two types: internal and external, depending on their relation to the logistics system of any enterprise. The scheme has been drawn to exercise predictive analytics step by step in management of logistics innovations in enterprises with a program-targeted approach. The invariance in application of predictive analytics while managing logistics innovations in micro logistics systems has been proved and its necessity has been defined.

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Section: Ivdiscussionmentioning

confidence: 99%

Predictive analytics in management of enterprise logistics innovations

Ivanova¹

2019

Proceedings of the International Conference on Digital Technologies in Logistics and Infrastructure (ICDTLI 2019)

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“…For the purpose of capacity planning and performance evaluation, the monitoring system is needed. In this paper, Zabbix enterprise-class system is considered as one of the leading monitoring solutions in the world, having many built-in possibilities including [23][24][25] Zabbix supports a wide scope of built-in system metrics and triggers to monitor CPU, memory, disk space, etc., but business specific items should be implemented manually. Figures 4 and 5 show examples of such business items, measuring the incoming and outgoing user traffic at RingCentral and Genesys Companies accordingly [16,17].…”

Section: Monitoring System and Prediction Modelsmentioning

confidence: 99%

Digital Cloud Environment: Present Challenges and Future Forecast

et al. 2020

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This article addresses the challenges of a digital cloud environment when it comes to global scalability with a large number of remote servers and an unsecure public cloud, such as Amazon. The goal of the study was to work out an approach for evaluating the reasonable system capacity under heavy workload. For that purpose, Zabbix monitoring solution is used and business metrics are applied in relation to existing system ones. A prediction data model is proposed to compute the future forecast of the user activity based on the collected historical statistics and to verify whether capacity adjustment is possible or not. The results of capacity planning are implemented at Genesys International Telecommunications Company. System analysis of the production environment indicates the possibility to downscale the capacity of certain virtual servers, which allowed savings to the annual operational costs of $3500 (50%) for each affected server.

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“…На рисунке 2 приведен пример системной метрики мониторинга свободного места на дисках серверов БД [11], а на рисунке 3 показана логика соблюдения соотношения исторического периода и горизонта прогнозирования для своевременного реагирования на окончание свободного места на диске [12].…”

Section: описание мониторинга системных метрикunclassified

Prediction of Cloud Computing Resources Based on the Open Source Monitoring System

Kucherova¹

2020

PTU

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The paper describes the universal approach for monitoring the data storage of a globally distributed cloud computing system, which allows you to automate creation of new metrics in the system and predict their behavior for the end users. Since the existing monitoring software products provide built-in scheme only for system metrics like RAM, CPU, disk drives, network traffic, but don’t offer solutions for business functions, IT companies have to design specialized database structure (DB). The data structure proposed in this paper for storing the monitoring statistics is universal and allows you to save resources when orginizing database monitoring on the scale of the GDCCS. The goal of the research is to develop a universal model for monitoring and forecasting of data storage in a globally distributed cloud computing system and its adequacy to real operating conditions.

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Using Predictive Monitoring Models in Cloud Computing Systems

Cited by 4 publications

References 1 publication

Predictive analytics in management of enterprise logistics innovations

Predictive analytics in management of enterprise logistics innovations

Digital Cloud Environment: Present Challenges and Future Forecast

Prediction of Cloud Computing Resources Based on the Open Source Monitoring System

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