Methods and analytical conditions for adapting the provision of resources to users of cloud computing

• Datum: 2020
• 1. Verfasser: Матов, О. Я.
• Format: Artikel
• Sprache: Ukrainian
• Veröffentlicht: Інститут проблем реєстрації інформації НАН України 2020
• Schlagworte: cloud computing; mathematical model; discipline of computing resources provision; mixed service discipline; absolute andrelative priorities; time characteristics; response time; efficiency of cloud computing
• Online Zugang: http://drsp.ipri.kiev.ua/article/view/225913
• Назва журналу: Data Recording, Storage & Processing

It is considered the issues of further development of the principles of creating adaptive infrastructures of cloud computing, capable of dynamically adapting to user requirements and current features and changes in operating conditions. Methods and analytical conditions for adapting the provision of resources to users of cloud computing have been developed. These conditions provide an opportunity to develop technology (mechanisms and algorithms) for the use of adaptive discipline (order) of providing computing resources to users. In turn, this allows you to meet the time requirements of different users to obtain timely computational results or make the most efficient use of available cloud computing resources. This is relevant for real-time systems and, above all, for special information systems built using private clouds, and can be critical with limited computing resources of cloud computing. Analytical (formulaic) conditions of adaptation are developed on the basis of the corresponding indicators of efficiency and mathematical models of cloud calculations. The stochastic nature of the main factors and the need to quantify mass processes based on probability theory determines the use of the analytical model of cloud computing as a multi-threaded and multi-priority queuing system with queues with mixed service discipline. The model utilizes probable failures and various features and has arbitrary distribution laws for some probable processes. The model allows to calculate the time characteristic — the response time of the system in terms of features of operation and failures of cloud computing. Refs: 9 titles.