COMPREHENSIVE METHOD OF ENERGY-EFFICIENT WORKLOAD PROCESSING IN THE INFORMATION AND COMMUNICATION NETWORK

Background. Peculiarities of the workload in a modern information and communication network (ICN) determine specific requirements for energy efficiency, performance and availability of its processing system. Existing approaches to increase energy efficiency and performance of workload processing do not take into account the possibility of dynamic changes in ICN workload arrival rate and individual energy consumption characteristics of computing nodes of the system. 
Objective. The purpose of the paper is to increase the energy efficiency and performance of ICN workload processing while meeting the requirements for the availability of the processing system, taking into account dynamic changes in the input workload arrival rate and the individual characteristics of the computing nodes’ energy consumption. 
Methods. A mathematical model of the workload processing system was built using the queueing theory methods, and an ontological model of this system was built using intelligent data analysis methods, which made it possible to quantitatively and qualitatively describe the complex relationships between system parameters and workload processing efficiency indicators. On the basis of the built models, a comprehensive method of energy-efficient workload processing was proposed, which differs from the known ones by the use of individual energy consumption models of computing nodes, combining the advantages of horizontal scaling and energy-efficient scheduling taking into account dynamic changes in workload arrival rate. 
Results. The efficiency of ICN workload processing is increased by 15.722% according to the efficiency criterion, which includes energy efficiency and performance indicators, compared to the known energy-efficient Backfill approach while meeting the requirements for the availability of the processing system. 
Conclusions. The energy efficiency, performance and availability of the ICN workload processing system can be improved by combining horizontal scaling and energy-efficient scheduling approaches using individual energy consumption models of computing nodes and taking into account dynamic changes in the input workload arrival rate.