Optimizing the Energy Consumption of On-site Private Cloud Computing Platforms

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Abstract
Cloud computing offers advantages such as self-service and dynamic redistribution of resource structures, necessitating the development of effective algorithms for optimal use of hardware infrastructure. Recent studies highlight the problems of resource redistribution in on-site private cloud computing platforms software layers, affecting total energy consumption. Existing approaches to mathematical modeling use combinatorial optimization, game theory, and artificial intelligence but lack an integrated approach to energy consumption optimization. The proposed approach involves creating an original algorithm based on a multi-level architecture developed by the author. This architecture divides objects into seven functional layers, from hardware physical infrastructure to application SaaS layer. The algorithm aims to minimize electricity consumption by isolating critical parameters in the process of redistributing resource structures. The optimization problem is segmented into subproblems, allowing for efficient load placement and energy savings. The model is focused on optimizing the power consumption of on-site private cloud computing platforms using a well-known approach – reducing the number of hardware nodes involved. The idea is to switch off not only individual computing nodes but also cooling nodes when idle. The mathematical formulation of the resource allocation in on-site private cloud computing platforms problem is presented as a Multi-dimensional Bin packing. The algorithm employs dynamic programming to maximize load placement density and reduce the number of hardware nodes. The model provides support technologies for both virtual machines and software containers. The paper presents the original author's algorithm, Optimization of Programmable Infrastructure Resources (OPIR), focused on reducing electricity consumption through combinatorial optimization.

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