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Abstract

The organization of modern cloud services is based on theoretical results in logistics, operations research, supply chains, information transmission (transportation) networks, and on the practical achievements of the novel information and communication technologies. As all the inhabitants of the planet become regular users and at the same time creators of such services, the issues of decentralized decision making are becoming everyday problems. The paper presents the setup for the problem of such solutions by suppliers (providers) of cloud services and suggests a mathematical formulation of the corresponding optimization problem with resource constraints. It is a starting point for further mathematical elaboration of the new everyday problems.

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If the first computer networks represented mainly research projects for military and academic purposes (for double-purpose), the current networks represent rather competitive business structures with the highest capitalization ever. Apple is the first company in the world which achieved capitalization trillion dollars. Two years later, in 2020, Apple doubled its capitalization. Its corporate capitalization exceeds the gross domestic product of any country in the world except the United States, China, Japan, Germany, India, and the United Kingdom. The Internet-related industries finally become the true production factors and engines of human progress. Those industries need qualified microeconomic analysis, including the analysis of human capital development, with financial macroeconomic implications.
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Internet of Things (IoT) is producing an extraordinary volume of data daily, and it is possible that the data may become useless while on its way to the cloud, due to long distances. Fog/edge computing is a new model for analysing and acting on time-sensitive data, adjacent to where it is produced. Further, cloud services provided by large companies such as Google, can also be localised to improve response time and service agility. This is accomplished through deploying small-scale datacentres in various locations, where needed in proximity of users; and connected to a centralised cloud that establish a multi-access edge computing (MEC). The MEC setup involves three parties, i.e. service-providers (IaaS), application-providers (SaaS), network-providers (NaaS); which might have different goals, therefore, making resource management difficult. Unlike existing literature, we consider resource management with-respect-to all parties; and suggest game-theoretic resource management techniques to minimise infrastructure energy consumption and costs while ensuring applications' performance. Our empirical evaluation, using Google's workload traces, suggests that our approach could reduce up to 11.95% energy consumption, and ~17.86% user costs with negligible loss in performance. Moreover, IaaS can reduce up-to 20.27% energy bills and NaaS can increase their costs-savings up-to 18.52% as compared to other methods.
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