A model of joint dynamic offloading of cloud architecture with load balancing of layers

Abstract

Analysis of recent studies and publications. Recent research related to cloud systems of-fers computing paradigms for solving a large number of tasks of various types with minimal latency. The proposed paradigms can use resources, devices, nodes and clusters of the cloud center. But the main task of creating such paradigms is to solve the problem of offloading the cloud computing system using a combination of existing and new approaches. For example, when studying the cloud architecture, which is proposed in the form of a hierarchical boundary fog system [1], it is proposed to pay attention to the shift between the levels of the system according to the user's tolerance for latency. Many works of scientists have paid attention to the problem of using machine learning methods to solve the problems of offloading calculations and managing the mobility of the cloud system at different levels of its architecture. The current direction of cloud computing development is to solve the problem of load forecasting (proactive optimization) with the integration of intelligent agents into the system for monitoring, managing and adapting cloud service resources in real time, which interact with each other and central orchestrators for autonomous scaling and self-healing of the system [2].
To study a more complex structure of the cloud computing system, the author's idea of the work [3] was used regarding the proposed strategy of dynamic joint unloading of cloud edge devices taking into account load balancing.
Purpose of research. The purpose of the study is to consider a strategy for dynamic joint offloading of cloud devices, taking into account load balancing on edge servers and connection balancing on fog nodes for a multi-tier structure of a cloud computing system. Conducting experiments and analyzing the results. 
Presentation of the main research material. The author of the work [3] presented an ar-chitectural solution of a cloud system with the levels of end devices (DL), edge servers (EL) and cloud center (CL). In the proposed study, a fog level (FL) was added to approximate the model of the cloud computing system to the real one. A typical workflow of a cloud computing system is formed from several stages: users create tasks, end devices of the DL level distribute the received tasks to edge servers of the EL level through the main network under the control of the DL level offloading scheme, edge servers place the received tasks in a queue, on each edge server part of the tasks is processed locally, and the other part can be transferred to other edge servers for load balancing at the EL level; in parallel, tasks can be redirected due to the operation of the control scheme for selecting connection routers of the FL level; Part of the FL level tasks can also be redirected through the work of the cloud center for remote assistance under the control of the cloud offload scheme, after processing the tasks are delivered to the end devices to users from edge servers, fog nodes or from the cloud center.
Conclusions. As a result of the study, a model of the evolution of the load of a multi-tier system was obtained to analyze the impact of the selected strategies for solving the problem of unloading the system levels. A model of a multi-tier system was proposed that combines the principles of edge, fog and cloud computing design, in which, when devices at the system levels are overloaded, part of the tasks can be partially redistributed.
In the future, a hybrid system can be considered that includes a typical edge, fog and cloud computing system to study the issue of load optimization by transferring part of the tasks from edge servers and fog nodes directly to the cloud center or from edge servers, fog nodes, cloud center to the load control center.