Auto Scaling in Mobile Edge Computing to Reduce Response Time and Operational Costs

Internet of Things (IoT) technology is rapidly advancing and evolving. Currently, a wide range of stakeholders are closely studying how to distribute the computational infrastructure and mobile edge computing networks that are activated by cloud computing services. The mobile edge computing (MEC) environment is rapidly becoming a suitable framework for IoT-related infrastructures and mobile devices that include real-time applications. However, with the increasing growth of devices and the need to offload tasks to edge servers in the MEC environment, there has been an increase in task response time, and even some tasks are not executed due to the expiration of their deadline. Given that the main reason for the emergence of the MEC environment is to reduce task response time compared to other cloud environments, the issue of increasing response time in this environment is unacceptable. Also, Considering that edge servers have limited processing capacity compared to other cloud environments, edge server resources must be used optimally, so reducing operational costs must also be on the agenda. Therefore, the main objective of this paper is to reduce task response time and operational costs in the MEC environment. To achieve this goal, In this paper, we propose an automatic scaling method using LSTM prediction algorithm for optimal allocation of resources to tasks. so that the proposed approach can effectively and timely maintain an appropriate number of instances in response to changing dynamic traffic, while minimizing response time and also reducing operational execution costs. The present research is descriptive-analytical and relies on library resources.