Abstract
Mobile Edge Computing (MEC) has emerged as an alternative to cloud computing to meet the latency and Quality-of-Service (QoS) requirements of mobile devices. In this paper, we address the problem of server resource allocation in MEC. Due to the dynamic load conditions on MEC servers, their resources need to be used intelligently to meet the QoS requirements of the users and to minimize server energy consumption. We present a novel resource allocation algorithm, called Power Migration Expand ( PowMigExpand ). Our algorithm assigns user requests to the optimal server and allocates optimal amount of resources to User Equipment (UE) based on our comprehensive utility function. PowMigExpand also migrates UE requests to new servers, when needed due to the mobility of users. We also present a low cost Energy Efficient Smart Allocator (EESA) algorithm that uses deep learning for energy efficient allocation of requests to optimal servers. The proposed algorithms consider varying load of incoming requests and their heterogeneous nature, energy efficient activation of servers, and Virtual Machine (VM) migration for smart resource allocation and, thus, is the first comprehensive approach to address the complex and multidimensional resource allocation problem using deep learning. We compare our proposed algorithms with other resource allocation approaches and show that our approach can handle the dynamic load conditions better. The proposed algorithms improve the service rate and the overall utility with minimum energy consumption. On average, it reduces 26% energy consumption of MESs and improves the service rate by 23%, compared with other algorithms. We also get more than 70% accuracy for EESA in allocating the resources of multiple servers to multiple users.
Highlights
As opposed to traditional computers, mobile devices process only a handful of tasks, and have certain inherent limitations, such as low processing power, memory and battery life
We present an energy efficient resource allocation algorithm, called Power Migration Expand (PowMigExpand), which considers the mobility of User Equipment (UE) and migrates the Virtual Machine (VM) of UEs from one server to another, when required, with high utility for Mobile Edge Server (MES)
The resource allocation problem in Mobile Edge Computing (MEC) is of great importance
Summary
As opposed to traditional computers, mobile devices process only a handful of tasks, and have certain inherent limitations, such as low processing power, memory and battery life. The authors in [32] solve the resource allocation problem using cooperative game theory They do not consider the mobility of UEs and energy consumption for MESs. The authors in [31]–[33] only consider the CPU and time as resources requested by UEs and the utility functions do not depend upon the realistic resources such as CPU, RAM, and disk space. The objective of this paper is to maximize the server utility and the UE service rate, while minimizing the MES energy consumption, in a realistic multi-user multi-server scenario considering UE mobility. The central control unit finds the optimal server for each incoming UE request and allots MES resources to it For this purpose, we propose a mobility-aware utility function that considers the amount of resources requested, time, and distance between the UE and MES.
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