Abstract
Mobile edge computing is a new cloud computing paradigm that utilizes small-sized edge clouds to provide real-time services to users. These mobile edge clouds (MECs) are located near users, thereby enabling users to seamlessly access applications that are running on MECs and to easily access MECs. Terminal devices can transfer tasks to MEC servers nearby to improve the quality of computing. In this paper, we study multi-user computation offloading problem for mobile-edge computing in a multichannel wireless interference environment. Then, we analyze the overhead of each mobile devices, and we propose strategies for task scheduling and offloading in a multi-user MEC system. For reducing the energy consumption, we propose a server partitioning algorithm that is based on clustering. We formulate the task offloading decision problem as a multi-user game, which always has a Nash equilibrium. The simulation results demonstrate that our scheme outperforms the traditional offloading strategy in terms of energy consumption.
Highlights
The growing popularity of mobile devices, such as smart phones, tablet computers and wearable devices, is accelerating the advent of the Internet of things (IoT) and triggering a revolution of mobile applications [1]
As functions of cloud computing increasingly move to the edge of the network, a new trend of computing has emerged: It is estimated that tens of billions of edge computing devices will be deployed on the edge of the network [10]
In the decision-making process, if a mobile device is in a low-battery state, to conserve more energy, it would choose a larger value of λl and put more weight on energy consumption
Summary
The growing popularity of mobile devices, such as smart phones, tablet computers and wearable devices, is accelerating the advent of the Internet of things (IoT) and triggering a revolution of mobile applications [1]. As functions of cloud computing increasingly move to the edge of the network, a new trend of computing has emerged: It is estimated that tens of billions of edge computing devices will be deployed on the edge of the network [10] In this new environment, we must manage, process, and store the large amounts of data that are generated at the edge of the network [11]. Due to its dense geographical distribution, proximity to users, support for high mobility, and open platforms, MEC can support applications and services with reduced latency and improved QOS [15]. It is becoming an important enabler of user-centric IoT applications and services that demand real-time operations
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