Multiuser Offloading with Cloud Server Data

TL;DR

This paper addresses multiuser computation offloading in mobile edge computing, optimizing energy consumption when users access local and cloud data, by formulating and solving binary integer linear programming problems.

Contribution

It introduces a novel optimization framework for energy-efficient offloading considering both local and cloud data in MEC environments.

Findings

Effective energy minimization achieved

Optimization problem solved via binary integer linear programming

Applicable to scenarios with local and cloud data access

Abstract

Computation offloading becomes useful for users of limited computing power and mobile edge computing (MEC) can help mobile users perform their tasks more effectively. In this paper, we consider MEC when users perform tasks with local data as well as data at cloud storage servers, since users can often keep their data at cloud storage servers or have tasks that need to use public datasets. An optimization problem to minimize the total consumed energy of users as well as a base station (BS) is formulated with a total transmission time constraint, which is solved by converting into multiple binary integer linear programming problems.