Security modeling and efficient computation offloading for service workflow in mobile edge computing

TL;DR

This paper introduces SEECO, a strategy for secure and energy-efficient computation offloading in mobile edge computing, optimizing energy use while considering security risks and deadlines.

Contribution

It formulates a novel offloading problem incorporating security, energy, and timing, and devises a genetic algorithm-based solution for workflow task allocation.

Findings

SEECO reduces energy consumption in MEC offloading.

The strategy enhances security while maintaining QoS.

Experimental results validate effectiveness across various workflows.

Abstract

It is a big challenge for resource-limited mobile devices (MDs) to execute various complex and energy-consumed mobile applications. Fortunately, as a novel computing paradigm, edge computing (MEC) can provide abundant computing resources to execute all or parts of the tasks of MDs and thereby can greatly reduce the energy of MD and improve the QoS of applications. However, offloading workflow tasks to the MEC servers are liable to external security threats (e.g., snooping, alteration). In this paper, we propose a security and energy efficient computation offloading (SEECO) strategy for service workflows in MEC environment, the goal of which is to optimize the energy consumption under the risk probability and deadline constraints. First, we build a security overhead model to measure the execution time of security services. Then, we formulate the computation offloading problem by incorporating the security, energy consumption and execution time of workflow application. Finally, based on the genetic algorithm (GA), the corresponding coding strategies of SEECO are devised by considering tasks execution order and location and security services selection. Extensive experiments with the variety of workflow parameters demonstrate that SEECO strategy can achieve the security and energy efficiency for the mobile applications.