Energy-Aware Adaptive Offloading of Soft Real-Time Jobs in Mobile Edge Clouds

TL;DR

This paper introduces a model and framework for adaptive offloading of soft real-time jobs in mobile edge clouds, optimizing energy and execution time by dynamically adjusting offloading strategies based on runtime feedback.

Contribution

It presents a novel model and a modular framework for adaptive offloading in hybrid edge clouds, enabling dynamic decision-making based on real-time system metrics.

Findings

Runtime-aware strategies improve energy efficiency.

Dynamic offloading reduces execution time.

Adaptive strategies better meet job deadlines.

Abstract

We present a model for measuring the impact of offloading soft real-time jobs over multi-tier cloud infrastructures. The jobs originate in mobile devices and offloading strategies may choose to execute them locally, in neighbouring devices, in cloudlets or in infrastructure cloud servers. Within this specification, we put forward several such offloading strategies characterised by their differential use of the cloud tiers with the goal of optimizing execution time and/or energy consumption. We implement an instance of the model using Jay, a software framework for adaptive computation offloading in hybrid edge clouds. The framework is modular and allows the model and the offloading strategies to be seamlessly implemented while providing the tools to make informed runtime offloading decisions based on system feedback, namely through a built-in system profiler that gathers runtime information such as workload, energy consumption and available bandwidth for every participating device or server. The results show that offloading strategies sensitive to runtime conditions can effectively and dynamically adjust their offloading decisions to produce significant gains in terms of their target optimization functions, namely, execution time, energy consumption and fulfillment of job deadlines.