End-edge-cloud collaborative computation offloading for multiple mobile users in heterogeneous edge-server environment
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End-edge-cloud collaborative computation offloading for multiple mobile users in heterogeneous edge-server environment Kai Peng1
•
Hualong Huang1 • Shaohua Wan2 • Victor C. M. Leung3
Ó Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract With the drastic development of Internet of things, the number of connected mobile users (MUs) is increasing at an unprecedented speed. The increasing popularity of MUs has triggered more and more new mobile applications. However, these applications are sensitive to latency, which inevitably increases pressure on MUs. Fortunately, computation offloading of mobile edge computing is becoming a promising technology that can improve quality of service for MUs. However, it becomes much difficult when there are multiple edge servers (ESs) near to the MU, even to the multiple MUs. On the other hand, as the resources of ESs are heterogeneous and finite, and thus it is challenge to design effective offloading strategies for multiple MUs. To tackle the above challenges, we firstly establish a multi-objective optimization model concerning time consumption and energy consumption of MUs, and resource utilization of ESs. Moreover, we devise an end-edge-cloud collaborative computing offloading method based on improved Strength Pareto Evolutionary Algorithm 2 for addressing this mode. Finally, compared to benchmark methods, numerous experiments have proved that our proposed method is effective and efficient and can be widely used for the scenario of multiple MUs and multiple heterogeneous ESs. Keywords Mobile edge computing Multi-ES Multi-MU Energy consumption Time consumption Resource utilization
1 Introduction Along with the drastic growth of Internet of things (IoT), the number of connected mobile users (MUs) has grown at an unprecedented rate [1–4]. Driven by 5G technology, it is predicted by the analysts of Gartner that the total number of MUs will reach 2.16 billion by 2020, working out to a rise of 0.9% compared to 2019. Meanwhile, with the increasing popularity of tablet computers, smart phones and wearable devices, MU has triggered more and more new mobile & Kai Peng [email protected] Shaohua Wan [email protected] 1
College of Engineering, Huaqiao University, Quanzhou, China
2
School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China
3
Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, Canada
applications, such as virtual reality, augmented reality, deep learning-based applications [5–9]. Additionally, these applications are also sensitive to latency, which puts higher demand on MUs [10, 11]. Although the processing capacity of MU has been greatly improved, executing applications on a MU still fails to provide satisfactory quality of experience(QoE) for MUs [12–16]. In order to tackle these challenges, mobile cloud computing (MCC) is proposed to improve processing capacity of MUs by offloading the compu
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