Mobility-aware computational offloading in mobile edge networks: a survey Sardar Khaliq uz Zaman 1 • Ali Imran Jehangiri 1 • Tahir Maqsood 2 • Zulfiqar Ahmad 1 • Arif Iqbal Umar 1 • Junaid Shuja 2,4 • Eisa Alanazi 3 • Waleed Alasmary 4 Received: 29 September 2020 / Revised: 5 February 2021 / Accepted: 14 March 2021 Ó The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Technological evolution of mobile devices, such as smart phones, laptops, wearable and other handheld devices have come up with the emergence of different user applications in learning, social networking, entertainment, and community computing domains. Many of such applications are fully or partially offloaded to the nearby server capable with high computing and storage resources. The delivery of task offloading results to the users is a challenge in those networks where the frequency of user mobility is high, leading to increased latency, higher energy consumption and inefficient resource utilization. In this paper, we survey the existing studies which optimize the task offloading in edge networks with mobility management. We formulate taxonomy of the research domain for classification of research works. We compare the listed state-of-the-art research works based on the components identified from taxonomy. Moreover, we debate future research directions for mobility, security, and scalability aware MEC offloading. Keywords Mobile Edge Computing (MEC) Á Mobility aware Á Computational offloading 1 Introduction With the advent of modern computing cores and memory architectures, today‘s mobile devices have become smart but still limited to computing and residual battery capacity. There are many computationally intensive applications such as 3D modeling, augmented/virtual reality (AR/VR), online games, ultra-HD image and video processing, arti- ficial intelligence and Internet of things (IoT) based applications which are resource hungry and generate tremendous amount of data [1][2]. Such applications may induce heavy amount of computational workload, leading to potential battery drainage in smart/mobile devices [1]. Other than limited battery power, memory, processing, and storage constraints also hinder the successful execution at mobile devices. The computationally intensive tasks can be offloaded to the proximate cloud and edge servers equipped with sufficient computing resources [3]. The concept of computation offloading or cyber-forag- ing was first introduced in Mobile Cloud Computing (MCC) [4, 5]. In computational offloading, mobile devices offload their computationally intensive tasks to the central cloud data centers. The mobile devices face a major chal- lenge while contending for computational offloading. The time and resources saved by mobile devices is sometimes offset by latency involved in discovering appropriate cloud server and migrate the workload. Several real-time mobile applications, such as online gaming, audio/video confer- encing, and financial transactions require high quality of service (QoS) and low latency. Failing to provide desired QoS adversely affects the user experience [3]. To reduce network delays and improve user experience compared to MCC, researchers proposed the idea of cloudlets. A cloudlet can be referred as small scale data center that is placed near to the users [6, 7]. A few fixed & Junaid Shuja skhaleeq@cuiatd.edu.pk 1 Department of Information Technology, Hazara University Mansehra, Mansehra, Pakistan 2 Department of Computer Science, COMSATS University Islamabad (CUI), Abbottabad Campus, Islamabad, Pakistan 3 Department of Computer Science, Umm Al-Qura University, Makkah, Saudi Arabia 4 Department of Computer Engineering, Umm Al-Qura University, Makkah, Saudi Arabia 123 Cluster Computing https://doi.org/10.1007/s10586-021-03268-6