Abstract
Mobile edge computing, a promising paradigm, brings services closer to a user by leveraging the available resources in an edge network. The crux of MEC is to reasonably allocate resources to satisfy the computing requirements of each node in the network. In this paper, we investigate the service migration problem of the offloading scheme in a power‐constrained network consisting of multiple mobile users and fixed edge servers. We propose an affinity propagation‐based clustering‐assisted offloading scheme by taking into account the users’ mobility prediction and sociality association between mobile users and edge servers. The clustering results provide the candidate edge servers, which greatly reduces the complexity of observing all edge servers and decreases the rate of service migration. Besides, the available resource of candidate edge servers and the channel conditions are considered to optimize the offloading scheme to guarantee the quality of service. Numerical simulation results demonstrate that our offloading strategy can enhance the data processing capability of power‐constrained networks and reach computing load balance.
Highlights
The arrival and evolution of the 5G delivers a transformative solution to an ultimate high-quality end-user experience
If users move far away from the mobile edge computing (MEC) server that is responding to their request, this could result in significant quality of service (QoS) and quality of experience (QoE) degradation and service interruption due to long transmission latency of the offloaded task [8]
By using the mobility prediction and the social association results as input parameters of an affinity propagation algorithm, we propose a mobility-aware and sociality-associate clustering algorithm (MASACA), where MEC servers are divided into candidate sets associated with different users
Summary
The arrival and evolution of the 5G delivers a transformative solution to an ultimate high-quality end-user experience. The numerous applications require high-speed internet connectivity and high computation power, which is not possible in a mobile device with limited memory and storage capacity [1,2,3] In such situations, it is feasible to transfer resource-intensive tasks to external platforms like cloud, grid, and edge servers. Without considering users’ trajectory data and QoS utility, the accuracy of edge server selection and the efficiency of service migration decrease. We employ a Kalman filtering algorithm to do mobility prediction of users’ trajectory; we compute the sociality association between mobile users and edge servers by using the history connection relationship.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
