Abstract
With the development of industrial intelligence, the resource requests of various social media services in smart cities are expanding rapidly. For hosting services, the edge computing (EC) platform for its low-latency resource provisioning is fully explored. However, the mapping between edge servers (ESs) and services affects the service latency. Meanwhile, the real-time dynamic distribution of resource requirements also impairs the load balance. Therefore, how to optimize the load balance of ESs while meeting the latency-critical requests remains challenging. To deal with the above challenge, in this article, we propose a resource pre-allocation (RPA) method for the social media services with cognitive analytics. Technically, the deep spatiotemporal residual network (ST-ResNet) is employed to complete the cognitive analytics of resource requests. Then based on the analysis results, the optimal resource allocation (ORA) scheme is designed with multiobjective optimization. Finally, the performance of RPA is evaluated by a real-world resource request data set.
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.
