A resource pre-allocation method for cognitive analytics-based social media service in edge computing.

With the rapid development of 5G mobile communication technology, the growing demand for media services in society is becoming a characteristic of smart cities. Technical study has exhaustively investigated the low-latency resource supply of edge computing systems for web hosting services. However, external variables (such as transmission and network delays) impact the transmission between edge servers and service requests, resulting in service request delays. In addition, resource service requests that are constantly updated and in dynamic distribution may overload some servers while others are idle, resulting in poor load balancing. Consequently, this research presents a Resource Pre-Allocation method (RPA) for cognitive analytics-based social media platforms, which aims to improve the load balancing of edge servers while serving requests with strict latency requirements, so as to obtain the optimal resource allocation strategy. First, the resource requirement prediction algorithm is developed based on temporal-spatial demand history. Then, we propose a multi-objective algorithm combined with the optimal solution selection techniques to obtain the ideal resource allocation decisions. Finally, the performance of RPA is tested and evaluated. The experimental results show that RPA can allocate resources better than other methods. [ABSTRACT FROM AUTHOR]