Use of regional computing to minimize the social big data effects

Smart devices are commonly used these days, especially in smart cities, resulting in massive social media engagement and heavy workload generation. Statistics show that over 4.41 billion people will subscribe to social media by 2025, which covers the majority of the world's population. Its versatility and enriched features allow users to upload and download large data (e.g, High Definition (HD)) videos and HD live streaming). This heavy workload overburdens the mainstream network and social media cloud, increasing the delay and costs for instant communications. To cope with the aforementioned challenges, this paper aims to minimize the social big data effects on the mainstream network and the social media cloud servers. In connection with these objectives, a survey result shows that 75% of social connections originate from the local region, and their data has no need for instant migration to the remote cloud servers. We extended the Edge/Fog computing concept to create Regional Computing (RC) for Social Media Platforms (SMP). These servers are created at the regional level. Initially, the data is stored and processed at regional computing servers and later on, in off-peak hours, migrated to the cloud servers. The initial result shows that the regional computing servers filter the content regionally and minimize the burden on the mainstream network. It also reduces the cloud server's workload, resulting in minimal delays and costs.

Automated summary

The widespread use of smart devices has led to an increase in social media engagement and workload, placing a heavy burden on mainstream networks and social media cloud servers. To address this challenge, this paper introduces the concept of Regional Computing (RC) for Social Media Platforms (SMP), which involves processing and storing data at regional computing servers before migrating to cloud servers during off-peak hours. Preliminary results show that RC effectively filters content regionally, reducing the burden on mainstream networks and minimizing delays and costs for instant communication.