A Dynamic Scaling Methodology for Improving Performance of Data-Intensive Systems

The continuous growth of data volume in various fields such as healthcare, sciences, economics, and business has caused an overwhelming flow of data in the last decade. The overwhelming flow of data has raised challenges in processing, analyzing, and storing data, which lead many systems to face an issue in performance. Poor performance of systems, such as slow processing speed, creates negative impact such as delays, unprocessed data, and increasing response time. This paper presents a novel dynamic scaling methodology to improve the performance of data-intensive systems. The dynamic scaling methodology is developed to scale up the system based on the several aspects from the data-intensive perspective. Moreover, these aspects are used by the helper project algorithm which is designed to divide a task into small pieces to be processed by the system. These small pieces run on several virtual machines to work in parallel to enhance the system’s runtime performance. In addition, the dynamic scaling methodology does not require many modifications on the applied, which makes it easy to use.