Using Virtual Machine Size Recommendation Algorithms to Reduce Cloud Cost

Cloud spending has risen on a year-to-year basis, with the pandemic acting as primary catalyst for its recent growth; however, “cloud waste,” referring to cloud resources that are not used their full capacity, also follows this upward trend and causes loss of an increasingly large amount money. Unfortunately, present-day research lacks data-driven studies analyze why users wasting resources, or suggestions how lessen such waste. In order prevent over-expenditure, it is vital choose best-suited options when comes virtual machines (VM), especially small mid-sized businesses limited funds lack expertise. paper, we first analyzed 235 GB Azure user dataset from users’ perspective. We then implemented machine learning determine our pricing model VM costs. With these statistics, delineated methodology calculate wasted cost each VM, using data, propose algorithm can identify potential candidates wasteful VMs assist in reducing By applying approximately 2.7 million VMs, demonstrate ability help 66,721 created by 1,520 lower monthly costs $14.9 million. conclude businesses, while still reaping benefits services, do so at much lighter save VMs.