Cloud computing virtual machine consolidation based on stock trading forecast techniques

In Cloud Computing, the virtual machine scheduling in datacenters becomes challenging when trying to optimize user-service requirements and, at the same time, efficient resource management. Clumsy load management results in host overloads that trigger a continuous flow of virtual machine (VM) migrations to correct this situation, thus negatively impacting the Service Level Agreement (SLA), resource availability and energy consumption. The present paper explores the combined use of trend analysis techniques with time series forecasting techniques broadly used in stock markets, to improve VM-to-host consolidation. The main goal is to provide an efficient estimate of the near future trend of virtual machine resource usage and host availability. This information improves the scheduler's decisions when determining the correct VM to be migrated and the candidate host to allocate it to. The results have demonstrated that it is possible to reduce the number of migrations by up to 75% while obtaining a reduction in the SLA violations by up to 60%. The results also showed noticeable improvements regarding the reduction of energy consumption. The migration decisions based on predictions of near-future resource usage trends using stock trading techniques showed a decrease in network usage, thus obtaining an energy saving of up to 16%.(c) 2023 Published by Elsevier B.V.