Virtual Machine Placement (VMP) is crucial in a cloud data cen-ter(CDC). It is a critical step carried out as part of the Virtual Machine (VM) placement to allocate the best Physical Machine (PM) to host the VMs. The efficacy of the virtual machine placement strategy has a considerable impact on cloud computing efficiency. The ineffec-tiveness of the VMP approach has a major negative impact on the CDC.Virtualization facilitated VM migration has met the ever-increasing demands of dynamic workload by transferring VMs inside CDC. Many resource management goals, including power efficiency, load balancing, fault tolerance, and system maintenance, are aided VM placement. As a result, VMP needs to assess characteristics that may impact placement performance and energy efficiency. Most past research has concentrated solely on reducing energy consumption while ignoring SLA (service level agreement) breaches, enhancing the resource usage of PMs, and ignoring the over-commitment issue. MOM-VMP To propose a multiobjective Mayfly VMP algorithm (MOM-VMP) meta-heuristic optimization algorithm with a massive CDC with different and multi-dimensional resources to handle these issues. A multi-objective dynamic VMP strategy is employed to reduce resource wastage, overcom-mitment ratio, migration time, SLA violation and energy consumption at the same time. This paper presents a dynamic multi-objective VMP in CDC based on overcommitment resource allocation to influence VM-PM mapping. We validated our method of conducting a performance evaluation study using the CloudSim tool. The experimental findings show that the suggested study decreases energy consumption, makespan, SLA violations, and PM overloading while enhancing resource utilization.

The Expected Goals (xG) is a performance metric used to evaluate a football team's or a player's performance. Simply put, it represents the probability of a scoring opportunity that may result in a goal. This metric suits the low-scoring nature of sports such as football. The score of a match involves randomness and inexplicable factors that skew the data represented by standard metrics and often may not represent the actual performance of an individual or a team; therefore, it would be of more significant benefit to individuals trying to analyse a player or a team to use alternative statistics rather than shots on target, ball possessions percentage, and sprints completed. The xG Model is trained on several key metrics derived from on-field events, corroborating with the historical to measure the probability of a shot being a goal by the common goal. The selection of these features, the size and date of the data, and the model used as the parameters that may affect the model's performance. Using machine learning models to increase the model's predictive performance decreases the vagueness caused by subjective interpretation. This paper proposes an accurate expected goal model trained on a compiled dataset containing data from the FIFA World Cup 2018 and 2022, and the UEFA Champions League 2018-2022, with a total of 768,744 shots taken by the top players take when representing their country and club on the biggest stage. Moreover, this model is explained by using data visualisation tools to obtain an explainable expected goal representation for evaluating a team or player's performance. Moreover, these methods can be generalised to other sports.