# Task-Driven Virtual Machine Optimization Placement Model and Algorithm
## 2 Related Works
### VM Placement Method  
First-fit decreasing VM placement (FFD-VMP) [8] is generally used as a benchmark method for evaluating VM placement; a first-fit heuristic algorithm is employed to place VMs. PABFD-VM placement (PABFD-VMP) [7] involves the use of an adaptive heuristic algorithm to adjust VM placement dynamically. COFFGA-VM placement (COFFGA-VMP) [9] requires using a genetic algorithm to address combinatorial optimization problems with varying capacity constraints to determine the optimal allocation of VMs to PMs. [PERSON] et al. [10] proposed an enhanced energy-efficient ant colony optimization strategy (EEACS) for VM placement, which prioritizes PMs by energy efficiency and improves server selection and pheromone update rules. This approach ensures efficient VM placement using pheromones and heuristic information. [PERSON] et al. [18] proposed a multi-objective algorithm based on ant colony optimization, which improved the service quality and resource utilization of data centers by optimizing multiple objectives such as resource allocation, energy consumption, and network latency. [PERSON] et al. [19] improved the VM placement process by introducing a multicore-aware VM placement policy combined with constraint programming techniques. This approach aims to improve the resource utilization, system performance, and scalability of cloud data centers. [PERSON] et al. [20] proposed a dynamic evolutionary multi-objective heuristic algorithm for VM placement; it dynamically adapted to changing resource demands while optimizing energy efficiency, reducing network latency, and guaranteeing service quality. However, in these studies, VM placement was examined without considering the impact of task loading; thus, dynamically adjusting to actual workloads failed. Consequently, the makespan could not be effectively optimized, ultimately impacting cloud data center performance and user experience.  
[PERSON] et al. [15] considered both task scheduling and VM placement to develop a multi-objective optimization framework that optimized both VM rental costs and resource utilization in cloud data centers. Meta-heuristic algorithms were employed to solve this joint optimization problem to achieve a comprehensive balance between multiple optimization objectives. However, the use of a weighted sum method to transform a multi-objective optimization problem into a single-objective one lacks flexibility and may result in the omission of many valuable solutions. To address resource management and optimization issuesin cloud data centers, a decomposition-based multi-objective evolutionary algorithm for optimizing both VM placement and task scheduling was developed by considering three objectives: minimizing the completion time, costs, and total tardiness [17]. Although the focus was only on minimizing energy consumption, [PERSON] et al. [16] introduced an integrated optimization framework for task scheduling and VM placement. This resulted in a more comprehensive optimization method when compared with existing research. Inspired by previous studies [15; 16; 17], this work improved NSGA-III to explore VM placement schemes tailored to user task loading, aiming to balance the interests of both cloud service providers and users.