In cloud computing environments, when a client wants to access any resources, hardware components, or application services, he needs to get a subscription for the same from service providers. The usages of each client are monitored over a network by service providers and later on user will be charged for the services used. Cloud service provider is responsible for providing Quality of Service to clients. As the number of client request increases in cloud environment, cloud service providers face various issues such as scheduling and allocation of resources, security, privacy and virtual machine migration. Swarm intelligence, biological systems, physical and chemical systems based metaheuristic algorithms have proved to be efficient and used to solve real world scheduling optimization problems. This review focused on the insight view of various nature-inspired metaheuristic algorithms and their comparisons on the basis of certain parameters that affects the efficiency and effectiveness of their applicability in order to schedule different tasks in cloud environment. This work facilitates comparative analysis of six metaheuristic techniques quantitatively based on scheduling parameters like makespan and resource utilization cost. The objective of this systematic review is to find the most optimal scheduling technique for solving multi criteria scheduling problem. After evaluating and comparing Ant Colony Optimization, Particle Swarm Optimization, Genetic Algorithm, Artificial Bee Colony algorithm, Crow Search Algorithm and Penguin Swarm Optimization Algorithm, it has been identified that Crow Search algorithm is the most optimal technique in terms of makespan and resource utilization cost parameters with significant improvement over others. Finally, the promising research directions has been identified.

在云计算环境中，当客户想要访问任何资源、硬件组件或应用服务时，他需要从服务提供商那里获得订阅。服务提供商通过网络监控每个客户端的使用情况，之后用户将就所使用的服务付费。云服务提供商负责向客户提供服务质量。随着云环境中客户端请求数量的增加，云服务提供商面临着各种问题，例如资源的调度和分配、安全、隐私和虚拟机迁移。基于群智能、生物系统、物理和化学系统的元启发式算法已被证明是有效的，并用于解决现实世界的调度优化问题。本综述重点介绍了各种受自然启发的元启发式算法的洞察力视图及其基于影响其适用性的效率和有效性的某些参数的比较，以便在云环境中调度不同的任务。这项工作有助于根据完工时间和资源利用成本等调度参数定量比较分析六种元启发式技术。本系统综述的目的是找到解决多标准调度问题的最佳调度技术。在对蚁群优化、粒子群优化、遗传算法、人工蜂群算法、乌鸦搜索算法和企鹅群优化算法进行评估和比较后，已经确定，就完工时间和资源利用成本参数而言，Crow Search 算法是最佳技术，并且比其他算法有显着改进。最后，确定了有前景的研究方向。