As cloud computing usage grows, cloud data centers play an increasingly important role. To maximize resource utilization, ensure service quality, and enhance system performance, it is crucial to allocate tasks and manage performance effectively. The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers. The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies, categories, and gaps. A literature review was conducted, which included the analysis of 463 task allocations and 480 performance management papers. The review revealed three task allocation research topics and seven performance management methods. Task allocation research areas are resource allocation, load-Balancing, and scheduling. Performance management includes monitoring and control, power and energy management, resource utilization optimization, quality of service management, fault management, virtual machine management, and network management. The study proposes new techniques to enhance cloud computing work allocation and performance management. Short-comings in each approach can guide future research. The research's findings on cloud data center task allocation and performance management can assist academics, practitioners, and cloud service providers in optimizing their systems for dependability, cost-effectiveness, and scalability. Innovative methodologies can steer future research to fill gaps in the literature.

翻译：随着云计算应用的日益增长，云数据中心发挥着越来越重要的作用。为最大化资源利用率、保障服务质量并提升系统性能，高效的任务分配与性能管理至关重要。本研究旨在对云数据中心采用的任务分配及性能管理技术进行深入分析，通过识别云计算方法论、分类及研究空白，系统性地对既有研究进行分类与梳理。本文通过文献综述，分析了463篇任务分配论文与480篇性能管理论文。综述揭示了三个任务分配研究主题和七种性能管理方法。任务分配研究领域包括资源分配、负载均衡与调度。性能管理涵盖监控与控制、功耗与能源管理、资源利用率优化、服务质量保障、故障管理、虚拟机管理及网络管理。本研究提出了增强云计算工作分配与性能管理的新技术，并指出了各方法的局限性，可为未来研究提供指引。研究揭示的云数据中心任务分配与性能管理成果，有助于学术界、从业者及云服务提供商优化系统，以实现高可靠性、经济性与可扩展性。创新方法论可引领未来研究填补文献空白。