  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.
