# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
### Review of Existing Surveys  
Many surveys have been performed considering resource allocation and scheduling in cloud [6], resource sharing in heterogeneous cloud radio access network (H-CRAN) [7], resource management in 5G RAN [8], energy-efficient base station switching techniques in green cellular network [9], energy-efficient wireless communication [10], radio resource management in machine-to-machine communication [11], strategies for switching off base stations in heterogeneous networks [12], and clustering techniques for RRHs [13]. The topics of the previous surveys along with their respective publishing years, and the areas they covered in those surveys are listed in Table 1.  
The authors in [6; 14; 15; 16; 17; 18] presented surveys through investigating various works related to resource management methods in a cloud computing environment. [PERSON] et al. discussed the studies on the application of computational intelligence tools in cloud computing resource optimization problems by dividing the resource management problems into static and dynamic problems [15]. This work further identified a few alternative methods and unexplored territories with regard to the optimization of cloud resource management, for future researchers. A short survey regarding the resource allocation and scheduling methods for application in the cloud is presented in [6]. [PERSON] et al. outlined a conceptual framework for cloud resource management and used it to structure the state-of-the-art review; furthermore, they identified a few challenges for future investigation based on the review [14]. [PERSON] et al. presented a methodical analysis of resource scheduling in cloud computing, resource scheduling algorithms, and resource management [16]. The study also analyzed the types and benefits of resource management tools as well as resource scheduling aspects and resource allocation policies. The article further recommended a few research directions for future works. [PERSON] et al. studied the research issues in the area of resource management, specifically, resource allocation and monitoring in the cloud-computing environment. They also studied solution approaches [17]. [PERSON] et al. presented a comparative study of the works that employed machine learning to offer solutions for energy efficiency in cloud computing environments along with some future research directions [19]. The related works on resource management techniques for H-CRANs have been reviewed in [7; 12; 20; 21; 22]. In [7], the resource sharing opportunities in H-CRAN were investigated considering the spectrum, infrastructure, and network levels. The major challenges in deploying dynamic resource sharing in this architecture and the research direction for addressing these challenges were also discussed. In addition, a few trending technologies that are essential for resource sharing in an H-CRAN were highlighted. Finally, a simulation of spectrum and infrastructure sharing in the H-CRAN environment was presented with results. Meanwhile, in [20], a comprehensive review of the works on the energy efficiency of H-CRAN and cloud computing was presented individually in conjunction with a discussion of the challenges and continuing issues in joint deployment. The authors discussed the energy efficiency issue for each building block of the network architecture. The authors in [21] reviewed the recent advances in HetNets, in conjunction with the architecture evolution, key techniques, and continuing research issues. They presented the system architectures of traditional HetNets, H-CRANs, and F-RANs (fog radio access networks). Moreover, a novel performance metric called economic energy efficiency was introduced with self-organizing HetNets and access slicing techniques. In addition, the key techniques across the physical, MAC, and network layers were elaborated.