A computing resource adjustment mechanism for communication protocol processing in centralized radio access networks

The centralized radio access cellular network infrastructure based on centralized Super Base Station(CSBS) is a promising solution to reduce the high construction cost and energy consumption of conventional cellular networks. With CSBS, the computing resource for communication protocol processing could be managed flexibly according the protocol load to improve the resource efficiency. Since the protocol load changes frequently and may exceed the capacity of processors, load balancing is needed. However, existing load balancing mechanisms used in data centers cannot satisfy the real-time requirement of the communication protocol processing. Therefore, a new computing resource adjustment scheme is proposed for communication protocol processing in the CSBS architecture. First of all, the main principles of protocol processing resource adjustment is concluded, followed by the analysis on the processing resource outage probability that the computing resource becomes inadequate for protocol processing as load changes. Following the adjustment principles, the proposed scheme is designed to reduce the processing resource outage probability based onthe optimized connected graph which is constructed by the approximate Kruskal algorithm. Simulation results show that compared with the conventional load balancing mechanisms, the proposed scheme can reduce the occurrence number of inadequate processing resource and the additional resource consumption of adjustment greatly.

[1]  Hayssam Dahrouj,et al.  Resource allocation in heterogeneous cloud radio access networks: advances and challenges , 2015, IEEE Wireless Communications.

[2]  Yu Cheng,et al.  CONCERT: a cloud-based architecture for next-generation cellular systems , 2014, IEEE Wireless Communications.

[3]  Yong Li,et al.  System architecture and key technologies for 5G heterogeneous cloud radio access networks , 2015, IEEE Netw..

[4]  Massoud Pedram,et al.  Multi-dimensional SLA-Based Resource Allocation for Multi-tier Cloud Computing Systems , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[5]  Lih-Hsing Hsu,et al.  Finding the Most Vital Edge with Respect to Minimum Spanning Tree in Weighted Graphs , 1991, Inf. Process. Lett..

[6]  Lin Tian,et al.  Two-Stage Cooperative Multicast Transmission with Optimized Power Consumption and Guaranteed Coverage , 2014, IEEE Journal on Selected Areas in Communications.

[7]  Lin Tian,et al.  Load diversity based optimal processing resource allocation for super base stations in centralized radio access networks , 2014, Science China Information Sciences.

[8]  Lin Tian,et al.  Real-time guaranteed TDD protocol processing for centralized super base station architecture , 2015, 2015 IEEE Wireless Communications and Networking Conference (WCNC).

[9]  Qiang Liu,et al.  Acquisition of channel state information in heterogeneous cloud radio access networks: challenges and research directions , 2015, IEEE Wireless Communications.

[10]  Moustafa Ghanem,et al.  Lightweight Resource Scaling for Cloud Applications , 2012, 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012).

[11]  Yiqing Zhou,et al.  Coordinated Multipoint Transmission in Dense Cellular Networks With User-Centric Adaptive Clustering , 2014, IEEE Transactions on Wireless Communications.

[12]  Qing Wang,et al.  Virtual base station pool: towards a wireless network cloud for radio access networks , 2011, CF '11.

[13]  Cheng Hua Li,et al.  An Adaptive Load Balancing Method of Adjustment Based on Cloud Computing Resources , 2013 .