Jixian Zhang¹, Ning Xie¹, Xuejie Zhang¹, Kun Yue¹, Weidong Li^2,*, Deepesh Kumar³

CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 123-135, 2018, DOI: 10.3970/cmc.2018.03728

Abstract Resource allocation in auctions is a challenging problem for cloud computing. However, the resource allocation problem is NP-hard and cannot be solved in polynomial time. The existing studies mainly use approximate algorithms such as PTAS or heuristic algorithms to determine a feasible solution; however, these algorithms have the disadvantages of low computational efficiency or low allocate accuracy. In this paper, we use the classification of machine learning to model and analyze the multi-dimensional cloud resource allocation problem and propose two resource allocation prediction algorithms based on linear and logistic regressions. By learning a small-scale training set, the prediction model can… More >

Changming Zhao^1,2,*, Tiejun Wang², Alan Yang³

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 423-437, 2019, DOI:10.32604/cmc.2019.07501

Abstract In the paper, we investigate the heterogeneous resource allocation scheme for virtual machines with slicing technology in the 5G/B5G edge computing environment. In general, the different slices for different task scenarios exist in the same edge layer synchronously. A lot of researches reveal that the virtual machines of different slices indicate strong heterogeneity with different reserved resource granularity. In the condition, the allocation process is a NP hard problem and difficult for the actual demand of the tasks in the strongly heterogeneous environment. Based on the slicing and container concept, we propose the resource allocation scheme named Two-Dimension allocation and… More >