To address the problems of large energy consumption and illegal possession of computing resources by users in cloud environment, a new algorithm named Fair and Green Resource Scheduling Algorithm (FGRSA) was proposed to save resources and enhance the fairness of the system, so that all users can reasonably use all the resources in the data center. By using the proposed method, various types of resources can be can scheduled to make use of all resources to achieve relative fairness. The simulation experiments of the proposed scheduling strategy was conducted on CloudSim. Experimental resutls show that, compared with Greedy algorithm and Round Robin algorithm, FGRSA can significantly reduce the energy consumption and simultaneously ensure fair use of all types of resources.

Concerning the problems of resource allocation about uneven distribution, low efficiency, dislocation and so on, a new algorithm named Global Dominant Resource Fair (GDRF) allocation algorithm which adopts several rounds of allocation was proposed to meet the needs of different users, achieve multiple types of resource fairness, and get high resource utilization. First, a qualification queue was determined by allocated resource amount of the users, then the specific user was determined to allocate resource through the global dominant resource share and the global dominant resource weight. The matching condition of resources was took into account in allocation process and the progressive filling of Max-Min strategy was used. In addition, the universal fairness evaluation model of multi-resource allocation was applied to the specific algorithm. Comparison experiments were conducted based on a Google's cluster. Experimental results show that compared with maximizing multi-resource fairness based on dominant resource, the amount of allocated virtual machine is increased by 12%, the resource utilization is increased by 0.5 percentage points, and fairness evaluation value is increased by about 15%. The proposed algorithm has a high degree of adaptation of resources combination allocation, allowing the supply to better match users' demand.