Fairness-Efficiency Tradeoff Allocation with Meta-Types in Cloud Computing

We study the problem of multiple resource allocation in cloud computing systems. Existing fairness-efficiency scheduling procedures can relax fairness constraints by using a knob to improve efficiency. However, these approaches do not take into account users with special needs, i.e., the same resource (meta-type, e.g., CPU) contains different types (e.g., Intel's CPU, AMD's CPU) and the user can only use a specific type of resources (e.g., Intel's CPU). We propose a new allocation mechanism called Fairness-Efficiency Tradeoff Allocation with Meta-Types (FET-MT), which introduces the concept of meta-types. FET-MT not only meets specific requirements proposed by users but also allows users to flexibly balance fairness and efficiency by adjusting the knob values. Finally, we implemented the FET-MT method using GUROBI, and our experiments show that the running time of FET-MT is reduced by approximately a factor of 7 with respect to Maximum Nash Welfare (MNW) and discrete MNW and that FET-MT can still maintain good running efficiency as the number of users increases. The experimental results also show that FET-MT can obtain nearly twice the social welfare of MNW and DRF-MT, and the utilization of meta-types in the system is close to 100%.