Generalized asset fairness mechanism for multi-resource fair allocation mechanism with two different types of resources

Fair and efficient allocation of multiple types of resources is a fundamental goal in cloud computing systems. Due to the heterogeneous resource requirements of a user's jobs on CPU, memory, etc., fairness of allocation is difficult to ensure while maximizing efficiency. Existing representative multi-resource fair allocation mechanisms, such as dominant resource fairness and its follow-up work, can hardly achieve maximum efficiency while ensuring fairness. To overcome this drawback, we propose a new fair allocation mechanism, called generalized asset fairness (GAF), to maximize the system resource utilization and ensure fairness. We show that GAF satisfies many desired fairness properties. To implement our resource allocation mechanism, we design a scheduling algorithm to find feasible solutions. The experimental results show that GAF can produce a fair allocation with higher resource utilization than all previous known fair mechanisms; GAF also performs well in resource sharing.

Automated summary

Fair and efficient allocation of multiple types of resources is a fundamental goal in cloud computing systems. However, ensuring fairness while maximizing efficiency is challenging due to the heterogeneous resource requirements of user tasks. To address this, the paper proposes a new fair allocation mechanism called generalized asset fairness (GAF) that maximizes system resource utilization and ensures fairness. Experimental results show that GAF achieves higher resource utilization compared to previous fair mechanisms and performs well in resource sharing.