Incremental Throughput Allocation of Heterogeneous Storage With No Disruptions in Dynamic Setting

Solid-state drives (SSDs) have been added into storage systems for improving their performance, which will bring the heterogeneity into the storage medium. The throughput is one of the essential resources in heterogeneous storage systems, and how to allocate the throughput plays a crucial role in user performance. There are many types of research on the throughput allocation of heterogeneous storage systems. However, the throughput allocation of heterogeneous storage is facing new challenges in a dynamic setting, where users are not present in the system simultaneously, and enter the system dynamically. Drawing on economic game-theory, researchers have proposed many methods to tackle dynamic throughput allocation issues for heterogeneous storages, cross out enjoying Sharing Incentive (SI), Envy Freeness (EF), and Pareto Optimality (PO). However, they either relax constraints of fairness property to cause the allocation with weak fairness or interrupt some users present in the system to give up a piece of their allocations for new users entering the system, which will degrade these donors’ performance. Moreover, all of existing methods will cause lower resource utilization due to constraints of users’ dominant share equality. In this article, we propose a dynamic throughout allocation method based on gradual increase (DAGI), which can adapt to various workloads to make a fair allocation with a maximum resource utilization. Without relaxing constraints of fairness properties, when new users enter the system, DAGI can make a dynamic allocation with strong fairness by appropriately postponing the allocation of surplus throughputs, so this can provide an opportunity that DAGI can guarantee the final allocation with strong fairness when allocating remaining throughputs after all users are present in the system. Meanwhile, DAGI can gradually increase user allocation without reduction, which will not interrupt any users present in the system. Furthermore, DAGI can conduct a dynamic throughput allocation based on users’ local bottleneck resources, which can adapt to various workloads of users to improve resource utilization. Extensive experiments are conducted to prove the effectiveness of DAGI. The experimental results show that DAGI can achieve higher resource utilization and performance than existing methods, and can satisfy desirable game-theoretic properties with guaranteeing the strong fairness. In addition, DAGI gradually increases the allocation of each user without interrupting any user to reduce its allocation to degrade its performance.