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.

中文翻译：

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动态设置中无中断的异构存储增量吞吐量分配

固态硬盘 (SSD) 已被添加到存储系统中以提高其性能，这将为存储介质带来异构性。吞吐量是异构存储系统中必不可少的资源之一，如何分配吞吐量对用户性能起着至关重要的作用。关于异构存储系统的吞吐量分配的研究有很多种。然而，异构存储的吞吐量分配在动态设置中面临着新的挑战，即用户不是同时出现在系统中，而是动态地进入系统。借鉴经济博弈论，研究人员提出了许多方法来解决异构存储的动态吞吐量分配问题，包括共享激励 (SI)、嫉妒自由 (EF) 和帕累托最优 (PO)。然而，他们要么放松公平属性的约束，导致分配公平性较弱，要么中断系统中存在的一些用户，为进入系统的新用户放弃一部分分配，这将降低这些捐赠者的性能。而且，现有的所有方法都会由于用户主导份额平等的限制而导致资源利用率较低。在本文中，我们提出了一种基于逐渐增加（DAGI）的动态吞吐量分配方法，该方法可以适应各种工作负载以最大程度地利用资源进行公平分配。在不放松公平性约束的情况下，当新用户进入系统时，DAGI可以通过适当推迟剩余吞吐量的分配，进行具有强公平性的动态分配，因此，这可以提供一个机会，即 DAGI 在系统中所有用户都存在后分配剩余吞吐量时，可以保证最终分配具有很强的公平性。同时，DAGI 可以在不减少的情况下逐步增加用户分配，不会中断系统中存在的任何用户。此外，DAGI 可以根据用户的本地瓶颈资源进行动态吞吐量分配，可以适应用户的各种工作负载，提高资源利用率。进行了大量实验以证明 DAGI 的有效性。实验结果表明，与现有方法相比，DAGI 可以实现更高的资源利用率和性能，并且可以在保证强公平性的情况下满足理想的博弈论特性。此外，