GREM: Dynamic SSD Resource Allocation In Virtualized Storage Systems With Heterogeneous VMs

In a shared virtualized storage system that runs heterogeneous VMs with diverse IO demands, it becomes a problem for the hypervisor to cost-effectively partition and allocate SSD resources among multiple VMs. There are two straightforward approaches to solve this problem: either equally assigning SSDs to each VM or managing SSD resources in a fair competition mode. Unfortunately, they cannot fully utilize the benefits of SSD resources, particularly when the workloads frequently change and bursts or spikes of IOs occur from time to time. In this paper, we design a Global SSD Resource Management solution GREM, which aims to fully utilize SSD resources as “smart” cache under the consideration of performance isolation. In particular, GREM takes dynamic IO demands of all VMs into consideration to split the entire SSD space into a long-term zone and a short-term zone and cost-effectively updates the content of SSDs in these two zones. GREM is able to adaptively adjust the reservation for each VM inside the long-term zone based on their IO changes. GREM can further dynamically partition SSDs between longand shortterm zones during runtime by leveraging the feedbacks from both cache performance and bursty workloads. Experimental results show that GREM can capture the cross-VM IO changes to make correct decisions on resource allocation and thus obtain high IO hit ratio and low IO costs, compared with both traditional and recent caching algorithms. Keywords—Solid State Drives, resource allocation, virtualized storage systems, caching algorithms, bursty workloads