SSD/ NVMe SSD Deployment
The demand for high speed `Storage-as-a-Service' (SaaS) is increasing day-by-day. SSDs are commonly used in higher tiers of storage rack in data centers. Also, all-flash data centers are evolving to serve cloud services better. In the era of big data processing, more and more data centers in cloud storages are now replacing traditional HDDs with enterprise SSDs. Although SSDs guaranty better performance when compared to HDDs, but SSDs endurance is still a matter of concern. Therefore, we put significant efforts towards resource management of different cloud-based workloads while using SSDs. To improve the performance, we develop frameworks that could take advantage of high bandwidth and parallel I/O queues provided by NVMe SSDs. We also innovate efficient caching and tiering techniques to boost up system performance. To improve the endurance of SSDs, we build infrastructures and techniques to manage data storage on SSDs.
FIOS: Feature Based I/O Stream Identification for Improving Endurance of Multi-Stream SSDs - [IEEE CLOUD'18]
The demand for high speed `Storage-as-a-Service' (SaaS) is increasing day-by-day. SSDs are commonly used in higher tiers of storage rack in data centers. Also, all flash data centers are evolving to better serve cloud services. Although SSDs guaranty better performance when compared to HDDs, but SSDs endurance is still a matter of concern. Storing data with different lifetime in an SSD can cause high write amplification and reduce the endurance and performance of SSDs. Recently, multi-stream SSDs have been developed to enable data with different lifetime to be stored in different SSD regions and thus reduce write amplification. To efficiently use this new multi-streaming technology, it is important to choose appropriate workload features to assign the same streamID to data with similar lifetime. However, we found that streamID identification using different features may have varying impacts on the final write amplification of multi-stream SSDs. Therefore, in this work we develop a portable and adoptable framework to study the impacts of different workload features and their combinations on write amplification. We also introduce a new feature, named "coherency", to capture the friendship among write operations with respect to their update time. Finally, we propose a feature-based stream identification approach, which co-relates the measurable workload attributes (such as I/O size, I/O rate, etc.) with high level workload features (such as frequency, sequentiality etc.) and determines a good combination of workload features for assigning streamIDs. Our evaluation results show that our proposed approach can always reduce the Write Amplification Factor (WAF) by using appropriate features for stream assignment.
BloomStream: Data Temperature Identification for Flash Based Memory Storage Using Bloom Filters - [IEEE CLOUD'18]
Data temperature identification is an importance issue of many fields like data caching and storage tiering in modern flash-based storage systems. With the technological advancement of memory and storage, data temperature identification is no longer just a classification of hot and cold, but instead becomes a ``multi-streaming" data categorization problem to classify data into multiple categories according to their temperature. Therefore, we propose a novel data temperature identification scheme that adopts bloom filters to efficiently capture both frequency and recency of data blocks and accurately identify the exact data temperature for each data block. Moreover, in bloom filter data structure we replace the original OR operation with the XOR masking operation such that our scheme can delete or reset bits in bloom filters and thus avoid high false positives due to saturation. We further utilize twin bloom filters to alternatively keep unmasked clean copies of data and thus ensure low false negative rate. Our extensive evaluation results show that our new scheme can accurately identify the exact data temperature with low false identification rates across different synthetic and real I/O workloads. More importantly, our scheme consumes less memory space compared to other existing data temperature identification schemes.
Docker Controller: Scheduler for I/O Intensive Applications running on NVMe SSDs - [TMSCS 2018], [IPCCC'16]
By using fast back-end storage, performance benefits of a lightweight container platform can be leveraged with quick I/O response. Nevertheless, the performance of simultaneously executing multiple instances of same or different applications may vary significantly with the number of containers. The performance may also vary with the nature of applications because different applications can exhibit different nature on SSDs in terms of I/O types (read/write), I/O access pattern (random/sequential), I/O size, etc. Therefore, this work aims to investigate and analyze the performance characterization of both homogeneous and heterogeneous mixtures of I/O intensive containerized applications, operating with high performance NVMe SSDs and derive novel design guidelines for achieving an optimal and fair operation of the both homogeneous and heterogeneous mixtures. By leveraging these design guidelines, we further develop a new docker controller for scheduling workload containers of different types of applications. Our controller decides the optimal batches of simultaneously operating containers in order to minimize total execution time and maximize resource utilization. Meanwhile, our controller also strives to balance the throughput among all simultaneously running applications. We develop this new docker controller by solving an optimization problem using five different optimization solvers. We conduct our experiments in a platform of multiple docker containers operating on an array of three enterprise NVMe drives. We further evaluate our controller using different applications of diverse I/O behaviors and compare it with simultaneous operation of containers without the controller. Our evaluation results show that our new docker workload controller helps speed-up the overall execution of multiple applications on SSDs.
H-NVMe: A Hybrid Framework of NVMe-based Storage System in Cloud Computing Environment - [IPCCC'17]
In the year of 2017, more and more datacenters have started to replace traditional SATA and SAS SSDs with NVMe SSDs due to NVMe's outstanding performance. However, for historical reasons, current popular deployments of NVMe in VM-hypervisor-based platforms (such as VMware ESXi) have numbers of intermediate queues along the I/O stack. As a result, performance is bottlenecked by synchronization locks in these queues, cross-VM interference induces I/O latency, and most importantly, up-to-64K-queue capability of NVMe SSDs cannot be fully utilized. In this work, we developed a hybrid framework of NVMe-based storage system called ``H-NVMe'', which provides two VM I/O stack deployment modes ``Parallel Queue Mode'' and ``Direct Access Mode''. The first mode increases parallelism and enables lock-free operations by implementing local lightweight queues in the NVMe driver. The second mode further bypasses the entire I/O stack in the hypervisor layer and allows trusted user applications whose hosting VMDK (Virtual Machine Disk) files are attached with our customized vSphere IOFilters to directly access NVMe SSDs to improve the performance isolation. This suits premium users who have higher priorities and the permission to attach IOFilter to their VMDKs. H-NVMe is implemented on VMware EXSi 6.0.0, and our evaluation results show that the proposed H-NVMe framework can significant improve throughputs and bandwidths compared to the original inbox NVMe solution.