Input/Output Operations Per Second Research Articles

Approaching DRAM performance by using microsecond-latency flash memory for small-sized random read accesses

For applications in which small-sized random accesses frequently occur for datasets that exceed DRAM capacity, placing the datasets on SSD can result in poor application performance. For the read-intensive case we focus on in this paper, low latency flash memory with microsecond read latency is a promising solution. However, when they are used in large numbers to achieve high IOPS (Input/Output operations Per Second), the CPU processing involved in IO requests is an overhead. To tackle the problem, we propose a new access method combining two approaches: 1) optimizing issuance and completion of the IO requests to reduce the CPU overhead. 2) utilizing many contexts with lightweight context switches by stackless coroutines. These reduce the CPU overhead per request to less than 10 ns, enabling read access with DRAM-like overhead, while the access latency longer than DRAM can be hidden by the context switches. We apply the proposed method to graph algorithms such as BFS (Breadth First Search), which involves many small-sized random read accesses. In our evaluation, the large graph data is placed on microsecond-latency flash memories within prototype boards, and it is accessed by the proposed method. As a result, for the synthetic and real-world graphs, the execution times of the graph algorithms are 88--141% of those when all the data are placed in DRAM.

Mitigating Self-Heating in Solid State Drives for Industrial Internet-of-Things Edge Gateways

Data storage in the Industrial Internet-of-Things scenario presents critical aspects related to the necessity of bringing storage devices closer to the point where data are captured. Concerns on storage temperature are to be considered especially when Solid State Drives (SSD) based on 3D NAND Flash technology are part of edge gateway architectures. Indeed, self-heating effects caused by oppressive storage demands combined with harsh environmental conditions call for proper handling at multiple abstraction levels to minimize severe performance slow downs and reliability threats. In this work, with the help of a SSD co-simulation environment that is stimulated within a realistic Industrial Internet-of-Things (IIoT) workload, we explore a methodology orthogonal to performance throttling that can be applied in synergy with the operating system of the host. Results evidenced that by leveraging on the SSD micro-architectural parameters of the queuing system it is possible to reduce the Input/Output operations Per Second (IOPS) penalty due to temperature protection mechanisms with minimum effort by the system. The methodology presented in this work opens further optimization tasks and algorithmic refinements for SSD and system designers not only in the IIoT market segment, but generally in all areas where storage power consumption is a concern.

Performance Enhancement of Cloud Based Storage using Disk Scheduling Technique

Cloud-based high-performance storage techniques are proposed in this article. Primarily, the performance of storage is dependent on seek time of incoming track requests, IOPS(Input/output operations per second), and throughput. The proposed technique is designed to calculate the average seek time of incoming track requests, IOPS and throughput calculation, sorting algorithms and R/W head movement in order to represent the possibility of better performance than existing high performing algorithms.

A High‐Performance Hierarchical Snapshot Scheme for Hybrid Storage Systems

With the increased snapshot operations (creation or deletion), the performance of storage system will degrade severely whether in Copy on write (COW) or Redirect on write (ROW). This paper presents a novel snapshot scheme which could use of storage resources effectively and keep the historical data accurately for SSDHDD-hybrid storage system. SSD (source volume) stores all the active data and executes read/write operations without responding to snapshot operations. In contrast, HDDs (snapshot volume) store the writing data sequentially in the form of logs and are responsible for backup and snapshot operations. In this way, the hybrid storage system can get the high throughput power via SSD even the snapshot functions are active and HDDs can keep pace with the high Input/Output operations per second (IOPS) capability of the SSD. Logs are combined and kept in a separate image volume by the controller. So, fine-grained Recovery point objective (RPO) and good Recovery time objective (RTO) can be achieved. Experiment results show that the prototype can achieve excellent I/O performance while snapshot operations are active and consume a few extra storage space.

Garbage Collection for Low Performance Variation in NAND Flash Storage Systems

In many NAND flash-memory storage systems, invalidated pages can occupy the storage space until being erased. In order to preserve sustained write performance and effective storage capacity, the flash translation layer (FTL) must recycle these pages through garbage collection (GC) operations. Many previous studies have investigated GC techniques, most of which have focused on the effective selection of victim blocks to reduce the operational overhead. However, methods to reduce the cost overhead of the victim selection process, as well as to improve the responsiveness of storage systems during GC, have not yet been explored. In this paper, therefore, we propose a novel GC mechanism, called link-based GC (LINK-GC), which provides fast victim selection and preemptive operation with small additional space overhead to existing page-mapped FTLs. In our experiments, when compared with a GC scheme based on an on-demand victim search, the proposed mechanism increases the average input-output operations per second (IOPS) by up to 15.8% and decreases the standard deviation of IOPS by up to 6.16 times. Additionally, the LINK-GC shows better performance than the existing preemptive GC techniques in terms of responsiveness to host requests.