Exploiting flash memory characteristics to improve performance of RAIS storage systems

Redundant array of independent SSDs (RAIS) is generally based on the traditional RAID design and implementation. The random small write problem is a serious challenge of RAIS. Random small writes in parity-based RAIS systems generate significantly more pre-reads and writes which can degrade RAIS performance and shorten SSD lifetime. In order to overcome the well-known write-penalty problem in the parity-based RAID5 storage systems, several logging techniques such as Parity Logging and Data Logging have been put forward. However, these techniques are originally based on mechanical characteristics of the HDDs, which ignore the properties of the flash memory. In this article, we firstly propose RAISL, a flash-aware logging method that improves the small write performance of RAIS storage systems. RAISL writes new data instead of new data and pre-read data to the log SSD by making full use of the invalid pages on the SSD of RAIS. RAISL does not need to perform the pre-read operations so that the original characteristics of workloads are kept. Secondly, we propose AGCRL on the basis of RAISL to further boost performance. AGCRL combines RAISL with access characteristic to guide read and write cost regulation to improve the performance of RAIS storage systems. Our experiments demonstrate that the RAISL significantly improves write performance and AGCRL improves both of write performance and read performance. AGCRL on average outperforms RAIS5 and RAISL by 39.15% and 16.59% respectively.

Prober： exploiting sequential characteristics in buffer for improving SSDs write performance

Solid state disks （SSDs） are becoming one of the mainstream storage devices due to their salient features, such as high read performance and low power consump- tion. In order to obtain high write performance and extend flash lifespan, SSDs leverage an internal DRAM to buffer frequently rewritten data to reduce the number of program operations upon the flash. However, existing buffer manage- ment algorithms demonstrate their blank in leveraging data access features to predict data attributes. In various real-world workloads, most of large sequential write requests are rarely rewritten in near future. Once these write requests occur, many hot data will be evicted from DRAM into flash mem- ory, thus jeopardizing the overall system performance. In order to address this problem, we propose a novel large write data identification scheme, called Prober. This scheme probes large sequential write sequences among the write streams at early stage to prevent them from residing in the buffer. In the meantime, to further release space and reduce waiting time for handling the incoming requests, we temporarily buffer the large data into DRAM when the buffer has free space, and leverage an actively write-back scheme for large sequential write data when the flash array turns into idle state. Experi- mental results demonstrate that our schemes improve hit ratio of write requests by up to 10%, decrease the average response time by up to 42% and reduce the number of erase opera- tions by up to 11%, compared with the state-of-the-art buffer replacement algorithms.

Critical survival barrier for branching random walk

We consider a branching random walk with an absorbing barrier,where the associated one-dimensional random walk is in the domain of attraction of an a-stable law.We shall prove that there is a barrier and a critical value such that the process dies under the critical barrier,and survives above it.This generalizes previous result in the case that the associated random walk has finite variance.