SSD Cache Research Articles

GCRFP - PAGE REPLACEMENT FOR SOLID STATE DRIVE USING GHOST-CACHE

State Drive (SSD) is an alternative to data storage that is popular today, widely used as a media cache to speed up data access to the hard disk (HDD). This paper proposes page replacement technique on SSD cache that used frequency and recency parameter, alternately. The algorithm is selected adaptively based on trace input. This method helps to overcome changes in access patterns while minimizing the number of write processes to SSD. The proposed algorithm can choose a replacement technique that suits the user access pattern so that it can bring a better hit rate. The proposed algorithm is also integrated with the ghost-cache mechanism so that the reduction in the number of writing processes to SSD is significant. The experiment runs using a real dataset, describing trace of data read, and data write taken from real usage. The trial shows that the proposed algorithm can give good results compared to other similar algorithms.

Cache What You Need to Cache

The SSD has been playing a significantly important role in caching systems due to its high performance-to-cost ratio. Since the cache space is typically much smaller than that of the backend storage by one order of magnitude or even more, write density (defined as writes per unit time and space) of the SSD cache is therefore much more intensive than that of HDD storage, which brings about tremendous challenges to the SSD’s lifetime. Meanwhile, under social network workloads, quite a lot writes to the SSD cache are unnecessary. For example, our study on Tencent’s photo caching shows that about 61% of total photos are accessed only once, whereas they are still swapped in and out of the cache. Therefore, if we can predict these kinds of photos proactively and prevent them from entering the cache, we can eliminate unnecessary SSD cache writes and improve cache space utilization. To cope with the challenge, we put forward a “one-time-access criteria” that is applied to the cache space and further propose a “one-time-access-exclusion” policy. Based on these two techniques, we design a prediction-based classifier to facilitate the policy. Unlike the state-of-the-art history-based predictions, our prediction is non-history oriented, which is challenging to achieve good prediction accuracy. To address this issue, we integrate a decision tree into the classifier, extract social-related information as classifying features, and apply cost-sensitive learning to improve classification precision. Due to these techniques, we attain a prediction accuracy greater than 80%. Experimental results show that the one-time-access-exclusion approach results in outstanding cache performance in most aspects. Take LRU, for instance: applying our approach improves the hit rate by 4.4%, decreases the cache writes by 56.8%, and cuts the average access latency by 5.5%.

PAP: power aware prediction based framework to reduce disk energy consumption

Disk-based storage subsystems account for a significant portion of the energy consumption in both low and high end servers. Therefore, there is a dire need to reduce the server power consumption of the hard disks. In this work, the power-aware framework has been proposed, which efficiently switches the disk into standby, active and idle states, leading to the least power consumption. Firstly, the trace of a real-world application has been generated and processed. The frequently used queries from the trace have been analyzed and prefetched in SSD cache using the data placement policy which lead to 78.5% cache hits. Subsequently, the idle time threshold policy has been executed, which regularly monitors and compares the disk idle time with its threshold value. Later, the request arrival threshold policy predicts the breakeven time using the ensemble machine learning model, which yields 87% accuracy with 3.5% average error rate. Only upon exceeding the threshold values, the disk would smartly be placed in the standby mode; otherwise, it would remain in the idle state to avoid the high power spins in case of frequent requests. Finally, the experimental results have been validated with the existing benchmarks using SSD as a cache, which leads to 75% average power savings.

Workload Scheduling for Massive Storage Systems with Arbitrary Renewable Supply

As datacenters grow in scale, increasing energy costs and carbon emissions have led data centers to seek renewable energy, such as wind and solar energy. However, tackling the challenges associated with the intermittency and variability of renewable energy is difficult. This paper proposes a scheme called GreenMatch, which deploys an SSD cache to match green energy supplies with a time-shifting workload schedule while maintaining low latency for online data-intensive services. With the SSD cache, the process for a latency-sensitive request to access a disk is divided into two stages: a low-energy/low-latency online stage and a high-energy/high-latency off-line stage. As the process in the latter stage is off-line, it offers opportunities for time-shifting workload scheduling in response to variations of green energy supplies. We also allocate an HDD cache to guarantee data availability when renewable energy is inadequate. Furthermore, we design a novel replacement policy called Inactive P-disk First for the HDD cache to avoid inactive disk accesses. The experimental results show that GreenMatch can make full use of renewable energy while minimizing the negative impacts of intermittency and variability on performance and availability.

Endurable SSD-Based Read Cache for Improving the Performance of Selective Restore from Deduplication Systems

Deduplication has been commonly used in both enterprise storage systems and cloud storage. To overcome the performance challenge for the selective restore operations of deduplication systems, solid-state-drive-based (i.e., SSD-based) read cache can be deployed for speeding up by caching popular restore contents dynamically. Unfortunately, frequent data updates induced by classical cache schemes (e.g., LRU and LFU) significantly shorten SSDs’ lifetime while slowing down I/O processes in SSDs. To address this problem, we propose a new solution — LOP-Cache — to greatly improve the write durability of SSDs as well as I/O performance by enlarging the proportion of long-term popular (LOP) data among data written into SSD-based cache. LOP-Cache keeps LOP data in the SSD cache for a long time period to decrease the number of cache replacements. Furthermore, it prevents unpopular or unnecessary data in deduplication containers from being written into the SSD cache. We implemented LOP-Cache in a prototype deduplication system to evaluate its performance. Our experimental results indicate that LOP-Cache shortens the latency of selective restore by an average of 37.3% at the cost of a small SSD-based cache with only 5.56% capacity of the deduplicated data. Importantly, LOP-Cache improves SSDs’ lifetime by a factor of 9.77. The evidence shows that LOP-Cache offers a cost-efficient SSD-based read cache solution to boost performance of selective restore for deduplication systems.

RAID를 위한 SSD 캐시: 데이터 캐싱과 패리티 갱신 지연 기법의 결합

RAID를 위한 SSD 캐시: 데이터 캐싱과 패리티 갱신 지연 기법의 결합

A fully persistent and consistent read/write cache using flash-based general SSDs for desktop workloads

The flash-based SSD is used as a tiered cache between RAM and HDD. Conventional schemes do not utilize the nonvolatile feature of SSD and cannot cache write requests. Writes are a significant, or often dominant, fraction of storage workloads. To cache write requests, the SSD cache should persistently and consistently manage its data and metadata, and guarantee no data loss even after a crash. Persistent cache management may require frequent metadata changes and causes high overhead. Some researchers insist that a nonvolatile persistent cache requires new additional primitives that are not supported by general SSDs in the market. We proposed a fully persistent read/write cache, which improves both read and write performance, does not require any special primitive, has a low overhead, guarantees the integrity of the cache metadata and the consistency of the cached data, even during a crash or power failure, and is able to recover the flash cache quickly without any data loss. We implemented the persistent read/write cache as a block device driver in Linux. Our scheme aims at virtual desktop infra servers. So the evaluation was performed with massive, real desktop traces of five users for ten days. The evaluation shows that our scheme outperforms an LRU version of SSD cache by 50% and the read-only version of our scheme by 37%, on average, for all experiments. This paper describes most of the parts of our scheme in detail. Detailed pseudo-codes are included in the Appendix.

A Novel Method of Improving Cache Hit-rate in Hadoop MapReduce using SSD Cache

The MapReduce Program of Hadoop Distributed File System operates on any unspecified nodes due to distributed-parallel process and block replicate for data stability. Since it is difficult to guarantee the cache locality when a Solid State Drive is used as a cache in hadoop, cache hit-rate is decreased. In this paper, we suggest a method to improve cache hit rate by pre-loading the input data of the MapReduce onto the SSD cache. To perform this method, we estimated the blocks that are used on each node by using capacity scheduler and block metadata. Eventually we could increase the performance of SSD cache by loading the blocks onto SSD cache before the Map Task run.

Evaluation of Volume Tiering Method and SSD Cache Method in Tiered Storage System

SUMMARYWe evaluated the storage I/O response time with the volume tiering method and the SSD cache method on tiered storage having SSD and HDD. The simulation results show that the volume tiering method improves the storage performance with low I/O locality workload and small SSD capacity. The SSD cache method is effective with read‐intensive and high I/O locality workloads. Utilizing both methods simultaneously reduces the storage I/O response time by up to 20% with write‐intensive workloads and large SSD capacity compared to using the volume tiering method or the SSD cache method separately.

Enhanced Hypervisor-based SSD Cache with Dynamic Cache Scanning and Allocation for Virtualized Cloud System

Enhanced Hypervisor-based SSD Cache with Dynamic Cache Scanning and Allocation for Virtualized Cloud System

Improving performance and lifetime of the SSD RAID-based host cache through a log-structured approach

This paper proposes a cost-effective and reliable SSD host cache solution that we call SRC (SSD RAID Cache). Costeffectiveness is brought about by using multiple low-cost SSDs and reliability is enhanced through RAID-based data redundancy. RAID, however, is managed in a log-structured manner on multiple SSDs effectively eliminating the detrimental read-modify-write operations found in conventional RAID-5. Within the proposed framework, we also propose to eliminate parity blocks for stripes that are composed of clean blocks as the original data resides in primary storage. We also propose the use of destaging, instead of garbage collection, to make space in the cache when the SSD cache is full. We show that the proposed techniques have significant implications on the performance of the cache and lifetime of the SSDs that comprise the cache. Finally, we study various ways in which stripes can be formed based on data and parity block allocation policies. Our experimental results using different realistic I/O workloads show using the SRC scheme is on average 59% better than the conventional SSD cache scheme supporting RAID-5. In case of lifetime, our results show that SRC reduces the erase count of the SSD drives by an average of 47% compared to the RAID-5 scheme.

Evaluation of SSD Allocation Capacity Optimization Method between Tiered Volume and SSD Cache in Tiered Storage System

Evaluation of SSD Allocation Capacity Optimization Method between Tiered Volume and SSD Cache in Tiered Storage System

Evaluation of Volume Tiering Method and SSD Cache Method in Tiered Storage System

SUMMARY We evaluated the storage I/O response time with the volume tiering method and the SSD cache method on tiered storage having SSD and HDD. The simulation results show that the volume tiering method improves the storage performance with low I/O locality workload and small SSD capacity. The SSD cache method is effective with read-intensive and high I/O locality workloads. Utilizing both methods simultaneously reduces the storage I/O response time by up to 20% with write-intensive workloads and large SSD capacity compared to using the volume tiering method or the SSD cache method separately.