Elevating commodity storage with the SALSA host translation layer

To satisfy increasing storage demands in both capacity and performance, industry has turned to multiple storage technologies, including Flash SSDs and SMR disks. These devices employ a translation layer that conceals the idiosyncrasies of their mediums and enables random access. Device translation layers are, however, inherently constrained: resources on the drive are scarce, they cannot be adapted to application requirements, and lack visibility across multiple devices. As a result, performance and durability of many storage devices is severely degraded. In this paper, we present SALSA: a translation layer that executes on the host and allows unmodified applications to better utilize commodity storage. SALSA supports a wide range of single- and multi-device optimizations and, because is implemented in software, can adapt to specific workloads. We describe SALSA's design, and demonstrate its significant benefits using microbenchmarks and case studies based on three applications: MySQL, the Swift object store, and a video server.Comment: Presented at 2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS

On-Disk Data Processing: Issues and Future Directions

In this paper, we present a survey of "on-disk" data processing (ODDP). ODDP, which is a form of near-data processing, refers to the computing arrangement where the secondary storage drives have the data processing capability. Proposed ODDP schemes vary widely in terms of the data processing capability, target applications, architecture and the kind of storage drive employed. Some ODDP schemes provide only a specific but heavily used operation like sort whereas some provide a full range of operations. Recently, with the advent of Solid State Drives, powerful and extensive ODDP solutions have been proposed. In this paper, we present a thorough review of architectures developed for different on-disk processing approaches along with current and future challenges and also identify the future directions which ODDP can take.Comment: 24 pages, 17 Figures, 3 Table

The case for a Hardware Filesystem

As secondary storage devices get faster with flash based solid state drives (SSDs) and emerging technologies like phase change memories (PCM), overheads in system software like operating system (OS) and filesystem become prominent and may limit the potential performance improvements. Moreover, with rapidly increasing on-chip core count, monolithic operating systems will face scalability issues on these many-core chips. Future operating systems are likely to have a distributed nature, with a separation of operating system services amongst cores. Also, general purpose processors are known to be both performance and power inefficient while executing operating system code. In the domain of High Performance Computing with FPGAs too, relying on the OS for file I/O transactions using slow embedded processors, hinders performance. Migrating the filesystem into a dedicated hardware core, has the potential of improving the performance of data-intensive applications by bypassing the OS stack to provide higher bandwdith and reduced latency while accessing disks. To test the feasibility of this idea, an FPGA-based Hardware Filesystem (HWFS) was designed with five basic operations (open, read, write, delete and seek). Furthermore, multi-disk and RAID-0 (striping) support has been implemented as an option in the filesystem. In order to reduce design complexity and facilitate easier testing of the HWFS, a RAM disk was used initially. The filesystem core has been integrated and tested with a hardware application core (BLAST) as well as a multi-node FPGA network to provide remote-disk access. Finally, a SATA IP core was developed and directly integrated with HWFS to test with SSDs. For evaluation, HWFS's performance was compared to an Ext2 filesystem, both on an FPGA-based soft processor as well as a modern AMD Opteron Linux server with sequential and random workloads. Results prove that the Hardware Filesystem and supporting infrastructure provide substantial performance improvement over software only systems. The system is also resource efficient consuming less than 3% of logic and 5% of the Block RAMs of a Xilinx Virtex-6 chip

Dynamic Virtual Page-based Flash Translation Layer with Novel Hot Data Identification and Adaptive Parallelism Management

Solid-state disks (SSDs) tend to replace traditional motor-driven hard disks in high-end storage devices in past few decades. However, various inherent features, such as out-of-place update [resorting to garbage collection (GC)] and limited endurance (resorting to wear leveling), need to be reduced to a large extent before that day comes. Both the GC and wear leveling fundamentally depend on hot data identification (HDI). In this paper, we propose a hot data-aware flash translation layer architecture based on a dynamic virtual page (DVPFTL) so as to improve the performance and lifetime of NAND flash devices. First, we develop a generalized dual layer HDI (DL-HDI) framework, which is composed of a cold data pre-classifier and a hot data post-identifier. Those can efficiently follow the frequency and recency of information access. Then, we design an adaptive parallelism manager (APM) to assign the clustered data chunks to distinct resident blocks in the SSD so as to prolong its endurance. Finally, the experimental results from our realized SSD prototype indicate that the DVPFTL scheme has reliably improved the parallelizability and endurance of NAND flash devices with improved GC-costs, compared with related works.Peer reviewe

Analysis of SSD’s Performance in Database Servers

Data storage is much needed in any type of device and there are multiple mechanisms for data storage which vary from the device to device but at the end it’s a magnetic drive which holds the data and stored in the form of digital format. One predominant data storage device is hard disk drive also called as HDD. Hard disk drives are used in a wide range of systems like computers, laptops and netbooks etc., it has magnetic platter which is used for reading and writing operations. (Hard disk drive, n.d.) With the emerging technologies and modularization of web application design architecture created a need for different kind of operating system and system architecture based on the functionality. If we want a server where files need to be placed it should be designed in such a way that it needs to be good at input and output operations (I/O). (How does a hard drive work?, 2018) If we want to store videos and stream, that server should be good at asynchronous streaming functionality. If we need to store the structured/un-structured data which can be pertained to any educational institution or an organization, we can use a database server to store this data in tables and it can be used. In general, we use hard disk drives to store any kind of data in all the servers, but there will be only changes in the system architecture. The concept of HDD utilization has been constant from past 20 years. There was a huge growth in the architectural design of operating systems used for hosting database servers, but when it comes to storage HDD’s have been used for many years. With the need for speed and faster operations from the perspective of storage, solid state drives come in to picture. (SSD Advantage, n.d.)They have a different kind of architecture when compared to HDD and they are called as SSD. This paper discusses the idea of using SSD’s instead of HDD’s in database servers. We created multiple database instances for SSD’s and HDD’s and also created multiple web applications using JAVA and connected to each of these database servers to access data via REST API’s. We have run multiple tests to compare the load time of all the different database instances and generated some visual analytics how it behaves when multiple/series of get operations made on the database with the REST API. This analysis will help in finding if there are any anomalies in the behavior with increase in throughput of read and write operations

SSD: New Challenges for Digital Forensics

ICT changes continuously and we are used to look at IT in a slightly dif-ferent way every year. Things are developed and manufactured to be smaller and faster but few changes are truly technologically revolutionary. Some changes creep up on us as they arrive under cover of previously known technology. Solid State Disks (SSD) is such a technology. The use of SSD is simple enough and for many purposes it can be used as if it was a normal hard disc but many times faster and with a very much lower power consumption. But, SSD is not an evolution of hard disc technology, it is a completely new technology which imitates the behav-iour of a hard disc. There are major underpinning differences which have serious consequences for security and for digital forensic. Due to how the SSDs work it is not always certain that deleted data are purged from the disc. On the other hand SSD‟s can sometimes purge data all by themselves even if they are not connected to any interface with only the power on. This means that normal guidelines aimed at hard discs for how to preserve digital forensic evidence are not just inappropri-ate but could if followed result in potential evidence being lost, destroyed or deemed unvalid as evidence. This paper gives an overview of some of the princi-pal and unexpected challenges that SSDs have brought with them for Digital Fo-rensics investigations

High availability using virtualization

High availability has always been one of the main problems for a data center. Till now high availability was achieved by host per host redundancy, a highly expensive method in terms of hardware and human costs. A new approach to the problem can be offered by virtualization. Using virtualization, it is possible to achieve a redundancy system for all the services running on a data center. This new approach to high availability allows to share the running virtual machines over the servers up and running, by exploiting the features of the virtualization layer: start, stop and move virtual machines between physical hosts. The system (3RC) is based on a finite state machine with hysteresis, providing the possibility to restart each virtual machine over any physical host, or reinstall it from scratch. A complete infrastructure has been developed to install operating system and middleware in a few minutes. To virtualize the main servers of a data center, a new procedure has been developed to migrate physical to virtual hosts. The whole Grid data center SNS-PISA is running at the moment in virtual environment under the high availability system. As extension of the 3RC architecture, several storage solutions have been tested to store and centralize all the virtual disks, from NAS to SAN, to grant data safety and access from everywhere. Exploiting virtualization and ability to automatically reinstall a host, we provide a sort of host on-demand, where the action on a virtual machine is performed only when a disaster occurs.Comment: PhD Thesis in Information Technology Engineering: Electronics, Computer Science, Telecommunications, pp. 94, University of Pisa [Italy