Implications of non-volatile memory as primary storage for database management systems

Traditional Database Management System (DBMS) software relies on hard disks for storing relational data. Hard disks are cheap, persistent, and offer huge storage capacities. However, data retrieval latency for hard disks is extremely high. To hide this latency, DRAM is used as an intermediate storage. DRAM is significantly faster than disk, but deployed in smaller capacities due to cost and power constraints, and without the necessary persistency feature that disks have. Non-Volatile Memory (NVM) is an emerging storage class technology which promises the best of both worlds. It can offer large storage capacities, due to better scaling and cost metrics than DRAM, and is non-volatile (persistent) like hard disks. At the same time, its data retrieval time is much lower than that of hard disks and it is also byte-addressable like DRAM. In this paper, we explore the implications of employing NVM as primary storage for DBMS. In other words, we investigate the modifications necessary to be applied on a traditional relational DBMS to take advantage of NVM features. As a case study, we have modified the storage engine (SE) of PostgreSQL enabling efficient use of NVM hardware. We detail the necessary changes and challenges such modifications entail and evaluate them using a comprehensive emulation platform. Results indicate that our modified SE reduces query execution time by up to 40% and 14.4% when compared to disk and NVM storage, with average reductions of 20.5% and 4.5%, respectively.The research leading to these results has received funding from the European Union’s 7th Framework Programme under grant agreement number 318633, the Ministry of Science and Technology of Spain under contract TIN2015-65316-P, and a HiPEAC collaboration grant awarded to Naveed Ul Mustafa.Peer ReviewedPostprint (author's final draft

HEC: Collaborative Research: SAM^2 Toolkit: Scalable and Adaptive Metadata Management for High-End Computing

The increasing demand for Exa-byte-scale storage capacity by high end computing applications requires a higher level of scalability and dependability than that provided by current file and storage systems. The proposal deals with file systems research for metadata management of scalable cluster-based parallel and distributed file storage systems in the HEC environment. It aims to develop a scalable and adaptive metadata management (SAM2) toolkit to extend features of and fully leverage the peak performance promised by state-of-the-art cluster-based parallel and distributed file storage systems used by the high performance computing community. There is a large body of research on data movement and management scaling, however, the need to scale up the attributes of cluster-based file systems and I/O, that is, metadata, has been underestimated. An understanding of the characteristics of metadata traffic, and an application of proper load-balancing, caching, prefetching and grouping mechanisms to perform metadata management correspondingly, will lead to a high scalability. It is anticipated that by appropriately plugging the scalable and adaptive metadata management components into the state-of-the-art cluster-based parallel and distributed file storage systems one could potentially increase the performance of applications and file systems, and help translate the promise and potential of high peak performance of such systems to real application performance improvements. The project involves the following components: 1. Develop multi-variable forecasting models to analyze and predict file metadata access patterns. 2. Develop scalable and adaptive file name mapping schemes using the duplicative Bloom filter array technique to enforce load balance and increase scalability 3. Develop decentralized, locality-aware metadata grouping schemes to facilitate the bulk metadata operations such as prefetching. 4. Develop an adaptive cache coherence protocol using a distributed shared object model for client-side and server-side metadata caching. 5. Prototype the SAM2 components into the state-of-the-art parallel virtual file system PVFS2 and a distributed storage data caching system, set up an experimental framework for a DOE CMS Tier 2 site at University of Nebraska-Lincoln and conduct benchmark, evaluation and validation studies

Pervasive Data Access in Wireless and Mobile Computing Environments

The rapid advance of wireless and portable computing technology has brought a lot of research interests and momentum to the area of mobile computing. One of the research focus is on pervasive data access. with wireless connections, users can access information at any place at any time. However, various constraints such as limited client capability, limited bandwidth, weak connectivity, and client mobility impose many challenging technical issues. In the past years, tremendous research efforts have been put forth to address the issues related to pervasive data access. A number of interesting research results were reported in the literature. This survey paper reviews important works in two important dimensions of pervasive data access: data broadcast and client caching. In addition, data access techniques aiming at various application requirements (such as time, location, semantics and reliability) are covered

Inter-program Optimizations for Disk Energy Reduction

Compiler support for power and energy management has been shown to be effective in reducing overall power dissipation and energy consumption of programs, for instance through compiler-directed resource hibernation and dynamic frequency and voltage scaling. The multi-programming model with virtual memory presents a virtualized view of the machine such that compilers typically take single programs as input, without the knowledge of other programs that may run at the same time on the target machine. This work investigates the benefits of optimizing sets of programs with the goal of reducing overall disk energy. The two key ideas are to synchronize the disk accesses across a group of programs thereby allowing longer disk idle periods, and to utilize execution context knowledge to allocate maximal buffer sizes. The compiler inserts runtime system calls for profiling the application and disk, uses execution context in allocating buffers, and synchronizes disk accesses with an inverse barrier policy. Data prefetching has been added to mitigate the overhead of synchronization. Experimental results are based on three streaming applications and their subsets. The experiments show that inter-program optimizations can have significant disk energy savings over individually optimized programs. Applying the most aggressive inter-program optimizations result in energy savings of up to 49%, and saving 34% on average

Space-Efficient Predictive Block Management

With growing disk and storage capacities, the amount of required metadata for tracking all blocks in a system becomes a daunting task by itself. In previous work, we have demonstrated a system software effort in the area of predictive data grouping for reducing power and latency on hard disks. The structures used, very similar to prior efforts in prefetching and prefetch caching, track access successor information at the block level, keeping a fixed number of immediate successors per block. While providing powerful predictive expansion capabilities and being more space efficient in the amount of required metadata than many previous strategies, there remains a growing concern of how much data is actually required. In this paper, we present a novel method of storing equivalent information, SESH, a Space Efficient Storage of Heredity. This method utilizes the high amount of block-level predictability observed in a number of workload trace sets to reduce the overall metadata storage by up to 99% without any loss of information. As a result, we are able to provide a predictive tool that is adaptive, accurate, and robust in the face of workload noise, for a tiny fraction of the metadata cost previously anticipated; in some cases, reducing the required size from 12 gigabytes to less than 150 megabytes

New techniques to model energy-aware I/O architectures based on SSD and hard disk drives

For years, performance improvements at the computer I/O subsystem and at other subsystems have advanced at their own pace, being less the improvements at the I/O subsystem, and making the overall system speed dependant of the I/O subsystem speed. One of the main factors for this imbalance is the inherent nature of disk drives, which has allowed big advances in disk densities, but not so many in disk performance. Thus, to improve I/O subsystem performance, disk drives have become a goal of study for many researchers, having to use, in some cases, different kind of models. Other research studies aim to improve I/O subsystem performance by tuning more abstract I/O levels. Since disk drives lay behind those levels, real disk drives or just models need to be used. One of the most common techniques to evaluate the performance of a computer I/O subsystem is found on detailed simulation models including specific features of storage devices like disk geometry, zone splitting, caching, read-ahead buffers and request reordering. However, as soon as a new technological innovation is added, those models need to be reworked to include new characteristics, making difficult to have general models up to date. Our alternative is modeling a storage device as a black-box probabilistic model, where the storage device itself, its interface and the interconnection mechanisms are modeled as a single stochastic process, defining the service time as a random variable with an unknown distribution. This approach allows generating disk service times needing less computational power by means of a variate generator included in a simulator. This approach allows to reach a greater scalability in I/O subsystems performance evaluations by means of simulation. Lately, energy saving for computing systems has become an important need. In mobile computers, the battery life is limited to a certain amount of time, and not wasting energy at certain parts would extend the usage of the computer. Here, again the computer I/O subsystem has pointed out as field of study, because disk drives, which are a main part of it, are one of the most power consuming elements due to their mechanical nature. In server or enterprise computers, where the number of disks increase considerably, power saving may reduce cooling requirements for heat dissipation and thus, great monetary costs. This dissertation also considers the question of saving energy in the disk drive, by making advantage of diverse devices in hybrid storage systems, composed of Solid State Disks (SSDs) and Disk drives. SSDs and Disk drives offer different power characteristics, being SSDs much less power consuming than disk drives. In this thesis, several techniques that use SSDs as supporting devices for Disk drives, are proposed. Various options for managing SSDs and Disk devices in such hybrid systems are examinated, and it is shown that the proposed methods save energy and monetary costs in diverse scenarios. A simulator composed of Disks and SSD devices was implemented. This thesis studies the design and evaluation of the proposed approaches with the help of realistic workloads. --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Durante años, las mejoras de rendimiento en el subsistema de E/S del ordenador y en otros subsistemas han avanzado a su propio ritmo, siendo menores las mejoras en el subsistema de E/S, y provocando que la velocidad global del sistema dependa de la velocidad del subsistema de E/S. Uno de los factores principales de este desequilibrio es la naturaleza inherente de las unidades de disco, la cual que ha permitido grandes avances en las densidades de disco, pero no así en su rendimiento. Por lo tanto, para mejorar el rendimiento del subsistema de E/S, las unidades de disco se han convertido en objetivo de estudio para muchos investigadores, que se ven obligados a utilizar, en algunos casos, diferentes tipos de modelos o simuladores. Otros estudios de investigación tienen como objetivo mejorar el rendimiento del subsistema de E/S, estudiando otros niveles más abstractos. Como los dispositivos de disco siguen estando detrás de esos niveles, tanto discos reales como modelos pueden usarse para esos estudios. Una de las técnicas más comunes para evaluar el rendimiento del subsistema de E/S de un ordenador se ha encontrado en los modelos de simulación detallada, los cuales modelan características específicas de los dispositivos de almacenamiento como la geometría del disco, la división en zonas, el almacenamiento en caché, el comportamiento de los buffers de lectura anticipada y la reordenación de solicitudes. Sin embargo, cuando se agregan innovaciones tecnológicas, los modelos tienen que ser revisados a fin de incluir nuevas características que incorporen dichas innovaciones, y esto hace difícil el tener modelos generales actualizados. Nuestra alternativa es el modelado de un dispositivo de almacenamiento como un modelo probabilístico de caja negra, donde el dispositivo de almacenamiento en sí, su interfaz y sus mecanismos de interconexión se tratan como un proceso estocástico, definiendo el tiempo de servicio como una variable aleatoria con una distribución desconocida. Este enfoque permite la generación de los tiempos de servicio del disco, de forma que se necesite menos potencia de cálculo a través del uso de un generador de variable aleatoria incluido en un simulador. De este modo, se permite alcanzar una mayor escalabilidad en la evaluación del rendimiento del subsistema de E/S a través de la simulación. En los últimos años, el ahorro de energía en los sistemas de computación se ha convertido en una necesidad importante. En ordenadores portátiles, la duración de la batería se limita a una cierta cantidad de tiempo, y no desperdiciar energía en ciertas partes haría más largo el uso del ordenador. Aquí, de nuevo el subsistema de E/S se señala como campo de estudio, ya que las unidades de disco, que son una parte principal del mismo, son uno de los elementos de más consumo de energía debido a su naturaleza mecánica. En los equipos de servidor o de empresa, donde el número de discos aumenta considerablemente, el ahorro de energía puede reducir las necesidades de refrigeración para la disipación de calor y por lo tanto, grandes costes monetarios. Esta tesis también considera la cuestión del ahorro energético en la unidad de disco, haciendo uso de diversos dispositivos en sistemas de almacenamiento híbridos, que emplean discos de estado sólido (SSD) y unidades de disco. Las SSD y unidades de disco ofrecen diferentes características de potencia, consumiendo las SSDs menos energía que las unidades de disco. En esta tesis se proponen varias técnicas que utilizan los SSD como apoyo a los dispositivos de disco. Se examinan las diversas opciones para la gestión de las SSD y los dispositivos de disco en tales sistemas híbridos, y se muestra que los métodos propuestos ahorran energía y costes monetarios en diversos escenarios. Se ha implementado un simulador compuesto por discos y dispositivos SSD. Esta tesis estudia el diseño y evaluación de los enfoques propuestos con la ayuda de las cargas de trabajo reales

Optimizing Virtual Machine I/O Performance in Cloud Environments

Maintaining closeness between data sources and data consumers is crucial for workload I/O performance. In cloud environments, this kind of closeness can be violated by system administrative events and storage architecture barriers. VM migration events are frequent in cloud environments. VM migration changes VM runtime inter-connection or cache contexts, significantly degrading VM I/O performance. Virtualization is the backbone of cloud platforms. I/O virtualization adds additional hops to workload data access path, prolonging I/O latencies. I/O virtualization overheads cap the throughput of high-speed storage devices and imposes high CPU utilizations and energy consumptions to cloud infrastructures. To maintain the closeness between data sources and workloads during VM migration, we propose Clique, an affinity-aware migration scheduling policy, to minimize the aggregate wide area communication traffic during storage migration in virtual cluster contexts. In host-side caching contexts, we propose Successor to recognize warm pages and prefetch them into caches of destination hosts before migration completion. To bypass the I/O virtualization barriers, we propose VIP, an adaptive I/O prefetching framework, which utilizes a virtual I/O front-end buffer for prefetching so as to avoid the on-demand involvement of I/O virtualization stacks and accelerate the I/O response. Analysis on the traffic trace of a virtual cluster containing 68 VMs demonstrates that Clique can reduce inter-cloud traffic by up to 40%. Tests of MPI Reduce_scatter benchmark show that Clique can keep VM performance during migration up to 75% of the non-migration scenario, which is more than 3 times of the Random VM choosing policy. In host-side caching environments, Successor performs better than existing cache warm-up solutions and achieves zero VM-perceived cache warm-up time with low resource costs. At system level, we conducted comprehensive quantitative analysis on I/O virtualization overheads. Our trace replay based simulation demonstrates the effectiveness of VIP for data prefetching with ignorable additional cache resource costs