Laura Prada

Using Black-Box Modeling Techniques for Modern Disk Drives Service Time Simulation

One of the most common techniques to evaluate the performance of a computer I/O subsystem performance has been 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 devices making difficult to have general models up to date. Another 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 the I/O subsystems performance evaluation by means of simulation. In this paper, we present a method for building a variate generator from service time experimental data. In order to build the variate generator, both real workloads and synthetic workloads may be used. The workload is used to feed the evaluated disk to obtain service time measurements. From experimental data we build a variate generator that fits the disk service times distribution. We also present a use case of our method, where we have obtained a relative error ranging from 0.45% to 1%.

A novel black-box simulation model methodology for predicting performance and energy consumption in commodity storage devices

Abstract Traditional approaches for storage devices simulation have been based on detailed and analytic models. However, analytic models are difficult to obtain and detailed models require a high computational cost which may be not affordable for large scale simulations (e.g. detailed data center simulations). In current systems like large clusters, grids, or clouds, performance and energy studies are critical, and fast simulations take an important role on them. A different approach is the black-box statistical modeling, where the storage device, its interface, and the interconnection mechanisms are modeled as a single stochastic process, defining the request response time as a random variable with an unknown distribution. A random variate generator can be built and integrated into a bigger simulation model. This approach allows to generate a simulation model for both real and synthetic complex workloads. This article describes a novel methodology that aims to build fast simulation models for storage devices. Our method uses as starting point a workload and produces a random variate generator which can be easily integrated into large scale simulation models. A comparison between our variate generator and the widely known simulation tool DiskSim, shows that our variate generator is faster, and can be as accurate as DiskSim for both performance and energy consumption predictions.

SIMCAN: a SIMulator framework for computer architectures and storage networks

This paper presents an OMNeT-based Framework to simulate large complex storage networks, with its corresponding underlying subsystems (I/O, Networking, etc.). With this Framework, custom environments can be configured and deployed on a flexible and easy way. The most interesting features of this Framework are its flexibility and scalability, so the simulation of distributed storage environments can be performed with the required detail level. Thus, we will able to study the behaviour of complex distributed environments to several purposes, like detecting system bottlenecks, calculating the scalability degree of the system or testing the performance of developed algorithms, without using a real system.

New Techniques for Modeling File Data Distribution on Storage Nodes

This paper presents a new probabilistic model which describes the way data blocks belonging to a certain file are distributed along the disk in general purpose systems. The distribution type is classified depending on the kind of file system, the file size and the disk occupancy ratio. The resulting algorithm will be used to simulate the access time to the data stored on a disk of an I/O node, where the position of data blocks determines the access time. In order to perform the modeling, some parameters have been estimated using some stress tests with different degrees of disk occupancy and file sizes on Ext2 and ReiserFS file systems.

Saving power in flash and disk hybrid storage system

This paper considers the question of saving energy in the disk drive making advantage of diverse devices in a hybrid storage system employing flash and disk drives. The flash and disk offer different power characteristics, being flash much less power consuming than the disk drive. We propose a technique that uses a flash device as a cache for a single disk device. We examine various options for managing the flash and disk devices in such a hybrid system and show that the proposed method saves energy in diverse scenarios. We implemented a simulator composed of disk and flash devices. This paper gives an overview of the design and evaluation of the proposed approach with the help of realistic workloads.

M-PLAT: Multi-Programming Language Adaptive Tutor

In this paper we introduce M-PLAT, an intelligent tutoring system for helping students to learn the basics of programming languages. In fact, the M-PLAT system represents a full collection of intelligent tutoring systems, and due to its modular and hierarchical architecture it can be upgraded to deal with a new programming language that is not yet included in the system. Thus, this tutoring system is not limited to a unique programming language, making M-PLAT a very scalable system. The best important feature of our system is that M-PLAT dynamically adapts itself to the learning style of each student, optimizing the learning time to each student.

Power saving-aware prefetching for SSD-based systems

Energy saving for computing systems has recently become an important and worrying need. Energy demand has been increasing in many systems, especially in data centers and supercomputers. This article considers the problem of saving energy on storage systems taking advantage of SSD drives. SSD and magnetic disk devices offer different power characteristics, being SSD drives much less power consuming than conventional magnetic disk drives.This paper presents the design and evaluation of a novel power consumption-aware prefetching mechanism for hybrid storage systems. The prefetching mechanism aims to reduce the power consumption of high performance storage subsystems. Every disk access request is absorbed by an associated SSD device, and only when the SSD device is full, requests are forwarded to the disk in background.We have evaluated the proposed approach with the help of both synthetic and realistic workloads. The experimental results demonstrate that our solution achieves significant reduction in energy consumption. Additionally, the performance evaluation shows that our solution may bring a substantial I/O performance benefit.

A Black Box Model for Storage Devices Based on Probability Distributions

Traditional approaches for storage devices simulation have been based on detailed analytical models. However, detailed models require detailed computations which may be not affordable for large scale simulations. Moreover, highly detailed models cannot be easily generalized. A different approach is the black-box statistical modeling, where the storage device, its interface, and the interconnection mechanisms are modeled as a single stochastic process, defining the request response time as a random variable with an unknown distribution. A random variate generator can be built and integrated into a simulation model. This approach allows to generate a simulation model for both real and synthetic workloads. This article describes a method suitable for building fast simulation models for storage devices. Our method uses as starting point a workload and produces a random variate generator which can be easily integrated into large scale simulation models. A comparison between our variate generator and the widely known simulation tool DiskSim, shows that our variate generator is faster, and can be as accurate as DiskSim.

Optimizing Distributed Architectures to Improve Performance on Checkpointing Applications

Nowadays, satisfying the global throughput targets of each application in High Performance Computing systems is a difficult task because of the high number of architectural configurations having a considerable impact on the overall system performance, such as the number of storage servers, features of the communication links, number of CPU cores per node, etc. In this paper we have performed a thorough study of the compared performance of scaling up HPC cluster architectures using a checkpointing application model. This study is specifically focused on multi-core HPC clusters and the scaling process is oriented towards the three main resources: computing power, communications and storage. The main goal of this work is to evaluate and analyze how evolves both scalability and bottlenecks existent on different HPC multi-core architectures using different architectural configurations. In order to achieve this goal, a set of simulation experiments has been achieved using a simulation framework, called SIMCAN, specifically designed for modeling and simulating HPC architectures. The results obtained show that the computing power is well suited thanks to the multi-core processors, while the problems are found on the storage and on the communications channels, being the storage network the main bottleneck.

A Power-Aware Based Storage Architecture for High Performance Computing

The energy crisis of the last years and the ever increasing conscience about the negative effects of energy waste on the climate change have brought the sustainability both into public attention, industry, and scientific scrutiny. Energy demand has been increasing in many subsystems, specially in data centers and supercomputers. This paper considers the problem of saving energy on storage systems taking advantage of SSD devices. SSDs and magnetic disk devices offer different power characteristics, being SSD devices much less power consuming than conventional magnetic disk devices. We propose a novel power saving solution based on SSD devices, namely SSD-PASS. Our storage system obtains benefits of permanent caching on SSDs in storage nodes. Nowadays we can find solutions that do not consider the viability and feasibility of the SSD-based storage systems, in terms of monetary cost. We present a cost analysis and evaluate our proposed architecture, in terms of saved energy and performance. Our cost model takes into account magnetic disk and SSD devices reliability metrics, current energy prices, and replacement costs. The experimental results demonstrate that our solution achieves a significant reduction in energy consumption and subsequent monetary savings by up to 66\%. We have evaluated the proposed approach with realistic workloads of three well-known HPC applications.