Fast Reconstruction for Degraded Reads and Recovery Process in Primary Array Storage Systems

RAID has been widely deployed in disk array storage systems to manage both performance and reliability simultaneously. RAID conducts two performance-critical operations during disk failures known as degraded reads/writes and recovery process. Before the recovery process is complete, reads and writes are degraded because data is reconstructed using data redundancy. The performance of degraded reads/writes is critical in order to meet stipulations in customer service level agreements (SLAs), and the recovery process affects the reliability of a storage system considerably. Both operations require fast data reconstruction. Among the erasure codes for fast reconstruction, Local Reconstruction Codes (LRC) are known to offer the best (or optimal) trade-off between storage overhead, fault tolerance, and the number of disks involved in reconstruction. Originally, LRC was designed for fast reconstruction in distributed cloud storage systems, in which network traffic is a major bottleneck during reconstruction. Thus, LRC focuses on reducing the number of disks involved in data reconstruction, which reduces network traffic. However, we observe that when LRC is applied to primary array storage systems, a major bottleneck in reconstruction results from uneven disk utilization. In other words, underutilized disks can no longer receive I/O requests as a result of the bottleneck of overloaded disks. Uneven disk utilization in LRC is due to its dedicated group partitioning policy to achieve the Maximally Recoverable property. In this paper, we present Distributed Reconstruction Codes (DRC) that support fast reconstruction in primary array storage systems. DRC is designed with group shuffling policy to solve the problem of uneven disk utilization. Experiments on real-world workloads show that DRC using global parity rotation (DRC-G) improves degraded performance by as much as 72% compared to RAID-6 and by as much as 35% compared to LRC under the same reliability. In addition, our study shows that DRC-G reduces the recovery process completion time by as much as 52% compared to LRC.1100Ysciescopu

Scalability of RAID systems

RAID systems (Redundant Arrays of Inexpensive Disks) have dominated backend storage systems for more than two decades and have grown continuously in size and complexity. Currently they face unprecedented challenges from data intensive applications such as image processing, transaction processing and data warehousing. As the size of RAID systems increases, designers are faced with both performance and reliability challenges. These challenges include limited back-end network bandwidth, physical interconnect failures, correlated disk failures and long disk reconstruction time. This thesis studies the scalability of RAID systems in terms of both performance and reliability through simulation, using a discrete event driven simulator for RAID systems (SIMRAID) developed as part of this project. SIMRAID incorporates two benchmark workload generators, based on the SPC-1 and Iometer benchmark specifications. Each component of SIMRAID is highly parameterised, enabling it to explore a large design space. To improve the simulation speed, SIMRAID develops a set of abstraction techniques to extract the behaviour of the interconnection protocol without losing accuracy. Finally, to meet the technology trend toward heterogeneous storage architectures, SIMRAID develops a framework that allows easy modelling of different types of device and interconnection technique. Simulation experiments were first carried out on performance aspects of scalability. They were designed to answer two questions: (1) given a number of disks, which factors affect back-end network bandwidth requirements; (2) given an interconnection network, how many disks can be connected to the system. The results show that the bandwidth requirement per disk is primarily determined by workload features and stripe unit size (a smaller stripe unit size has better scalability than a larger one), with cache size and RAID algorithm having very little effect on this value. The maximum number of disks is limited, as would be expected, by the back-end network bandwidth. Studies of reliability have led to three proposals to improve the reliability and scalability of RAID systems. Firstly, a novel data layout called PCDSDF is proposed. PCDSDF combines the advantages of orthogonal data layouts and parity declustering data layouts, so that it can not only survivemultiple disk failures caused by physical interconnect failures or correlated disk failures, but also has a good degraded and rebuild performance. The generating process of PCDSDF is deterministic and time-efficient. The number of stripes per rotation (namely the number of stripes to achieve rebuild workload balance) is small. Analysis shows that the PCDSDF data layout can significantly improve the system reliability. Simulations performed on SIMRAID confirm the good performance of PCDSDF, which is comparable to other parity declustering data layouts, such as RELPR. Secondly, a system architecture and rebuilding mechanism have been designed, aimed at fast disk reconstruction. This architecture is based on parity declustering data layouts and a disk-oriented reconstruction algorithm. It uses stripe groups instead of stripes as the basic distribution unit so that it can make use of the sequential nature of the rebuilding workload. The design space of system factors such as parity declustering ratio, chunk size, private buffer size of surviving disks and free buffer size are explored to provide guidelines for storage system design. Thirdly, an efficient distributed hot spare allocation and assignment algorithm for general parity declustering data layouts has been developed. This algorithm avoids conflict problems in the process of assigning distributed spare space for the units on the failed disk. Simulation results show that it effectively solves the write bottleneck problem and, at the same time, there is only a small increase in the average response time to user requests

What broke where for distributed and parallel applications — a whodunit story

Detection, diagnosis and mitigation of performance problems in today\u27s large-scale distributed and parallel systems is a difficult task. These large distributed and parallel systems are composed of various complex software and hardware components. When the system experiences some performance or correctness problem, developers struggle to understand the root cause of the problem and fix in a timely manner. In my thesis, I address these three components of the performance problems in computer systems. First, we focus on diagnosing performance problems in large-scale parallel applications running on supercomputers. We developed techniques to localize the performance problem for root-cause analysis. Parallel applications, most of which are complex scientific simulations running in supercomputers, can create up to millions of parallel tasks that run on different machines and communicate using the message passing paradigm. We developed a highly scalable and accurate automated debugging tool called PRODOMETER, which uses sophisticated algorithms to first, create a logical progress dependency graph of the tasks to highlight how the problem spread through the system manifesting as a system-wide performance issue. Second, uses this logical progress dependence graph to identify the task where the problem originated. Finally, PRODOMETER pinpoints the code region corresponding to the origin of the bug. Second, we developed a tool-chain that can detect performance anomaly using machine-learning techniques and can achieve very low false positive rate. Our input-aware performance anomaly detection system consists of a scalable data collection framework to collect performance related metrics from different granularity of code regions, an offline model creation and prediction-error characterization technique, and a threshold based anomaly-detection-engine for production runs. Our system requires few training runs and can handle unknown inputs and parameter combinations by dynamically calibrating the anomaly detection threshold according to the characteristics of the input data and the characteristics of the prediction-error of the models. Third, we developed performance problem mitigation scheme for erasure-coded distributed storage systems. Repair operations of the failed blocks in erasure-coded distributed storage system take really long time in networked constrained data-centers. The reason being, during the repair operation for erasure-coded distributed storage, a lot of data from multiple nodes are gathered into a single node and then a mathematical operation is performed to reconstruct the missing part. This process severely congests the links toward the destination where newly recreated data is to be hosted. We proposed a novel distributed repair technique, called Partial-Parallel-Repair (PPR) that performs this reconstruction in parallel on multiple nodes and eliminates network bottlenecks, and as a result, greatly speeds up the repair process. Fourth, we study how for a class of applications, performance can be improved (or performance problems can be mitigated) by selectively approximating some of the computations. For many applications, the main computation happens inside a loop that can be logically divided into a few temporal segments, we call phases. We found that while approximating the initial phases might severely degrade the quality of the results, approximating the computation for the later phases have very small impact on the final quality of the result. Based on this observation, we developed an optimization framework that for a given budget of quality-loss, would find the best approximation settings for each phase in the execution

Image reconstruction using Wiener filtering and unsharp masking: a computer model

Research was conducted to computer model and compare the image reconstruction obtainable using Wiener filtering and unsharp masking. Wiener filtering and unsharp masking are techniques used to improve image quality and interpretation. It was demonstrated that far greater image restoration is obtained by Wiener filter than by unsharp masking because unsharp masking, unlike Wiener filtering, enhanced image noise along with the edges. A user friendly computer model, that may be used as a tutorial aid for Image Science students, incorporating Fast Fourier Transform (FFT) techniques was designed. Graphics allow the user to follow each stage of the image processing