CORE: Augmenting Regenerating-Coding-Based Recovery for Single and Concurrent Failures in Distributed Storage Systems

Data availability is critical in distributed storage systems, especially when node failures are prevalent in real life. A key requirement is to minimize the amount of data transferred among nodes when recovering the lost or unavailable data of failed nodes. This paper explores recovery solutions based on regenerating codes, which are shown to provide fault-tolerant storage and minimum recovery bandwidth. Existing optimal regenerating codes are designed for single node failures. We build a system called CORE, which augments existing optimal regenerating codes to support a general number of failures including single and concurrent failures. We theoretically show that CORE achieves the minimum possible recovery bandwidth for most cases. We implement CORE and evaluate our prototype atop a Hadoop HDFS cluster testbed with up to 20 storage nodes. We demonstrate that our CORE prototype conforms to our theoretical findings and achieves recovery bandwidth saving when compared to the conventional recovery approach based on erasure codes.Comment: 25 page

Efficient Striping Techniques for Variable Bit Rate Continuous Media File Servers

The performance of striped disk arrays is governed by two parameters: the stripe unit size and the degree of striping. In this paper, we describe techniques for determining the stripe unit size and degree of striping for disk arrays storing variable bit rate continuous media data. We present an analytical model that uses the server configuration and the workload characteristics to predict the load on the most heavily loaded disk in redundant and non-redundant arrays. We then use the model to determine the optimal stripe unit size for different workloads. We also use the model to study the effect of various system parameters on the optimal stripe unit size. To determine the degree of striping, we first demonstrate that striping a continuous media stream across all disks in the array causes the number of clients supported to increase sub-linearly with increase in the number of disks. To maximize the number of clients supported in large arrays, we propose a technique that partitions a disk array and stripes each media stream across a single partition. Since load imbalance can occur in such partitioned arrays, we present an analytical model to compute the imbalance across partitions in the array. We then use the model to determine a partition size that minimizes the load imbalance, and hence, maximizes the number of clients supported by the array

A Case for Redundant Arrays of Hybrid Disks (RAHD)

Hybrid Hard Disk Drive was originally concepted by Samsung, which incorporates a Flash memory in a magnetic disk. The combined ultra-high-density benefits of magnetic storage and the low-power and fast read access of NAND technology inspires us to construct Redundant Arrays of Hybrid Disks (RAHD) to offer a possible alternative to today’s Redundant Arrays of Independent Disks (RAIDs) and/or Massive Arrays of Idle Disks (MAIDs). We first design an internal management system (including Energy-Efficient Control) for hybrid disks. Three traces collected from real systems as well as a synthetic trace are then used to evaluate the RAHD arrays. The trace-driven experimental results show: in the high speed mode, a RAHD outplays the purely-magnetic-disk-based RAIDs by a factor of 2.4–4; in the energy-efficient mode, a RAHD4/5 can save up to 89% of energy at little performance degradationPeer reviewe

Data partitioning and load balancing in parallel disk systems

Parallel disk systems provide opportunities for exploiting I/O parallelism in two possible ways, namely via inter-request and intra-request parallelism. In this paper we discuss the main issues in performance tuning of such systems, namely striping and load balancing, and show their relationship to response time and throughput. We outline the main components of an intelligent file system that optimizes striping by taking into account the requirements of the applications, and performs load balancing by judicious file allocation and dynamic redistributions of the data when access patterns change. Our system uses simple but effective heuristics that incur only little overhead. We present performance experiments based on synthetic workloads and real-life traces

RAIDX: RAID EXTENDED FOR HETEROGENEOUS ARRAYS

The computer hard drive market has diversified with the establishment of solid state disks (SSDs) as an alternative to magnetic hard disks (HDDs). Each hard drive technology has its advantages: the SSDs are faster than HDDs but the HDDs are cheaper. Our goal is to construct a parallel storage system with HDDs and SSDs such that the parallel system is as fast as the SSDs. Achieving this goal is challenging since the slow HDDs store more data and become bottlenecks, while the SSDs remain idle. RAIDX is a parallel storage system designed for disks of different speeds, capacities and technologies. The RAIDX hardware consists of an array of disks; the RAIDX software consists of data structures and algorithms that allow the disks to be viewed as a single storage unit that has capacity equal to the sum of the capacities of its disks, failure rate lower than the failure rate of its individual disks, and speeds close to that of its faster disks. RAIDX achieves its performance goals with the aid of its novel parallel data organization technique that allows storage data to be moved on the fly without impacting the upper level file system. We show that storage data accesses satisfy the locality of reference principle, whereby only a small fraction of storage data are accessed frequently. RAIDX has a monitoring program that identifies frequently accessed blocks and a migration program that moves frequently accessed blocks to faster disks. The faster disks are caches that store the solo copy of frequently accessed data. Experimental evaluation has shown that a HDD+SSD RAIDX array is as fast as an all-SSD array when the workload shows locality of reference

Alpha Entanglement Codes: Practical Erasure Codes to Archive Data in Unreliable Environments

Data centres that use consumer-grade disks drives and distributed peer-to-peer systems are unreliable environments to archive data without enough redundancy. Most redundancy schemes are not completely effective for providing high availability, durability and integrity in the long-term. We propose alpha entanglement codes, a mechanism that creates a virtual layer of highly interconnected storage devices to propagate redundant information across a large scale storage system. Our motivation is to design flexible and practical erasure codes with high fault-tolerance to improve data durability and availability even in catastrophic scenarios. By flexible and practical, we mean code settings that can be adapted to future requirements and practical implementations with reasonable trade-offs between security, resource usage and performance. The codes have three parameters. Alpha increases storage overhead linearly but increases the possible paths to recover data exponentially. Two other parameters increase fault-tolerance even further without the need of additional storage. As a result, an entangled storage system can provide high availability, durability and offer additional integrity: it is more difficult to modify data undetectably. We evaluate how several redundancy schemes perform in unreliable environments and show that alpha entanglement codes are flexible and practical codes. Remarkably, they excel at code locality, hence, they reduce repair costs and become less dependent on storage locations with poor availability. Our solution outperforms Reed-Solomon codes in many disaster recovery scenarios.Comment: The publication has 12 pages and 13 figures. This work was partially supported by Swiss National Science Foundation SNSF Doc.Mobility 162014, 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN