Erasure codes are an integral part of many distributed storage systems aimed at Big Data, since they provide high fault-tolerance for low overheads. However, traditional erasure codes are inefficient on reading stored data in degraded environments (when nodes might be unavailable), and on replenishing lost data (vital for long term resilience). Consequently, novel codes optimized to cope with distributed storage system nuances are vigorously being researched. In this paper, we take an engineering alternative, exploring the use of simple and mature techniques ‐ juxtaposing a standard erasure code with RAID4 like parity. We carry out an analytical study to determine the efficacy of this approach over traditional as well as some novel codes. We build upon this study to design CORE, a general storage primitive that we integrate into HDFS. We benchmark this implementation in a proprietary cluster and in EC2. Our experiments show that compared to traditional erasure codes, CORE uses 50 less bandwidth and is up to 75 faster while recovering a single failed node, while the gains are respectively 15 and 60 for double node failures.
Despite the plethora of works investigating novel erasure codes, most existing distributed file systems using erasure codes do so by adapting traditional erasure codes. Microsoft's Windows Azure Storage @cite_10 is a prominent exception which uses an optimized version of Pyramid codes @cite_18 called Local Reconstruction Code (LRC) @cite_14 . Some recent academic prototypes - NCFS @cite_5 and @cite_19 Coincidentally, @cite_19 uses the same name, CORE, for co llaborative re generation. likewise explore the feasibility of applying network coding techniques for repairing lost data. The latter systems do not address the issue of degraded reads. In contrast to these systems based on proprietary and novel erasure coding techniques with significant system design complexity, CORE composes two mature techniques (standard erasure codes and RAID-4 like parity) while achieving very good repairability and degraded read performance. This makes CORE suitable for ready integration with many block based storage file systems, and its simple design makes it amenable to third party reimplementations.
@cite_18 @cite_14 @cite_19 @cite_5 @cite_10
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Distributed storage systems are increasingly transitioning to the use of erasure codes since they offer higher reliability at significantly lower storage costs than data replication. However, these codes tradeoff recovery performance as they require multiple disk reads and network transfers for reconstructing an unavailable data block. As a result, most existing systems use an erasure code either optimized for storage overhead or recovery performance. In this paper, we present HACFS, a new erasure-coded storage system that instead uses two different erasure codes and dynamically adapts to workload changes. It uses a fast code to optimize for recovery performance and a compact code to reduce the storage overhead. A novel conversion mechanism is used to efficiently upcode and downcode data blocks between fast and compact codes. We show that HACFS design techniques are generic and successfully apply it to two different code families: Product and LRC codes. We have implemented HACFS as an extension to the Hadoop Distributed File System (HDFS) and experimentally evaluate it with five different workloads from production clusters. The HACFS system always maintains a low storage overhead and significantly improves the recovery performance as compared to three popular single-code storage systems. It reduces the degraded read latency by up to 46 , and the reconstruction time and disk network traffic by up to 45 . Erasure codes are an integral part of many distributed storage systems aimed at Big Data, since they provide high fault-tolerance for low overheads. However, traditional erasure codes are inefficient on reading stored data in degraded environments (when nodes might be unavailable), and on replenishing lost data (vital for long term resilience). Consequently, novel codes optimized to cope with distributed storage system nuances are vigorously being researched. In this paper, we take an engineering alternative, exploring the use of simple and mature techniques ‐ juxtaposing a standard erasure code with RAID4 like parity. We carry out an analytical study to determine the efficacy of this approach over traditional as well as some novel codes. We build upon this study to design CORE, a general storage primitive that we integrate into HDFS. We benchmark this implementation in a proprietary cluster and in EC2. Our experiments show that compared to traditional erasure codes, CORE uses 50 less bandwidth and is up to 75 faster while recovering a single failed node, while the gains are respectively 15 and 60 for double node failures. Petabyte-scale distributed storage systems are currently transitioning to erasure codes to achieve higher storage efficiency. Classical codes like Reed-Solomon are highly suboptimal for distributed environments due to their high overhead in single-failure events. Locally Repairable Codes (LRCs) form a new family of codes that are repair efficient. In particular, LRCs minimize the number of nodes participating in single node repairs during which they generate small network traffic. Two large-scale distributed storage systems have already implemented different types of LRCs: Windows Azure Storage and the Hadoop Distributed File System RAID used by Facebook. The fundamental bounds for LRCs, namely the best possible distance for a given code locality, were recently discovered, but few explicit constructions exist. In this work, we present an explicit and simple to implement construction of optimal LRCs, for code parameters previously established by existence results. For the analysis of the optimality of our code, we derive a new result on the matroid represented by the code's generator matrix. Petabyte-scale distributed storage systems are currently transitioning to erasure codes to achieve higher storage efficiency. Classical codes like Reed-Solomon are highly sub-optimal for distributed environments due to their high overhead in single-failure events. Locally Repairable Codes (LRCs) form a new family of codes that are repair efficient. In particular, LRCs minimize the number of nodes participating in single node repairs during which they generate small network traffic. Two large-scale distributed storage systems have already implemented different types of LRCs: Windows Azure Storage and the Hadoop Distributed File System RAID used by Facebook. The fundamental bounds for LRCs, namely the best possible distance for a given code locality, were recently discovered, but few explicit constructions exist. In this work, we present an explicit and optimal LRCs that are simple to construct. Our construction is based on grouping Reed-Solomon (RS) coded symbols to obtain RS coded symbols over a larger finite field. We then partition these RS symbols in small groups, and re-encode them using a simple local code that offers low repair locality. For the analysis of the optimality of the code, we derive a new result on the matroid represented by the code generator matrix.