An official website of the United States government
The prevalence of disaggregated storage in public clouds has led to increased latency in modern OLAP cloud databases, particularly when handling ad-hoc and highly-selective queries on large objects. To address this, cloud databases have adopted computation pushdown, executing query predicates closer to the storage layer. However, existing pushdown solutions are ine!cient in erasure-coded storage. Cloud storage employs erasure coding that partitions analytics file objects into fixed-sized blocks and distributes them across storage nodes. Consequently, when a speci"c part of the object is queried, the storage system must reassemble the object across nodes, incurring significant network latency. In this work, we present Fusion, an object store for analytics that is optimized for query pushdown on erasure-coded data. It co-designs its erasure coding and file placement topologies, taking into account popular analytics file formats (e.g., Parquet). Fusion employs a novel stripe construction algorithm that prevents fragmentation of computable units within an object, and minimizes storage overhead during erasure coding. Compared to existing erasure-coded stores, Fusion improves median and tail latency by 64% and 81%, respectively, on TPC-H, and up to 40% and 48% respectively, on real-world SQL queries. Fusion achieves this while incurring a modest 1.2% storage overhead compared to the optimal.
more »
« less
EC-Fusion: An Efficient Hybrid Erasure Coding Framework to Improve Both Application and Recovery Performance in Cloud Storage Systems
Nowadays erasure coding is one of the most significant techniques in cloud storage systems, which provides both quick parallel I/O processing and high capabilities of fault tolerance on massive data accesses. In these systems, triple disk failure tolerant arrays (3DFTs) is a typical configuration, which is supported by several classic erasure codes like Reed-Solomon (RS) codes, Local Reconstruction Codes (LRC), Minimum Storage Regeneration (MSR) codes, etc. For an online recovery process, the foreground application workloads and the background recovery workloads are handled simultaneously, which requires a comprehensive understanding on both two types of workload characteristics. Although several techniques have been proposed to accelerate the I/O requests of online recovery processes, they are typically unilateral due to the fact that the above two workloads are not combined together to achieve high cost-effective performance.To address this problem, we propose Erasure Codes Fusion (EC-Fusion), an efficient hybrid erasure coding framework in cloud storage systems. EC-Fusion is a combination of RS and MSR codes, which dynamically selects the appropriate code based on its properties. On one hand, for write-intensive application workloads or low risk on data loss in recovery workloads, EC-Fusion uses RS code to decrease the computational overhead and storage cost concurrently. On the other hand, for read-intensive or frequent reconstruction in workloads, MSR code is a proper choice. Therefore, a better overall application and recovery performance can be achieved in a cost-effective fashion. To demonstrate the effectiveness of EC-Fusion, several experiments are conducted in hadoop systems. The results show that, compared with the traditional hybrid erasure coding techniques, EC-Fusion accelerates the response time for application by up to 1.77×, and reduces the reconstruction time by up to 69.10%.
more »
« less
- Award ID(s):
- 1813081
- PAR ID:
- 10176067
- Date Published:
- Journal Name:
- 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
- Page Range / eLocation ID:
- 191 to 201
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Cloud storage systems generally add redundancy in storing content files such that K files are replicated or erasure coded and stored on N > K nodes. In addition to providing reliability against failures, the redundant copies can be used to serve a larger volume of content access requests. A request for one of the files can be either be sent to a systematic node, or one of the repair groups. In this paper, we seek to maximize the service capacity region, that is, the set of request arrival rates for the K files that can be supported by a coded storage system. We explore two aspects of this problem: 1) for a given erasure code, how to optimally split incoming requests between systematic nodes and repair groups, and 2) choosing an underlying erasure code that maximizes the achievable service capacity region. In particular, we consider MDS and Simplex codes. Our analysis demonstrates that erasure coding makes the system more robust to skews in file popularity than simply replicating a file at multiple servers, and that coding and replication together can make the capacity region larger than either alone.more » « less
-
null (Ed.)Modern hybrid cloud infrastructures require software to be easily portable between heterogeneous clusters. Application containerization is a proven technology to provide this portability for the functionalities of an application. However, to ensure performance portability, dependable verification of a cluster's performance under realistic workloads is required. Such verification is usually achieved through benchmarking the target environment and its storage in particular, as I/O is often the slowest component in an application. Alas, existing storage benchmarks are not suitable to generate cloud native workloads as they do not generate any storage control operations (e.g., volume or snapshot creation), cannot easily orchestrate a high number of simultaneously running distinct workloads, and are limited in their ability to dynamically change workload characteristics during a run. In this paper, we present the design and prototype for the first-ever Cloud Native Storage Benchmark—CNSBench. CNSBench treats control operations as first-class citizens and allows to easily combine traditional storage benchmark workloads with user-defined control operation workloads. As CNSBench is a cloud native application itself, it natively supports orchestration of different control and I/O workload combinations at scale. We built a prototype of CNSBench for Kubernetes, leveraging several existing containerized storage benchmarks for data and metadata I/O generation. We demonstrate CNSBench's usefulness with case studies of Ceph and OpenEBS, two popular storage providers for Kubernetes, uncovering and analyzing previously unknown performance characteristics.more » « less
-
Cloud storage systems often use state machine replication (SMR) to ensure reliability and availability. Erasure coding has recently been integrated with SMR to reduce disk and network I/O costs. This brief announcement shares our experience in developing a leaderless erasure coding SMR system. We integrate our system Racos with etcd, a distributed key-value storage that powers Kubernetes. Racos outperforms competitors by up to 3.36x in throughput.more » « less
-
The dramatic increase in the scale of current and planned high-end HPC systems is leading new challenges, such as the growing costs of data movement and IO, and the reduced mean times between failures (MTBF) of system components. In- situ workflows, i.e., executing the entire application workflows on the HPC system, have emerged as an attractive approach to address data-related challenges by moving computations closer to the data, and staging-based frameworks have been effectively used to support in-situ workflows at scale. However, the resilience of these staging-based solutions has not been addressed and they remain susceptible to expensive data failures. Furthermore, naive use of data resilience techniques such as n-way replication and erasure codes can impact latency and/or result in significant storage overheads. In this paper, we present CoREC, a scalable resilient in-memory data staging runtime for large-scale in-situ workflows. CoREC uses a novel hybrid approach that combines dynamic replication with erasure coding based on data access patterns. The paper also presents optimizations for load balancing and conflict avoiding encoding, and a low overhead, lazy data recovery scheme. We have implemented the CoREC runtime and have deployed with the DataSpaces staging service on Titan at ORNL, and present an experimental evaluation in the paper. The experiments demonstrate that CoREC can tolerate in-memory data failures while maintaining low latency and sustaining high overall storage efficiency at large scales.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IPDPS47924.2020.00029
