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.
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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%.
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- Award ID(s):
- 1813081
- NSF-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
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IPDPS47924.2020.00029
