Exploiting Workload Characteristics and Service Diversity to Improve the Availability of Cloud Storage Systems

With the increasing utilization and popularity of the cloud infrastructure, more and more data are moved to the cloud storage systems. This makes the availability of cloud storage services critically important, particularly given the fact that outages of cloud storage services have indeed happened from time to time. Thus, solely depending on a single cloud storage provider for storage services can risk violating the service-level agreement (SLA) due to the weakening of service availability. This has led to the notion of Cloud-of-Clouds, where data redundancy is introduced to distribute data among multiple independent cloud storage providers, to address the problem. The key in the effectiveness of the Cloud-of-Clouds approaches lies in how the data redundancy is incorporated and distributed among the clouds. However, the existing Cloud-of-Clouds approaches utilize either replication or erasure codes to redundantly distribute data across multiple clouds, thus incurring either high space or high performance overheads. In this paper, we propose a hybrid redundant data distribution approach, called HyRD, to improve the cloud storage availability in Cloud-of-Clouds by exploiting the workload characteristics and the diversity of cloud providers. In HyRD, large files are distributed in multiple cost-efficient cloud storage providers with erasure-coded data redundancy while small files and file system metadata are replicated on multiple high-performance cloud storage providers. The experiments conducted on our lightweight prototype implementation of HyRD show that HyRD improves the cost efficiency by 33.4 and 20.4 percent, and reduces the access latency by 58.7 and 34.8 percent than the DuraCloud and RACS schemes, respectively.

[1]  Hong Jiang,et al.  Proactive Data Migration for Improved Storage Availability in Large-Scale Data Centers , 2015, IEEE Transactions on Computers.

[2]  Hong Jiang,et al.  Improving Storage Availability in Cloud-of-Clouds with Hybrid Redundant Data Distribution , 2015, 2015 IEEE International Parallel and Distributed Processing Symposium.

[3]  Jacob R. Lorch,et al.  A five-year study of file-system metadata , 2007, TOS.

[4]  Hong Jiang,et al.  WorkOut: I/O Workload Outsourcing for Boosting RAID Reconstruction Performance , 2009, FAST.

[5]  冯海超 Windows Azure:微软押上未来 , 2012 .

[6]  Kannan Ramchandran,et al.  A “Hitchhiker’s” Guide to Fast and Efficient Data Reconstruction in Erasure-coded Data Centers , 2014 .

[7]  Gang Huang,et al.  Towards a Model-Defined Cloud-of-Clouds , 2015, 2015 IEEE Conference on Collaboration and Internet Computing (CIC).

[8]  Hakim Weatherspoon,et al.  RACS: a case for cloud storage diversity , 2010, SoCC '10.

[9]  Cheng Huang,et al.  Rethinking erasure codes for cloud file systems: minimizing I/O for recovery and degraded reads , 2012, FAST.

[10]  Masaru Kitsuregawa,et al.  Hot mirroring: a method of hiding parity update penalty and degradation during rebuilds for RAID5 , 1996, SIGMOD '96.

[11]  Ari Juels,et al.  HAIL: a high-availability and integrity layer for cloud storage , 2009, CCS.

[12]  Hong Jiang,et al.  HPDA: A hybrid parity-based disk array for enhanced performance and reliability , 2010, 2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS).

[13]  Karsten Schwan,et al.  Six degrees of scientific data: reading patterns for extreme scale science IO , 2011, HPDC '11.

[14]  Sean D Dessureault,et al.  Understanding big data , 2016 .

[15]  Mingqiang Li,et al.  CDStore: Toward Reliable, Secure, and Cost-Efficient Cloud Storage via Convergent Dispersal , 2015, IEEE Internet Computing.

[16]  Randy H. Katz,et al.  Above the Clouds: A Berkeley View of Cloud Computing , 2009 .

[17]  Randy H. Katz,et al.  A case for redundant arrays of inexpensive disks (RAID) , 1988, SIGMOD '88.

[18]  Hong Jiang,et al.  Improving Availability of RAID-Structured Storage Systems by Workload Outsourcing , 2011, IEEE Transactions on Computers.

[19]  Nikolai Joukov,et al.  A nine year study of file system and storage benchmarking , 2008, TOS.

[20]  WilkesJohn,et al.  The HP AutoRAID hierarchical storage system , 1996 .

[21]  Kannan Ramchandran,et al.  A Solution to the Network Challenges of Data Recovery in Erasure-coded Distributed Storage Systems: A Study on the Facebook Warehouse Cluster , 2013, HotStorage.

[22]  Yang Tang,et al.  NCCloud: applying network coding for the storage repair in a cloud-of-clouds , 2012, FAST.

[23]  Garth A. Gibson,et al.  DiskReduce: RAID for data-intensive scalable computing , 2009, PDSW '09.

[24]  Suman Banerjee,et al.  An ensemble of replication and erasure codes for cloud file systems , 2013, 2013 Proceedings IEEE INFOCOM.

[25]  Miguel Correia,et al.  DepSky: Dependable and Secure Storage in a Cloud-of-Clouds , 2013, TOS.

[26]  Arnab Banerjee,et al.  An Energy and Performance Exploration of Network-on-Chip Architectures , 2009, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[27]  Huaqun Wang,et al.  Identity-Based Distributed Provable Data Possession in Multicloud Storage , 2015, IEEE Transactions on Services Computing.

[28]  Dan Dobre,et al.  Hybris: Robust Hybrid Cloud Storage , 2014, SoCC.

[29]  Tao Li,et al.  Towards Lightweight and Swift Storage Resource Management in Big Data Cloud Era , 2015, ICS.

[30]  Yang Wang,et al.  Gnothi: Separating Data and Metadata for Efficient and Available Storage Replication , 2012, USENIX Annual Technical Conference.

[31]  ZadokErez,et al.  A nine year study of file system and storage benchmarking , 2008 .

[32]  Hong Jiang,et al.  POD: Performance Oriented I/O Deduplication for Primary Storage Systems in the Cloud , 2014, 2014 IEEE 28th International Parallel and Distributed Processing Symposium.

[33]  Hong Jiang,et al.  IDO: Intelligent Data Outsourcing with Improved RAID Reconstruction Performance in Large-Scale Data Centers , 2012, LISA.

[34]  Qi Zhang,et al.  A New Disk I/O Model of Virtualized Cloud Environment , 2013, IEEE Transactions on Parallel and Distributed Systems.

[35]  Hong Jiang,et al.  Read-Performance Optimization for Deduplication-Based Storage Systems in the Cloud , 2014, TOS.