Popularity-Aware Multi-Failure Resilient and Cost-Effective Replication for High Data Durability in Cloud Storage

Large-scale data stores are an increasingly important component of cloud datacenter services. However, cloud storage system usually experiences data loss, hindering data durability. Three-way random replication is commonly used to lead better data durability in cloud storage systems. However, three-way random replication cannot effectively handle correlated machine failures to prevent data loss. Although Copyset Replication and Tiered Replication can reduce data loss in correlated and independent failures, and enhance data durability, they fail to leverage different data popularities to substantially reduce the storage cost and bandwidth cost caused by replication. To address these issues, we present a popularity-aware multi-failure resilient and cost-effective replication (PMCR) scheme for high data durability in cloud storage. PMCR splits the cloud storage system into primary tier and backup tier, and classifies data into hot data, warm data and cold data based on data popularities. To handle both correlated and independent failures, PMCR stores the three replicas of the same data into one Copyset formed by two servers in the primary tier and one server in the backup tier. For the third replicas of warm data and cold data in the backup tier, PMCR uses the compression methods to reduce storage cost and bandwidth cost. Extensive numerical results based on trace parameters and experimental results from real-world Amazon S3 show that PMCR achieves high data durability, low probability of data loss, and low storage cost and bandwidth cost compared to previous replication schemes.

[1]  Anne-Marie Kermarrec,et al.  Archiving cold data in warehouses with clustered network coding , 2014, EuroSys '14.

[2]  Robbert van Renesse,et al.  Chain Replication for Supporting High Throughput and Availability , 2004, OSDI.

[3]  P. Östergård,et al.  There exists no (15,5,4) RBIBD † * , 2001 .

[4]  Ming Zhao,et al.  Client-side Flash Caching for Cloud Systems , 2014, SYSTOR 2014.

[5]  Van-Anh Truong,et al.  Availability in Globally Distributed Storage Systems , 2010, OSDI.

[6]  Jin Li,et al.  Reducing replication bandwidth for distributed document databases , 2015, SoCC.

[7]  Kang Chen,et al.  DSearching: Distributed searching of mobile nodes in DTNs with floating mobility information , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[8]  Hong Jiang,et al.  SiLo: A Similarity-Locality based Near-Exact Deduplication Scheme with Low RAM Overhead and High Throughput , 2011, USENIX Annual Technical Conference.

[9]  Karl Aberer,et al.  A self-organized, fault-tolerant and scalable replication scheme for cloud storage , 2010, SoCC '10.

[10]  Veena Rawat,et al.  Reducing Failure Probability of cloud storage services using Multi-Clouds , 2013, ArXiv.

[11]  Hong Jiang,et al.  A Scalable Inline Cluster Deduplication Framework for Big Data Protection , 2012, Middleware.

[12]  Seung-won Hwang,et al.  Scalable Load Balancing in Cluster Storage Systems , 2011, Middleware.

[13]  Haitao Wu,et al.  CubicRing: Enabling One-Hop Failure Detection and Recovery for Distributed In-Memory Storage Systems , 2015, NSDI.

[14]  Ju Wang,et al.  Windows Azure Storage: a highly available cloud storage service with strong consistency , 2011, SOSP.

[15]  Karl Aberer,et al.  Autonomic SLA-Driven Provisioning for Cloud Applications , 2011, 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.

[16]  Indranil Gupta,et al.  Popular is cheaper: curtailing memory costs in interactive analytics engines , 2018, EuroSys.

[17]  S. Houghten,et al.  There is no (46, 6, 1) block design* , 2001 .

[18]  Michael Dahlin,et al.  TAPER: tiered approach for eliminating redundancy in replica synchronization , 2005, FAST'05.

[19]  Indranil Gupta,et al.  Making cloud intermediate data fault-tolerant , 2010, SoCC '10.

[20]  Andreas Haeberlen,et al.  Glacier: highly durable, decentralized storage despite massive correlated failures , 2005, NSDI.

[21]  Murali S. Kodialam,et al.  Frugal storage for cloud file systems , 2012, EuroSys '12.

[22]  Giridhar Appaji Nag Yasa,et al.  Space savings and design considerations in variable length deduplication , 2012, OPSR.

[23]  Robert J. Chansler,et al.  Data Availability and Durability with the Hadoop Distributed File System , 2012, login Usenix Mag..

[24]  Ming Zhong,et al.  Replication degree customization for high availability , 2008, Eurosys '08.

[25]  Mendel Rosenblum,et al.  Fast crash recovery in RAMCloud , 2011, SOSP.

[26]  Ethan L. Miller,et al.  Purity: Building Fast, Highly-Available Enterprise Flash Storage from Commodity Components , 2015, SIGMOD Conference.

[27]  Srinivasan Seshan,et al.  Subtleties in Tolerating Correlated Failures in Wide-area Storage Systems , 2006, NSDI.

[28]  Jeffrey Dean,et al.  Evolution and future directions of large-scale storage and computation systems at Google , 2010, SoCC '10.

[29]  Haiying Shen,et al.  A Low-Cost Multi-failure Resilient Replication Scheme for High Data Availability in Cloud Storage , 2016, 2016 IEEE 23rd International Conference on High Performance Computing (HiPC).

[30]  Fred Douglis,et al.  Migratory compression: coarse-grained data reordering to improve compressibility , 2014, FAST.

[31]  Salvador A. Gezan,et al.  Statistical Methods in Biology: Design and Analysis of Experiments and Regression , 2014 .

[32]  Hairong Kuang,et al.  The Hadoop Distributed File System , 2010, 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST).

[33]  Xin Pan,et al.  Database high availability using SHADOW systems , 2015, SoCC.

[34]  Albert G. Greenberg,et al.  Scarlett: coping with skewed content popularity in mapreduce clusters , 2011, EuroSys '11.

[35]  Raju Rangaswami,et al.  I/O Deduplication: Utilizing content similarity to improve I/O performance , 2010, TOS.

[36]  Burton H. Bloom,et al.  Space/time trade-offs in hash coding with allowable errors , 1970, CACM.

[37]  João Leitão,et al.  ChainReaction: a causal+ consistent datastore based on chain replication , 2013, EuroSys '13.

[38]  Robbert van Renesse,et al.  Leveraging sharding in the design of scalable replication protocols , 2013, SoCC.

[39]  GhemawatSanjay,et al.  The Google file system , 2003 .

[40]  Werner Vogels,et al.  Dynamo: amazon's highly available key-value store , 2007, SOSP.

[41]  Ben Y. Zhao,et al.  Exploiting locality of interest in online social networks , 2010, CoNEXT.

[42]  Komal Shringare,et al.  Apache Hadoop Goes Realtime at Facebook , 2015 .

[43]  Ben Y. Zhao,et al.  OceanStore: an architecture for global-scale persistent storage , 2000, SIGP.

[44]  Muriel Medard,et al.  How good is random linear coding based distributed networked storage , 2005 .

[45]  Antony I. T. Rowstron,et al.  Pelican: A Building Block for Exascale Cold Data Storage , 2014, OSDI.

[46]  Sachin Katti,et al.  Copysets: Reducing the Frequency of Data Loss in Cloud Storage , 2013, USENIX Annual Technical Conference.

[47]  Jian Yang,et al.  Mojim: A Reliable and Highly-Available Non-Volatile Memory System , 2015, ASPLOS.

[48]  Emin Gün Sirer,et al.  Tiered Replication: A Cost-effective Alternative to Full Cluster Geo-replication , 2015, USENIX Annual Technical Conference.

[49]  Philip Shilane,et al.  WAN-optimized replication of backup datasets using stream-informed delta compression , 2012, TOS.

[50]  Andreas Haeberlen,et al.  Efficient Replica Maintenance for Distributed Storage Systems , 2006, NSDI.