1. Presented By : Nitya Shankaran Ritika Sharma

2. Overview Motivation behind the project Need to know how your data can be stored efficiently in the cloud i.e. choosing the right kind of storage. What has been done so far

3. Types of Data Storage Object storage Relational Database storage Distributed File systems etc.

4. Object Storage Uses data objects instead of files to store and retrieve data Maintains an index of Object ID (OID) numbers Ideal for storing large files.

5. Amazon S3 “ Provides a simple web interface that can be used to store and retrieve any amount of data, at any time, from anywhere on the web”. Currently, S3 stores over 449 billion user objects as of July 2011 and handles 900 million user requests a day. Amazon claims that S3 provides infinite storage capacity, infinite data durability, 99.99% availability and good data access performance.

6. How Amazon S3 stores data Makes use of buckets Objects are identified by a unique key which is assigned by the user S3 stores objects of upto 5 TB in size, each accompanied by 2 KB of metadata (Content-type, date last modified etc.). ACL’s - Read for objects and buckets - Write for buckets only - Read and write for objects Buckets and objects are created, listed and retrieved using REST-style HTTP interface or a SOAP interface.

7. Evaluation of S3 Experimental Setup Features and Findings: - Data Durability - Replica Replacement - Data Reliability - Availability - It provides versioning of object - Data Access performance - Security - Easy to use

8. Swift Used for creating redundant, scalable object storage using clusters of standardized servers to store petabytes of accessible data. Provides greater scalability, redundancy and permanence due to no central point of control. Objects are written to multiple hardware devices in the data center, with the OpenStack software responsible for ensuring data replication and integrity across the cluster. Storage clusters can scale horizontally by adding new nodes. Should a node fail, OpenStack works to replicate its content from other active nodes. Used mainly for virtual machine images, photo storage, email storage and backup archiving. Swift has a ReST-ful API.

9. Architecture of Swift - Proxy server processes API requests and routes requests to storage nodes - Auth server authenticates and authorizes requests - Ring represents mapping between the names of entities stored on disk and their physical location - Replicator provides redundancy for accounts, containers, objects - Updater processes failed or queued updates - Auditor verifies integrity of objects, containers, and accounts - Account Server handles listing of containers, stores as SQLite DB - Container Server handles listing of objects, stores as SQLite DB - Object Server stores, retrieves, and deletes objects stored on local devices

10. Evaluation of Swift - Data Durability - Replica Replacement - Data Reliability - Availability - Data scalability - Security - Objects must be < 5GB - Not a Filesystem - No User Quotas - No Directory Hierarchies - No writing to a byte offset in a file - No ACL’s

11. Swift is mainly used for: o Storing media libraries (photos, music, videos, etc.) o Archiving video surveillance files o Archiving phone call audio recordings o Archiving compressed log files o Archiving backups (< 5GB each object) o Storing and loading of OS Images, etc. o Storing file populations that grow continuously on a practically infinite basis. o Storing small files (<50 KB). OpenStack Object Storage is great at this. o Storing billions of files. o Storing Petabytes (millions of Gigabytes) of data.

12. Relational Database Storage (RDS) Aims to move much of the operational burden of provisioning, configuration, scaling, performance tuning, backup, privacy, and access control from the database users to the service operator, offering lower overall costs to users. Advantages: - Hardware costs are lower - Operational costs are lower Disadvantages: - Inability to scale well - Labor intensive (managing relational databases) - Error prone - Increased complexity since each database package comes with its own configuration options, tools, performances sensitivities and bugs.

13. Microsoft SQL Azure Cloud-based relational database service built on SQL Server Uses T-SQL as the query language and Tabular Data Stream (TDS) as the protocol Does not provide a REST-based API to access the service over HTTP unlike S3. Instead, uses SQL Azure accessed via Tabular Data Stream (TDS). Allows relational queries to be made against stored data Enables querying data, search, data analysis and data synchronization.

14. Network Topology – Part 1 Client Layer (At custom premises or Windows Azure Platform) HTTP/REST

15. Network Topology – Part 2

16. Evaluation Of SQL Azure Replica Replacement - Data Reliability - Availability Data Access performance Security Scalability: You can store any amount of data, from kilobytes to terabytes, in SQL Azure. However, individual databases are limited to 10 GB in size. Sharding Data sharding is a technique used by many applications to improve performance, scalability and cost by partitioning the data. For example, applications that store and process sales data using date or time predicates. These applications can benefit from processing a subset of the data instead of the entire data set.

17. Amazon S3Openstack SwiftSQL Azure Type of StorageObject Storage RDS storage Data ReplicationStore multiple redundant copies. 95.89% availability rate Consists of a Replicator. Ensures integrity of data. Ensures data availability by replication (SQL Azure fabric) and provides load balancing Data ScalabilityScalableHigh scalabilityIndividual databases limited to 10GB. SecurityClients authenticated by using public/private key scheme. ----Provides set of security principles UsageSuitable for large data objects and parallel applications Media libraries, OS images, backups and log files Querying the data and performing analysis

18. Distributed File system

19. Google File System Architecture Single Master Multiple Chunkservers HeartBeat Messages Chunk Size = 64MB Metadata File & Chunk namespace Mapping from files to chunks Locations of each chunk’s replica

20. Gfs- Micro-benchmarks GFS Cluster 1 master and 2 master replicas 16 chunkservers, 16 clients Dual 1.4 GHz P3 processor 100Mbps full-duplex ethernet connection to switch 19 servers are connected to switch S1 and 16 clients are connected to switch S2. S1 and S2 are connected to with a 1 Gbps link Reads Limit Peak 125MB/s for 1 Gbps link 12.5MB/s for 100Mbps link When 1 client is reading Read rate = 10MB/s = 80% of 12.5MB/s When 16 clients are reading Read rate = 94MB/s = 6MB/s per client = 75% of limit peak

21. Gfs- Micro-benchmarks (cont’d) Writes Limit Input connection = 12.5MB/s Limit = 67MB/s (write each byte to 3 of 16 chunkservers) When 1 client is writing 6.3MB/s (delays in propagation data b/w servers) When 16 clients are writing 35MB/s 2.2 MB/s per client Records Append Performance = n/w bandwidth of chunkserver having last chunk of file Independent of no. of clients When 1 client is appending Limit = 6MB/s When 16 clents are appending 4.8 MB/s

22. Features Data Integrity : Checksum Replica Placement Data reliability Availability Fast recovery Chunk and Master replication Rebalancing Replicas Better disk space Load balancing Garbage Collection Does not immediately reclaim the available storage File – renamed to a hidden name Removes after 3 days Stale Replica Detection Chunk version number Increments on updation

23. Hadoop Distributed File system Architecture NameNode - metadata Hierarchy of files & directories Attributes Block size = 128MB Primary and Secondary NameNode DataNode - Application data Each block replica – 2 files Data Block’s metadata Checksum Handshake- Namenode & Datanode Verify namespace ID s/w version Communication via TCP Heartbeat Message HDFS Client Interface b/w user application and HDFS Reading a file Writing a file Single-writer, multiple-reader model

24. Hdfs- benchmarks HDFS clusters at Yahoo! 3500 nodes 2 quad core Xeopn Processors @ 2.5ghz Linux 16GB RAM 1Gbps ethernet DFSIO benchmark Read: 66MB/s per node Write: 40 MB/s per node Busy Cluster Read: 1.02 MB/s per node Busy Cluster Write: 1.09 MB/s per node NNThroughput benchmark Starts NameNode app and multiple client threads on same node OperationThroughput (op/s) Open file for read126100 Create file5600 Rename file8300 Delete file207000 DataNode heartbeat 300000 Blocks report(blocks/s) 639700

25. Features Good Placement Policy 1 replica per DataNode < 2 replica per Rack Data reliability Availability N/w bandwidth utilization Replication Management Priority queue Load balancing No strategy Balancer – tool Application Program Disk usage Cluster utilization – node utilization = range (0,1) Data Integrity Block scanner on Node Verifies Checksum Inter-Cluster data Copy

26. GFS v/s HDFS Google File SystemHadoop Distributed File System Read Rate 6MB/s per client 1.02 MB/s per node Write Rate 2.2 MB/s per client 1.09 MB/s per node Records Append 4.8 MB/s per client - AvailabilityHighSingle point of failure. Data IntegrityChecksum Replica PlacementYes RebalancingYes Garbage CollectionYesNo Stale ReplicaYesNo Inter-cluster Data CopyNoYes