Survey : Cloud Storage Systems

Presented By :

Nitya Shankaran

Ritika Sharma

OverviewMotivation 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

Types of Data StorageObject storageRelational Database storage Distributed File systems etc.

Object Storage

Uses data objects instead of files to store and retrieve dataMaintains an index of Object ID (OID) numbersIdeal for storing large files.

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.

How Amazon S3 stores dataMakes 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.

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

SwiftUsed 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.

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

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

Swift is mainly used for:

o Storing media libraries (photos, music, videos, etc.)o Archiving video surveillance fileso Archiving phone call audio recordingso Archiving compressed log fileso 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.

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.

Microsoft SQL AzureCloud-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.

Network Topology – Part 1

Client Layer(At custom premises or Windows Azure Platform)

HTTP/REST

Network Topology – Part 2

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.

Amazon S3 Openstack Swift

SQL Azure

Type of Storage Object Storage Object Storage RDS storage

Data Replication Store 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 Scalability Scalable High scalability Individual databases limited to 10GB.

Security Clients authenticated by using public/private key scheme.

---- Provides set of security principles

Usage Suitable for large data objects and parallel applications

Media libraries, OS images, backups and log files

Querying the data and performing analysis

Distributed File system

Google File System

ArchitectureSingle MasterMultiple

Chunkservers HeartBeat MessagesChunk Size = 64MBMetadata

File & Chunk namespace

Mapping from files to chunks

Locations of each chunk’s replica

Gfs- Micro-benchmarksGFS Cluster

1 master and 2 master replicas

16 chunkservers, 16 clientsDual 1.4 GHz P3 processor100Mbps 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

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

FeaturesData Integrity : ChecksumReplica Placement

Data reliabilityAvailability

Fast recovery Chunk and Master replication

Rebalancing ReplicasBetter disk spaceLoad balancing

Garbage CollectionDoes not immediately reclaim the available storageFile – renamed to a hidden nameRemoves after 3 days

Stale Replica DetectionChunk version numberIncrements on updation

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

Hdfs- benchmarksHDFS clusters at Yahoo!

3500 nodes2 quad core Xeopn

Processors @ 2.5ghzLinux16GB RAM1Gbps ethernet

DFSIO benchmarkRead: 66MB/s per nodeWrite: 40 MB/s per nodeBusy Cluster Read: 1.02

MB/s per nodeBusy Cluster Write: 1.09

MB/s per node

NNThroughput benchmarkStarts NameNode

app and multiple client threads on same node

Operation Throughput (op/s)

Open file for read

126100

Create file 5600

Rename file 8300

Delete file 207000

DataNode heartbeat

300000

Blocks report(blocks/s)

639700

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

GFS v/s HDFSGoogle File System

Hadoop 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 -

Availability High Single point of failure.

Data Integrity Checksum Checksum

Replica Placement Yes Yes

Rebalancing Yes Yes

Garbage Collection Yes No

Stale Replica Yes No

Inter-cluster Data Copy

No Yes

Thank You!