Survey of Distributed Storage System

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Outline• Background• Storage Virtualization• Object Storage• Distributed File System

Outline• Background• Storage Virtualization• Object Storage• Distributed File System

Background• As more and more digital devices(e.g. PC, laptop, ipad

and smart phone) connect to the Internet, massive amount of new data are created on the web

• There were 5 exabytes of data online in 2002, which had risen to 281 exabytes in 2009, and the online data growth rate is rising faster than Moore's Law

• Then, how to store and manage these massive data effectively and efficiently ?

• An natural approach: Distributed Storage System!

Traditional Storage Architecture• Direct Attached Storage(DAS)

- huge management burden - limited number of connected host

- severely limited data sharing

• Fabric Attached Storage- central system serves data to connected hosts- hosts and devices interconnected through Ethernet or Fibre Channel- NAS & SAN

FAS Implementations• Network Attached Storage(NAS)

- file-based storage architecture- data sharing across platforms- file sever can be the bottleneck

• Storage Area Networks(SAN)- scalable performance, high

capacity- limited ability of sharing data- unreliable security

Since the traditional storage architectures cannot satisfy the emerging requirement well, novel approaches need to be proposed !

Outline• Background• Storage Virtualization• Object Storage• Distributed File System

Storage Virtualization• Definitions of storage virtualization by SNIA

- the act of abstracting, hiding, or isolating the internal functions of a storage (sub)system or service from applications, computer servers, or general network resources for the purposes of enabling application and network independent management of storage or data- The application of virtualization to storage services or

devices for the purpose of aggregating, hiding complexity, or adding new capabilities to lower-level storage resources

Simply speaking, storage virtualization aggregates storage components, such as disks,

controllers, and storage networks, in a coordinated way to share them more efficiently

among the applications it serves!

Charactristics of ideal solution• A good storage virtualization solution should:

Enhance the storage resources it is virtualizing through the aggregation of services to increase the return of existing assets

Not add another level of complexity in configuration andmanagement

Improve performance rather than act as a bottleneck inorder for it to be scalable. Scalability is the capability of a

system to maintain performance linearly as new resources(typically hardware) are added

Provide secure multi-tenancy so that users and data canshare virtual resources without exposure to other users’

bad behavior or mistakes

Not be proprietary, but virtualize other vendor storage inthe same way as its own storage to make the management

seamless.

Types of Storage Virtualization• Modern storage virtualization technologies can be

implemented in three layers of the infrastructure• In the server, some of the earliest forms of storage

virtualization came from within the server’s operating systems

• In the storage network, network-based storage virtualization embeds the intelligence of managing the storage resources in the network layer

• In the storage controller, controller-based storage virtualization allows external storage to appear as if it’s internal

Server-basedServer-based storage virtualization is highly configurable and flexible since it’s implemented in the system software.Because most operating systems incorporate this functionality into their system software, it is very cheap.It does not require additional hardware in the storage infrastructure, and works with any devices that can be seen by the operating system.

Although it helps maximize the efficiency and resilience of storage resources, it’s optimized on a per-server basis only. The task of mirroring, striping, and calculating parity requires additional processing, taking valuable CPU and memory resources away from the application.Since every operating system implements file systems and volume management in different ways, organizations with multiple IT vendors need to maintain different skill sets and processes, with higher costs.When it comes to the migration or replication of data (either locally or remotely) it becomes difficult to keep track of data protection across the entire environment.

Network-basedBoth in-band and out-of-band approaches provide storage virtualization with the ability to:Pool heterogeneous vendor storage products in a seamless accessible pool.Perform replication between non-like devices.Provide a single management interface.Only the in-band approach can cache data for increased performance.

Both approaches also suffer from a number of drawbacks:Implementation can be very complex because the pooling of storage requires the storage extents to be remapped into virtual extents.The virtualization devices are typically servers running system software and requiring as much maintenance as a regular server.The I/O can suffer from latency, impacting performance and scalability due to the multiple steps required to complete the request, and limited to the amount of memory and CPU available in the appliance nodes.Decoupling the virtualization from the storage once it has been implemented is impossible because all the meta-data resides in the appliance, thereby making it proprietary.Solutions on the market only exist for fibre channel (FC) based SANs.

Controller-basedConnectivity to external storage assets is done via industry standard protocols, with no proprietary lock-in.Complexity is reduced as it needs no additional hardware to extend the benefits of virtualization. In many cases the requirement for SAN hardware is greatly reduced.Controller-based virtualization is typically cheaper than other approaches due to the ability to leverage existing SAN infrastructure, and the opportunity to consolidatemanagement, replication, and availability tools.Capabilities such as replication, partitioning, migration, and thin provisioning are extended to legacy storage arrays.Heterogeneous data replication between non-like vendors or different storage classes reduces data protection costs.Interoperability issues are reduced as the virtualized controller mimics a server connection to external storage.

Although a few downsides to controller-based virtualization exist, the advantages not only far outweigh them but they also address most of the deficiencies found in server- and network based approaches.

Outline

• Background• Storage Virtualization• Object Storage• Distributed File System

Motivation of Object Storage• Improved device and data sharing

- platform-dependent metadata moved to device

• Improved scalability & security- devices directly handle client requests- object security

• Improved performance- data types can be differentiated at the device

• Improved storage management- self-managed, policy-driven storage

- storage devices become more autonomous

Objects in Storage

P1

P2

P3

P4

User Data

AttributesObject ID

Metadata

U1

User Objects (for user data)

Collection Objects

Partition Objects

Root Object(one per device)

OSD

The root object -- The OSD itselfUser object -- Created by SCSI commands from the application or clientCollection object -- A group of user objects, such as all .mp3Partition object -- Containers that share common security and space management characteristics

Object Storage Device• Two changes

- Object-based storage offloads the storage component to the storage device

- The device interface changes from blocks to objects

Applications

System call interface

File system storage component

File system user component

Block interface

Block I/O manager

Storage device

Applications

System call interface

File system user component

Object interface

Block I/O manager

Storage device

File system storage component

Traditional model OSD model

Object Storage Architecture

Summary of OSD Key Benefits ■ Better data sharing – Using objects means less metadata

to keep coherent, which makes it possible to share thedata across different platforms.

■ Better security – Unlike blocks, objects can protectthemselves and authorize each I/O.

■ More intelligence – Object attributes help the storagedevices learn about its users, the applications and theworkloads. This leads to a variety of improvements, suchas better data management through caching. Active diskscan be implemented on OSDs to implement databasefilters. An intelligent OSD can also continuously reorganizethe data, manage its own backups and deal with failures.

Lustre• Lustre (Linux + Cluster)

- first open sourced system with object storage - a massively parallel distributed file system- consist of clients, MDS and OST- used by fifteen of the top 30 supercomputers in the world

A single metadata server (MDS) that has a single metadata target (MDT) per Lustre filesystem that stores namespace metadata, such as filenames, directories, access permissions, and file layout.

One or more object storage servers

(OSSes) that store file data on one or more

object storage targets (OSTs)

Client(s) that access and

use the data, concurrent

and coherent read and

write access to the files are allowed

Ceph• Ceph is a distributed file system that provides excellent

performance, reliability, and scalability based on object storage devices

Metadata Cluster store the cluster map and control

the data placement, higher-level

POSIX functions (such as open,

close, and rename) are

managed.

Panasas• Panasas (Panasas, Inc.)

- consist of OSD, Panasas File System, MDS- claim to be the world's fastest HPC storage system

Outline

• Background• Storage Virtualization• Object Storage• Distributed File System

Distributed File System• A distributed file system or network file system is any

file system that allows access to files from multiple hosts sharing via a computer network(Wikipedia)

• The history

- 1st generation(1980s): NFS, AFS- 2nd generation(1990~1995): Tiger Shark,

Slice File System - 3rd generation(1995~2000): Global File

System, General Parallel File System, DiFFs, CXFS, HighRoad

- 4th generation(2000~now): Lustre, GFSm, GlusterFS, HDFS

PerformanceScalabilityReliability

AvailabilityFault-tolerant

Google File System(GFS)• GFS is a scalable distributed file system for large

distributed data-intensive application in Google• Beyond the traditional choices

- normal component failures- huge files by traditional standards - appending new data rather than overwriting- co-designing the application and file system API

• GFS Interface- create, delete, open, close, read, write- snapshot & record append

Master maintains all file

system metadata, such as namespace, access control information,

mapping from files to chunks

and the location of chunks

Files are divided into fix-size(64MB) chunks, and each chunk is identified by immutable and

global unique 64 bit chunk handle. Chunkservers store chunks on local disks as

Linux files. In addition, each chunk is replicated on multiple chunkservers, in default, 3 replicas.

Clients interact with the master for metadata

operations, but all data-bearing communication

goes directly to the chunkservers

Write Control and Data FlowThe client asks the master which chunkserver holds the current lease for the chunk and the locations of the other replicas. If no one has, the master grants one to a replica it chooses.

The master replies with the identity of primary and the locations of the other replicas. The client caches the information.

The client pushes the data to all replicas in any order.

The client sends a write request to the primary once all the replicas have acknowledged receiving the data. The primary assigns consecutive serial numbers to all the mutations it receives and applies the mutation to its own local state in serial number order. The primary

forwards the write request to secondary replicas

The secondaries all reply to the primary indicating that they have completed the operation.

The primary replies to the client.

Error cases:1) Failed at the primary, it

would not have been assigned a serial number and forwarded;

2) Succeeded at primary and an arbitrary subset of the secondary replicas.

The client code handles such errors by retrying the failed mutation.

Hadoop Distributed File System (HDFS)

• The Hadoop Distributed File System (HDFS) is an open source implementation of GFS

NameNode, a master server that manages the file system namespace and regulates access to files by clients.

DataNodes, manage storage attached to the nodes that they run on

A file is split into one or more blocks and these blocks are stored in a set of DataNodes

Taobao File System• Taobao File System(TFS) is a distributed file system

optimized for the management of massive small files(1MB), such as pictures and descriptions of commodity

Name Sever: store metadata,

monitor data server through

heartbeat message, control IO balance, and

data location info such <block id,

data server>

Data Sever: store application data, load blance, redundant backup

Application/

Client: access

the name server &

data server

through TFSClient

GlusterFS• GlusterFS is an open source, clustered file system

capable of scaling to several petabytes and handling thousands of clients

Foundamental shift:elimination of metadata synchronization and updates, for each individual operation Gluster calculates metadata using universal algorithms effective distribution of data, file distribution is intelligently handled using elastic hash highly parallel architecture, there is a far more intelligent relationship between available CPUs and spindles

GlusterFS(cont.)Gluster offers

multiple ways for users to access

volumes in a Gluster storage cluster

Gluster allows to configure GlusterFS volumes in different scenarios:1)Distributed ,distributesfiles throughout the cluster;2)Distributed Replicated,replicates data across two ormore nodes in the cluster;3) Distributed Striped, Stripes files across multiple nodes in the cluster.

Sheepdog• Sheepdog is a distributed storage system for QEMU/KVM

- amazon EBS-like volume pool- highly scalable, available and reliable- support for advanced volume management- not general file system, API is designed specific to QEMU

Zero configuration about cluster nodes

Automatically detect removed nodes

Automatically detect added nodes

SheepdogVolumes are divided into 4 MB objects and each object is identified by globally unique 64 bit id, and replicated to multiple nodes

Consistent hashing is used to decidewhich node to store objects. Each node is also placed on the ring.Addition or removal of nodes does not significantly change the mapping of objects

Reference[1] A. D. Luca and M. Bhide. Storage virtualization for

dummies, Hitachi Data Systems Edition. Wiley Publishing, 2010.

[2] S. Ghemawat, H. Gobioff, and S.-T. Leung. The google file system. In Proceedings of the 19th ACM Symposium on Operating Systems Principles 2003, SOSP 2003, Bolton Landing, NY, USA, October 19-22, 2003.

[3] R. MacManus. The coming data explosion. Available: http://www.readwriteweb.com/archives/the_coming_data_explosion.php, 2010.

Reference (cont.)[4] Intel white paper: Object-based storage, the next wave

of storage technology and devices, 2003.[5] M. Mesnier, G. R. Ganger and E. Riedel. Object-based

storage. IEEE Communications Magazine, August 2003, 84-89.

[6] Lustre. Available: http://wiki.lustre.org/index.php, 2010.[7] Panasas. Available: http://www.panasas.com/.[8] Hadoop. Available: http://hadoop.apache.org/.[9] tfs. Available:

http://code.taobao.org/trac/tfs/wiki/intro.[10] GlusterFS. Available: http://www.gluster.org/.

Reference (cont.)[11] Sheep dog. Available: http://www.osrg.net/sheepdog/.[12] Ceph. Available: http://ceph.newdream.net/.[13] S. A. Weil, S. A. Brandt, E. L. Miller, D. D. E. Long. Ceph:

A Scalable, High-Performance Distributed File System. In Proceedings of 7th Symposium on Operating Systems Design and Implementation (OSDI '06), November 6-8, Seattle, WA, USA.

[14] Gluster Whitepaper: Gluster file system architecture.