Question Scalability vs Elasticity – What is the difference?

Question

• Scalability vs Elasticity– What is the difference?

Homework 1

• Installing the open source cloud Eucalyptus in SEC3429• Individual assignment• Will need two machines – machine to help with

installation and machine on which to install cloud so BRING YOUR LAPTOP

• Guide to help you – step by step, but you will also need to use Eucalyptus Installation Guide

• When you are done you will have a cloud with a VM instance running on it

• Can use for future work and if not, can say you have installed a cloud and VM image

Components of Eucalyptus• CLC – cloud controller• Walrus – Amazon’s S3 for storing VM images• SC – storage controller• CC – cluster controllers• NC – node controllers

Cloud Controller - CLC

• Java program (EC2 compatible interface) and web interface

• Administrative interface for cloud management

• Resource scheduling • Authentication, accounting, reporting• Only one CLC per cloud

Walrus• Written in Java (equivalent to AWS Simple

Storage Service S3)• Persistent storage to

– all VMs• VM Images• Application data

– Volume snapshots (point-in-time copies)

• Can be used as put/get storage as a service• Only one Walrus per cloud• Why is it called Walrus – WS3?

Cluster Controller - CC

• Written in C• Front-end for a cluster within the cloud• Communicates with Storage Controller and

Node Controller• Manages VM instance execution and SLAs per

cluster

Availability Zones• Each cluster exists in an availability zone• A cloud can have multiple locations• Within each location is a region• Each region has multiple isolated locations which

are called availability zones• Availability zones are connected through low-

latency links

Storage Controller - SC

• Written in Java (equivalent to AWS Elastic Block Store EBS)

• Communicates with CC and NC• Manages Eucalyptus block volumes and

snapshots of instances within cluster• If larger storage needed for application

Node Controller - NC

• Written in C• Hosts VM instances – where they run• Manages virtual network endpoints• Downloads, caches images from Walrus• Creates and caches instances• Many NCs per cluster

Interesting Info on Clouds

• What Americans think a compute cloud is– http://www.citrix.com/lang/English/lp/lp_2328330.asp

Send me interesting links about clouds

Homework

• Read the paper on GFS: Evolution on Fast-Forward

• Also a link to a longer paper on GFS – original paper from 2003

• I assume you are reading papers as specified in the class schedule

The Original Google File System

GFS

Some slides from Michael Raines

• During the lecture, you should point out problems with GFS design decisions

Common Goals of GFSand most Distributed File Systems

• Performance

• Reliability

• Scalability

• Availability

GFS Design Considerations

• Component failures are the norm rather than the exception.• File System consists of hundreds or even thousands of

storage machines built from inexpensive commodity parts.

• Files are Huge. Multi-GB Files are common.• Each file typically contains many application objects such as

web documents.

• Append, Append, Append.• Most files are mutated by appending new data rather than

overwriting existing data.

• Co-Designing• Co-designing applications and file system API benefits

overall system by increasing flexibility

GFS

• Why assume hardware failure is the norm?

• The amount of layers in a distributed system (network, disk, memory, physical connections, power, OS, application) mean failure on any could contribute to data corruption.

• It is cheaper to assume common failure on poor hardware and account for it, rather than invest in expensive hardware and still experience occasional failure.

Initial Assumptions

• System built from inexpensive commodity components that fail

• Modest number of files – expect few million and 100MB or larger. Didn’t optimize for smaller files

• 2 kinds of reads – large streaming read (1MB), small random reads (batch and sort)

• Well-defined semantics:– Master/slave, producer/ consumer and many-way merge. 1

producer per machine append to file. – Atomic RW

• High sustained bandwidth chosen over low latency (difference?)

High bandwidth versus low latency

• Example:– An airplane flying across the country filled with

backup tapes has very high bandwidth because it gets all data at destination faster than any existing network

– However – each individual piece of data had high latency

Interface

• GFS – familiar file system interface• Files organized hierarchically in directories,

path names• Create, delete, open, close, read, write• Snapshot and record append (allows multiple

clients to append simultaneously)– This means atomic read/writes – not transactions!

Master/Servers (Slaves)

• Single master, multiple chunkservers• Each file divided into fixed-size chunks of 64 MB

– Chunks stored by chunkservers on local disks as Linux files

– Immutable and globally unique 64 bit chunk handle (name or number) assigned at creation

Master/Servers

– R or W chunk data specified by chunk handle and byte range

– Each chunk replicated on multiple chunkservers – default is 3

Master/Servers

• Master maintains all file system metadata– Namespace, access control info, mapping from files to

chunks, location of chunks– Controls garbage collection of chunks– Communicates with each chunkserver through HeartBeat

messages– Clients interact with master for metadata, chunksevers do

the rest, e.g. R/W on behalf of applications– No caching –

• For client working sets too large, simplified coherence• For chunkserver – chunks already stored as local files, Linux

caches MFU in memory

Heartbeats

• What do we gain from Heartbeats?

• Not only do we get the new state of a remote system, this also updates the master regarding failures.

• Any system that fails to respond to a Heartbeat message is assumed dead. This information allows the master to update his metadata accordingly.

• This also queues the Master to create more replicas of the lost data.

Client• Client translates offset in file into chunk index within file• Send master request with file name/chunk index• Master replies with chunk handle and location of replicas• Client caches info using file name/chunk index as key• Client sends request to one of the replicas (closest)• Further reads of same chunk require no interaction• Can ask for multiple chunks in same request

Master Operations

• Master executes all namespace operations• Manages chunk replicas• Makes placement decision• Creates new chunks (and replicas)• Coordinates various system-wide activities to

keep chunks fully replicated• Balance load• Reclaim unused storage

• Do you see any problems?• Do you question any design decisions?

Master - Justification

• Single Master – – Simplifies design– Placement, replication decisions made with global

knowledge– Doesn’t R/W, so not a bottleneck– Client asks master which chunkservers to contact

Chunk Size - Justification

• 64 MB, larger than typical• Replica stored as plain Linux file, extended as needed• Lazy space allocation• Reduces interaction of client with master

– R/W on same chunk only 1 request to master– Mostly R/W large sequential files

• Likely to perform many operations on given chunk (keep persistent TCP connection)

• Reduces size of metadata stored on master

Chunk problems

• But – – If small file – one chunk may be hot spot– Can fix this with replication, stagger batch

application start times

Metadata

• 3 types:– File and chunk namespaces– Mapping from files to chunks– Location of each chunk’s replicas

• All metadata in memory• First two types stored

in logs for persistence(on master local disk and replicated remotely)

Metadata

• Instead of keeping track of chunk location info– Poll – which chunkserver has which replica– Master controls all chunk placement– Disks may go bad, chunkserver errors, etc.

Metadata - Justification

• In memory –fast– Periodically scans state

• garbage collect• Re-replication if chunkserver failure• Migration to load balance

– Master maintains < 64 B data for each 64 MB chunk

• File namespace < 64B

Chunk size (again)- Justification

• 64 MB is large – think of typical size of email

• Why Large Files?o METADATA!

• Every file in the system adds to the total overhead metadata that the system must store.

• More individual data means more data about the data is needed.

Operation Log

• Historical record of critical metadata changes• Provides logical time line of concurrent ops• Log replicated on remote machines• Flush record to disk locally and remotely• Log kept small – checkpoint when > size• Checkpoint in B-tree form• New checkpoint built without delaying mutations

(takes about 1 min for 2 M files)• Only keep latest checkpoint and subsequent logs

Snapshot• Snapshot makes copy of file• Used to create checkpoint or branch copies of

huge data sets • First revokes leases on chunks• Newly created snapshot points to same

chunks as source file• After snapshot, client sends request to master

to find lease holder• Master give lease to new copy

Shadow Master

• Master Replication– Replicated for reliability– Not mirrors, so may lag primary slightly

(fractions of second)– Shadow master read replica of operation log,

applies same sequence of changes to data structures as the primary does

Shadow Master• If Master fails:

– Start shadow instantly– Read-only access to file systems even when

primary master down– If machine or disk mails, monitor outside GFS

starts new master with replicated log– Clients only use canonical name of master

Creation, Re-replication, Rebalancing

• Master creates chunk– Place replicas on chunkservers with below-average disk

utilization– Limit number of recent creates per chunkserver

• New chunks may be hot– Spread replicas across racks

• Re-replicate– When number of replicas falls below goal

• Chunkserver unavailable, corrupted, etc.• Replicate based on priority (fewest replicas)

– Master limits number of active clone ops

Creation, Re-replication, Rebalancing

• Rebalance– Periodically moves replicas for better disk space

and load balancing– Gradually fills up new chunkserver – Removes replicas from chunkservers with below-

average free space

Leases and Mutation Order

• Chunk lease• One replica chosen as primary - given lease• Primary picks serial order for all mutations to

chunk• Lease expires after 60 s

Consistency Model

• Why Append Only?

• Overwriting existing data is not state safe.o We cannot read data while it is being modified.

• A customized ("Atomized") append is implemented by the system that allows for concurrent read/write, write/write, and read/write/write events.

Consistency Model

W rite R ec o rd A p p en d

S er ia l su ccess

defined defined inte rsp ersed w ith inconsistent C o n cu rrent

su ccesse s consistent b u t undefined

Fa ilu re inconsistent

Ta b le 1: F ile R eg io n S tate A fter M u ta tion

Consistency Model

• File namespace mutation (update) atomic• File Region

• Consistent if all clients see same data• Region – defined after file data mutation (all clients see

writes in entirety, no interference from writes)• Undefined but Consistent - concurrent successful

mutations – all clients see same data, but not reflect what any one mutation has written, fragments of updates

• Inconsistent – if failed mutation (retries)

Consistency

• Relaxed consistency can be accommodated – relying on appends instead of overwrites

• Appending more efficient/resilient to failure than random writes

• Checkpointing allows restart incrementally and no processing of incomplete successfully written data

Namespace Management and Locking

• Master ops can take time, e.g. revoking leases– allow multiple ops at same time, use locks over

regions for serialization– GFS does not have per directory data structure

listing all files– Instead lookup table mapping full pathnames to

metadata• Each name in tree has R/W lock• If accessing: /d1/d2/ ../dn/leaf, R lock on /d1, /d1/d2,

etc., W lock on /d1/d2 …/leaf

Locking

• Allows concurrent mutations in same directory• R lock on directory name prevents directory

from being deleted, renamed or snapshotted• W locks on file names serialize attempts to

create file with same name twice• R/W objects allocated lazily, delete when not in

use• Locks acquired in total order (by level in tree)

prevents deadlocks

Fault Tolerance

• Fast Recovery– Master/chunkservers restore state and start in

seconds regardless of how terminated• Abnormal or normal

• Chunk Replication

Data Integrity

• Checksumming to detect corruption of stored data• Impractical to compare replicas across chunkservers to

detect corruption• Divergent replicas may be legal• Chunk divided into 64KB blocks, each with 32 bit

checksums• Checksums stored in memory and persistently with logging

Data Integrity

• Before read, checksum• If problem, return error to requestor and reports to master• Requestor reads from replica, master clones chunk from

other replica, delete bad replica• Most reads span multiple blocks, checksum small part of it• Checksum lookups done without I/O• Checksum computation optimized for appends• If partial corrupted, will detect with next read• During idle, chunkservers scan and verify inactive chunks

Garbage Collection• Lazy at both file and chunk levels• When delete file, file renamed to hidden name including

delete timestamp• During regular scan of file namespace

– hidden files removed if existed > 3 days– Until then can be undeleted– When removed, in-memory metadata erased– Orphaned chunks identified and erased– With HeartBeat message, chunkserver/master

exchange info about files, master tells chunkserver about files it can delete, chunkserver free to delete

Garbage Collection

• Easy in GFS• All chunks in file-to-chunk mappings of

master• All chunk replicas are Linux files under

designated directories on each chunkserver• Everything else garbage

Conclusions

• GFS – qualities essential for large-scale data processing on commodity hardware

• Component failures the norm rather than exception• Optimize for huge files appended to• Fault tolerance by constant monitoring, replication,

fast/automatic recovery• High aggregate throughput

– Separate file system control – Large file size

GFS In the Wild - 2003

• Google currently has multiple GFS clusters deployed for different purposes.

• The largest currently implemented systems have over 1000 storage nodes and over 300 TB of disk storage.

• These clusters are heavily accessed by hundreds of clients on distinct machines.

• Has Google made any adjustments?

• Read paper on New GFS

• Google’s Colossus

OpenStack• 3 components in the architecture

– Cloud Controller - Nova (compute)• the cloud computing fabric controller,• Written in Python, uses external libraries

– Storage Controller –Swift• Analogous to AWS S3• Can store billions of objects across nodes• Built-in redundancy

– Image Controller – Glance• Manages/stores VM images• Can use local file system, OpenStack Object Store, S3