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CLOUD COMPUTING. Nguyễn Anh Tài Nguyễn Phương Duy Phạm Thanh Phương. C ontents. Introduction to Cloud Computing Cloud Implementations Market-Oriented Clouds Comparing Grids and Clouds. INTRODUCTION (1). Issues before cloud Cost (hardware, software, maintain, …) recession Scalable - PowerPoint PPT Presentation
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CLOUD CLOUD COMPUTINGCOMPUTING
1. Nguyễn Anh Tài
2. Nguyễn Phương Duy
3. Phạm Thanh Phương
CONTENTSCONTENTS
Introduction to Cloud Computing
Cloud Implementations
Market-Oriented Clouds
Comparing Grids and Clouds2
INTRODUCTION (1)INTRODUCTION (1)
Issues before cloud
Cost (hardware, software, maintain, …)
recession
Scalable
Technology
……
3
INTRODUCTION (2)INTRODUCTION (2)
““Cloud computing is hinting at a Cloud computing is hinting at a futurefuture in which we won’t in which we won’t
compute on compute on local computerslocal computers, , but on centralized facilities but on centralized facilities
operated by operated by third party third party compute and storage utilitiescompute and storage utilities.”.”
4
DEFINITIONSDEFINITIONS
“Cloud computing is a style of computing in which dynamically
scalable and often virtualized resources are provided as a
service over the Internet” – Wikipedia.
“A style of Computing where scalable and elastic IT
capabilities are provided as a service to multiple customers
using Internet technologies” – Gartner.
“A large scale distributed computing paradigm that is driven by
economics of scale, in which a pool of abstracted, virtualized,
dynamically – scalable, managed computing power, storage,
platforms, and services are delivered on demand to external
customers over the Internet” – Ian Foster. 5
Users need not have knowledge of, expertise in, or control over the technology infrastructure "in the cloud" that supports them.
Cloud computing services usually provide common business applications online that are accessed from a web browser, while the software and data are stored on the servers.
INTRODUCTION (4)INTRODUCTION (4)
6
Cloud computing is often confused with
grid computing
utility computing
autonomic computing
many cloud computing deployments as of 2009 depend on
grids, have autonomic characteristics and bill like utilities
cloud computing can be seen as a natural next step from the
grid-utility model
≠ P2P networks (BitTorrent), volunteer computing (SETI@home)
INTRODUCTION (5)INTRODUCTION (5)
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INTRODUCTION (6)INTRODUCTION (6)
8
Trends
INTRODUCTION (7)INTRODUCTION (7)
9
Overview
INTRODUCTION (8)INTRODUCTION (8)
10
Economics
INTRODUCTION (9)INTRODUCTION (9)ISSUESISSUES
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Cloud computing shines in the recession, vnunet.com, 04/28/2009 The most popular applications are data storage (27.7%
of respondents), financial applications (17%) and email (12.8%)
Issues Intellectual property Privacy Security Not control Latency Enclosure services Ready Laws
INTRODUCTION (10)INTRODUCTION (10)
12
Companies IBM (Blue Cloud) Amazon (EC2) Google (G. Apps) Microsoft (M. Azure) Yahoo Salesforce ……
CLOUD COMPUTING CLOUD COMPUTING IMPLEMENTATIONS IMPLEMENTATIONS CATEGORIESCATEGORIES
3 categories of cloud computing services: Infrastructure-as-a-Service (IaaS): raw infrastructure and
associated middleware Amazon EC2/S3, Elastra (Beta 2.0 2/2009), Nirvanix, AppNexus
Platform-as-a-Service (PaaS): APIs for developing
applications on an abstract platform Mosso (2/2008), Google App Engine, Salesforce, Heroku, Engine
Yard
Software-as-a-Service (SaaS): support for running
software services remotely 3Tera (2/2006), Salesforce
13
CLOUD COMPUTING CLOUD COMPUTING IMPLEMENTATIONSIMPLEMENTATIONSPRICING MODELPRICING MODEL
Pricing model:
Tired pricing: each tier offer fix computing
specification (i.e. memory allocation, CPU type and
speed, etc.)
Per-unit pricing: is normally applied to data transfers
or memory usage, memory allocation is more
flexible than tire pricing
Subscription base pricing: most-widely use for SaaS
– user predict periodic expenses of using cloud
application (lack of accuracy)
14
CLOUD COMPUTING CLOUD COMPUTING IMPLEMENTATIONSIMPLEMENTATIONSAMAZONE ELASTIC COMPUTE AMAZONE ELASTIC COMPUTE CLOUD (EC2)CLOUD (EC2)
Infrastructure-as-a-Service
Compute, Storage
Instance: Xen Virtual
machine
5 instance types: CPU, RAM,
Arch, I/O performance, Disk
Cost (each instance - $/h)
User requires instances
(instance type & VM Image)
15
CLOUD COMPUTING CLOUD COMPUTING IMPLEMENTATIONS IMPLEMENTATIONS GOGRIDGOGRID
Infrastructure-as-a-Service
"world's first multi-server control
panel” provide server image with
preinstalled software
Load balancer (virtual IP), Web-based
control panel, API, and .NET SDK
1 Server RAM-hour = 1GB of RAM
deployed for 1 Hour16
CLOUD COMPUTING CLOUD COMPUTING IMPLEMENTATIONSIMPLEMENTATIONSMICROSOFT LIVE MESHMICROSOFT LIVE MESH
Infrastructure-as-a-Service
Compute
OS Level
Provide a centralized location for a user to store
applications and data that can be accessed
across required devices (such as computers and
mobile phones)
User access interface: Web-based Live Desktop
and any Live Mesh installed devices17
CLOUD COMPUTING CLOUD COMPUTING IMPLEMENTATIONSIMPLEMENTATIONSSUN GRID (NETWORK.COM)SUN GRID (NETWORK.COM)
Infrastructure-as-a-Service
Compute
Job management system
Sun Grid Engine
User access interface: Job submission
scripts, Sun Grid Web portal
18
CLOUD COMPUTING CLOUD COMPUTING IMPLEMENTATIONS IMPLEMENTATIONS GOOGLE GOOGLE APP ENGINEAPP ENGINE
Platform-as-a-Service
Platform
Lets user run web app on Google’s infrastructure
These apps run in a sandbox:
No local files, App Engine datastore request within a limited
period
AppLogic: appliances run inside VM, configured by user
Hadoop (implementation of MapReduce) vast amount of data19
CLOUD COMPUTING CLOUD COMPUTING IMPLEMENTATIONS IMPLEMENTATIONS GOOGLE GOOGLE APP ENGINEAPP ENGINE
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CLOUD COMPUTING CLOUD COMPUTING IMPLEMENTATIONS IMPLEMENTATIONS GOOGLE GOOGLE APPSAPPS
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CLOUD COMPUTING CLOUD COMPUTING IMPLEMENTATIONSIMPLEMENTATIONSSALESFORCESALESFORCE
AppExchange
On-demand
application sharing
service
Applications that run
entirely within a web
browse
Force.com
On-demand platform
Provide Web Service API: Force.com Web Services
API Salesforce Object Query
Language (SOQL) Salesforce Object Search
Language (SOSL) Etc.
Software-as-a-Service Platform-as-a-Service
22
CLOUD COMPUTING CLOUD COMPUTING IMPLEMENTATIONSIMPLEMENTATIONSSALESFORCESALESFORCE
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CLOUD COMPUTING CLOUD COMPUTING IMPLEMENTATIONSIMPLEMENTATIONS3TERA3TERA
Software-as-a-Service
Launch AppLogic (grid OS)
package an entire N-tier application or service into a
logical entity and manage it as a single system
Application run on a grid of commodity servers
24
CLOUD COMPUTING CLOUD COMPUTING IMPLEMENTATIONSIMPLEMENTATIONSRIGHTSCALERIGHTSCALE MultipleMultiple Clouds Work (9/2007) Managing clouds:
Amazon’s Web Service GoGrid FlexiScale Mosso
4/2009 extend to Private and Hybrid Cloud 3 types of Cloud
Public cloud (external cloud): traditional mainstream sense
Private cloud Hybrid cloud: consisting of multiple internal and/or
external providers25
CLOUD COMPUTINGCLOUD COMPUTINGMARKET ORIENTED (1)MARKET ORIENTED (1)
26
CLOUD COMPUTINGCLOUD COMPUTINGMARKET ORIENTED (2)MARKET ORIENTED (2)
27
User/Broker
SLA Resource
Allocator
Interface
External Users/Brokers
Cloud service Provider
Virtual Machines (VM)
Physical Machines
CLOUD COMPUTINGCLOUD COMPUTINGMARKET ORIENTED (3) MARKET ORIENTED (3) - - SLA SLA RESOURCE ALLOCATORRESOURCE ALLOCATOR
Service Request Examiner and Admission Control ensure nooverloading
Pricing decide charge fee (time, rate, availbility)
Accounting maitain usage of resource
VM Monitor keep VMtrack Dispatcher execute
progress of service on VM Service Request Monitor
keep track of executing service 28
CLOUD COMPUTINGCLOUD COMPUTING MARKET MARKET ORIENTED (4)ORIENTED (4)RESOURCE MANAGEMENT RESOURCE MANAGEMENT STRATEGIESSTRATEGIES
Customer: keep inform and obtain feedback
Management risk: establish context of risk
and identify the risks involved
User requirement change overtime: Dynamically change service demand User can broke system acting to select suitable
provider and negotiation
Virtualization: configure VM as different
partitions of resources 29
CLOUD COMPUTINGCLOUD COMPUTINGGLOBAL CLOUD EXCHANGE AND GLOBAL CLOUD EXCHANGE AND MARKETSMARKETS
30
GRIDS AND CLOUDS GRIDS AND CLOUDS OVERVIEWOVERVIEW Is “Cloud Computing” just a new name for Grid ?
Yes: They are the same – to reduce the cost of
computing, increase reliability, flexibility by transforming
computers from something that we buy and operate
ourselves to something that is operated by third party.
No: Things are different now than they were 10 years
ago.
So we are operating at a different scale, and operating at
these new, more massive scales can demand fundamentally
different approaches to tackling problems.31
GRIDS AND CLOUDS OVERVIEWGRIDS AND CLOUDS OVERVIEW
32
The definition of
Cloud Computing overlaps with many
existing technologies,
such as Grid
Computing, Utility
Computing, Services
Computing, and
Distributed Computing
in general.
COMPARING GRIDS AND CLOUDSCOMPARING GRIDS AND CLOUDS
33
Business Model1
Architecture2
Resource Management3
Programming Model4
Security Model6
Application Model5
BUSINESS MODELBUSINESS MODEL
The business model for Clouds:User will pay the provider on a consumption
basis, such as electricity, gas, and water.The prospect of needing only a credit card to get
on demand access to 100000+ processors in tens of data centers distributed throughout the world.
The business model for Grids: It is project-oriented in which users or community
represented by that proposal have certain number of services units (i.e. CPU hours) they can spend.
A Grid economy for a global Grid ? 34
ARCHITECTUREARCHITECTURE
Grids started off in the mid-90s to address large scale computation problems using a network of resource sharing commodity machines Focus on integrating existing resources.
Clouds are developed to address Internet scale computing problems (homogeneous). Usually referred to as a large pool of computing
and/or storage resource.
35
Architecture
Cloud protocol ArchitectureGrid protocol Architecture
Application
Collective
Resource
Connectivity
Fabric Fabric
Unified Resource
Application
Platform
36
RESOURCE MANAGEMENTRESOURCE MANAGEMENT
1. Compute Model
2. Data Model
3. Data Locality
4. Combining compute and data
management
5. Virtualization
6. Monitoring
7. Provenance37
RESOURCE MANAGEMENTRESOURCE MANAGEMENT
1.1. Compute ModelCompute Model
Most Grids use a batch-scheduled compute
model. A grid site use a local resource manager
such as PBS, Condor, SGE.
Resources in Cloud Computing are shared by all
users at the same time (in contrast to dedicated
resources governed by a queuing system).
one of the major challenges for Cloud Computing38
RESOURCE MANAGEMENTRESOURCE MANAGEMENT
39
DataData
Cloud ComputingCloud Computing Client ComputingClient Computing
2.2. Data ModelData Model Cloud Computing
Storage, computing and all
kind of other resources will
mainly be provisioned by
the Cloud !!!
Grid Computing
Data Grids have been
designed to tackle data
intensive applications in
Grid environment.
RESOURCE MANAGEMENTRESOURCE MANAGEMENT
3.3. Data LocalityData Locality
In Grids:
Data storage usually relies on a shared file systems
(NFS, GPFS, PVFS…) where data locality cannot be
easily applied.
In Clouds:
One approach is to improve schedulers to be data-
aware, and to be able to leverage data locality
information when scheduling computational tasks.40
RESOURCE MANAGEMENTRESOURCE MANAGEMENT
4.4. Combine compute and data Combine compute and data
managementmanagement
Data-aware schedulers and dispersing data
close to processors is critical in achieving good
scalability and performance.
Grids have been making progress in combining compute
and data management with data-aware scheduler.
Clouds will face significant challenges in handling data-
intensive applications.41
RESOURCE MANAGEMENTRESOURCE MANAGEMENT
5.5. VirtualizationVirtualization Clouds need to run multiple applications, and all
the applications appear to the users as if they
were running simultaneously and could use all
the available resources in the Cloud.
Grids do not rely on virtualization as much as
Clouds do, but that might be more due to policy
and having each individual organization maintain
full control of their resources. (i.e. by not
virtualizing them).
42
RESOURCE MANAGEMENTRESOURCE MANAGEMENT
6.6. MonitoringMonitoring Grids in general have a different trust model in which users
via their identity delegation can access and browse
resources at different Grid sites and Grid resources are
not highly abstracted and virtualized as in Clouds.
Example: Ganglia-distributed monitoring system.
Virtualization brings to Clouds the potential difficulty in fine-
control over the monitoring of resources.
A significant challenge for Clouds, but it will become less important as
Clouds become more sophisticated and more less self-maintained and
self-healing.
43
RESOURCE MANAGEMENTRESOURCE MANAGEMENT
7.7. ProvenanceProvenance Provenance refers to the derivation history of
a data product, including all the data sources,
intermediate data products, and the procedures
that were applied to produce the data product.
Ex: Scientists can debug workflow execution,
validate or invalidate scientific results. In Grids, provenance management has been in general built
into a workflow system.
Provenance is still an unexplored area in Cloud
environments.44
PROGRAMMING MODELPROGRAMMING MODEL
Clouds have generally adopted Web Services
APIs where users access, configure and
program Cloud services using pre-defined APIs.
Although Clouds adopted some common
communication protocols such as HTTP and
SOAP, the integration and interoperability
of all the services and applications remain the
biggest challenges.
45
APPLICATION MODELAPPLICATION MODEL
Grids generally support many different kinds
of applications: from high performance
computing (HPC) to high throughput
computing (HTC).
Loosely coupled and tightly coupled applications
Cloud computing can support a similar set of
applications.
The one exception that will be hard to achieve in Cloud
Computing are HPC applications that require fast and
low latency network interconnects for efficient scaling
to many processors
46
APPLICATION MODELAPPLICATION MODEL
“ A Science Gateway is a community-
developed set of tools, applications and
data collections that are integrated via
a portal or a suite of applications. ”
Grid gateways interact with services and provide
rich user interactivity.
Cloud gateways have almost no interaction
between end-user.
47
SECURITY MODELSECURITY MODEL
Clouds mostly comprise dedicated data
centers belonging to the same organization.
Clouds environments is more homogeneous than Grids is.
The security model for Clouds seems to be
relatively simpler and less secure than the
security model adopted by Grids.
Security is one of the largest concerns for the
adoption of Cloud Computing.
Seven risks a Cloud user should raise with vendors before
committing ?48
SECURITY MODELSECURITY MODEL
49
Security
Long-term viability
Privileged user access
Regulatory compliance
Data Location
Data segregation
Recovery
Investigative support
SECURITY MODELSECURITY MODEL
1.Privileged user access: sensitive data
processed outside the enterprise needs the assurance
that they are only accessible and propagated to
privileged users.
2.Regulatory compliance: a customer needs to
verify if a Cloud provider has external audits and
security certifications and if their infrastructure
complies with some regulatory security requirements.
3.Data location: It is important that a Cloud
provider commit to storing and processing data in
specific jurisdictions and to obey local privacy
requirements on behalf of the customer.50
SECURITY MODELSECURITY MODEL
4.Data segregation: one needs to ensure that one customer’s data is fully segregated from another customer’s data.
5.Recovery: It is important that a Cloud provider has an efficient replication and recovery mechanism to restore data if a disaster occurs.
6.Investigative support: Cloud services are especially difficult to investigate, if this is important for a customer, then such support needs to be ensured with a contractual commitment.
7.Long-term viability: your data should be viable even the Cloud provider is acquired by another company. 51
REFERENCEREFERENCE R. Buyya et al., 2008, Market-Oriented Cloud Computing: Vision,
Hype, and Reality for Delivering IT Services as Computing Utilities, in Proceedings of the 2008 10th IEEE International Conference on High Performance Computing and Communications, HPCC '08, Page(s):5 – 13
Simon Ostermann, Alexandru Iosup, Nezih Yigitbasi, Radu Prodan, Thomas Fahringer, and Dick Epema, An Early Performance Analysis of Cloud Computing Services for Scientific Computing
Ian Foster, Yong Zhao, Ioan Raicu, Shiyong Lu, Cloud Computing and Grid Computing 360-Degree Compared
Mladen A. Vouk, Cloud Computing – Issues, Research and Implementations
www.ibm.com/developerworks/websphere/zones/hipods/ Internet (Product related homepage and review)
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Q & AQ & A
??53
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Thank youThank you
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