Slide 2 Cloud Computing and Grid Computing 360-Degree Compared
Rashid Tahir Slide 3 Slide 4 Then to now! 1961: John McCarthy
predicts that Computation may someday be organized as a public
utility Mid 1990s: the term Grid is coined by the community 2002:
Ian Foster publishes a grid checklist. July 2002: Amazon launches
its AWS. Subsequent Years: Large-scale federated systems such as
TeraGrid, Open Science Grid etc. are developed. Slide 5
Definitions!! Grid Computing: The ability, using a set of open
standards and protocols, to gain access to applications and data,
processing power, storage capacity and a vast array of other
computing resources over the Internet. A grid is a type of parallel
and distributed system that enables the sharing, selection, and
aggregation of resources distributed across multiple administrative
domains based on their (resources) availability, capacity,
performance, cost and users quality-of- service requirements. 1 1-
IBM Solutions Grid for Business Partners: Helping IBM Business
Partners to Grid-enable applications for the next phase of
e-business on demand Slide 6 Characteristics of a Grid Coordinates
resources that are not subject to centralized control Uses
standard, open, general-purpose protocols and interfaces Delivers
non-trivial qualities of service Slide 7 Definitions!! Cloud
Computing: A large-scale distributed computing paradigm that is
driven by economies 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. Slide 8 Characteristics of a
Cloud Massively scalable Can be encapsulated as an abstract entity
that delivers different levels of services to customers outside the
Cloud Driven by economies of scale Can be dynamically configured
(via virtualization or other approaches) and delivered on demand
Slide 9 The BIG Question!!! Is Cloud Computing just a new name for
Grid? YES: The vision is the same. BUT NO: The approach is
different. NEVERTHELESS, YES: The problem space, both paradigms
attempt to solve, is overlapping. Slide 10 Why cloud computing?
Exponentially growing data size in scientific
instrumentation/simulation and Internet publishing and archiving
Wide spread adoption of Computing Services and Web 2.0 applications
Rapid decrease in hardware cost and increase in computing power and
storage capacity, and the advent of multi-core architecture and
modern supercomputers consisting of hundreds of thousands of cores
Slide 11 Relationship between Clouds and other domains it overlaps
with Slide 12 Side-by-Side: Grids vs. Clouds Six comparison
metrics: Business Model Architecture Resource Management
Programming Model Application Model Security Model Slide 13
Business Model CLOUDGRID On-demand pay-per-use type Model Services
are charged usually on the basis of per instance-hour, per GB-Month
of storage, per TB/Month data transfer etc Project-oriented Model
Users pool resources that are shared by the community Each Grid
site is responsible for maintaining their own set of resources
Slide 14 Architecture GRID Grids provide protocols and services at
five different layers 1.Fabric Layer: Provides access to different
resource types such as compute, storage and network resource, code
repository etc. 2.Connectivity Layer: Defines core communication
and authentication protocols for easy and secure network
transactions. 3.Resource Layer: Defines protocols for the
publication, discovery, negotiation, monitoring, accounting and
payment of sharing operations on individual resources. 4.Collective
Layer: Captures interactions across collections of resources.
5.Application Layer: Comprises whatever user applications built on
top of the above protocols and APIs and operate in VO (Virtual
Organization) Environments. Slide 15 Architecture CLOUD Clouds
architecture can be divided in to four different layers: 1.Fabric
Layer: Contains the raw hardware level resources. 2.Unified
Resource Layer: Contains resources that have been
abstracted/encapsulated (usually by virtualization) so that they
can be exposed to upper layer. 3.Platform Layer: Adds on a
collection of specialized tools, middleware and services on top of
the unified resources. 4.Application Layer: Contains the
applications that would run in the Clouds. Slide 16 Architecture
Grids vsClouds Slide 17 Architecture CLOUD Clouds provide services
at three different levels: 1.IaaS: Provisions hardware, software,
and equipments to deliver software application environments with a
resource usage-based pricing model. 2.PaaS: Offers a high-level
integrated environment to build, test, and deploy custom
applications. 3.SaaS: Delivers special-purpose software that is
remotely accessible by consumers through the Internet with a
usage-based pricing model. Together the three are referred to as
the Cloud Computing Onion. Slide 18 Cloud Computing Onion Slide 19
Resource Management Comparison based on the following metrics:
Compute Model Data Model Data Locality Combining Compute and Data
Management Virtualization Monitoring Provenance Slide 20 Resource
Management CLOUDGRID Compute Model Users simultaneous share
resources and are serviced instantly Queuing based batch-scheduled
model Data ModelCurrent data model houses the data inside the cloud
however in the future this could change Specially designed
Data-Grids along with metadata catalogs that keep track of the
location of each piece of data Data LocalityHigh focus on storing
and replicating data near the associated compute unit Data is
stored in shared file systems where data locality can not easily be
applied. However data-aware schedulers dramatically improve
performance Combining Compute and Data Management Initially clouds
were not used much for data-intensive applications so not much work
had been done to manage large amounts of data over compute
resources. However this has changed substantially now Grids achieve
this well because of the usage of data-aware schedulers to schedule
jobs close to the node responsible for computing them Slide 21
Resource Management CLOUDGRID VirtualizationHighly virtualized to
meet service level agreements. Abstractions provided at each layer
to assist the process of virtualization Little or no support for
virtualization MonitoringSince services provided are layer-
specific, monitoring information is not provided for the entire
system Users have greater flexibility over the resources they are
allocated and hence can deploy fine grained monitoring
infrastructure ProvenanceStill an under-explored area however given
that clouds are increasingly being used for e-science research new
provenance systems are emerging fast Since Grids are project
oriented, provenance is essential and therefore Grids provide a lot
of support for this Slide 22 Programming Model CLOUDGRID Most
common parallel programming model is MapReduce Standard parallel
programming models are also used Scripting is used in place of
workflow management systems Clouds have generally adopted Web
Services APIs for providing services over the web. The most
commonly used parallel programming model is based on message
passing Less used models employ coordination languages that allow
heterogeneous components to coordinate and interact In a recent
effort to develop a service oriented Grid programming model the
community has started using WSRF (Web Services Resource Framework)
Workflow Management Systems are used when processing large sets of
data involving complex tasks Slide 23 Application Model CLOUDGRID
Cloud computing is still in its infancy so the app space has not
yet been clearly understood however one can characterize most
applications as loosely coupled All three layers mentioned
previously provide their own set of functionalities and
applications and tools are being developed that exploit these
functionalities Grids support a wide variety of applications such
as High Performance Computing (HPC) and High Throughput Computing
(HTC) apps Tightly coupled applications make use of the Message
Passing Interface (MPI) for inter-process communication whereas
loosely coupled apps rely on Workflow Management Systems Another
emerging set of apps are the scientific gateways apps. These
provide a large variety of services through a browser-based user
interface Slide 24 Security Model CLOUDGRID Clouds mostly comprise
dedicated data centers with most of the infrastructure homogeneous
in nature however when cross-data center interaction occurs
compatibility issues arise Clouds seem to have relatively simpler
and less secure security models Security is the biggest obstruction
to the wide scale adoption of cloud computing Grids are built under
the assumption that the shared resources will be mostly
heterogeneous in nature and hence are better equipped for
challenges pertaining to interoperability and compatibility Since
each Grid site has operational autonomy, they have security
engineered in to them by the administrators Security model is more
complex as compared to Clouds and perhaps more time consuming too
Slide 25 Conclusion Clouds and Grids share a common vision and have
overlapping architecture, technology and application space. However
they take different steps to provide scalable distributed on-demand
computation resulting in the evolution of two parallel
infrastructures. Tomorrows distributed systems will need to have
the centralized scale of todays Cloud utilities and the
distribution and interoperability of todays Grid facilities. Slide
26 Discussion Questions Is Cloud computing the same as Grid
computing? Why? Why not? What does the future entail for each of
these paradigms? Can you see a unified distributed paradigm in the
future? Why? Why not? Will the community shift more or more towards
Clouds and Grids or do you envision a future where high-end desktop
machines will dominate the market? Do you think Clouds and Grids
are reliable enough to be trusted with sensitive data and
computation? Currently Clouds cant guarantee reliability, security
and control however they provide ease of access, portability etc at
a very low cost. Do you think the benefits outweigh the costs?
Slide 27 References 1- Cloud Computing and Grid Computing 360
Degree Compared Authors: Ian Foster, Yong Zhao, Ioan Raicu, Shiyong
Lu This paper appears in: Grid Computing Environments Workshop,
2008. GCE '08
