What is grid computing
and how is it different from cloud computing?
If you do not know what grid computing is, we must say that
this term is a literal translation of Grid Computing. But what is grid
computing? Simply put, grid computing is a distributed architecture that uses a
group of resources to work together.
To date, the effective application and benefits of grid
computing have been proven in various industries such as information
technology, automotive, aerospace, astronomy, physical sciences, experimental
sciences and even the media industry. In fact, organizations can do their work
faster by distributing activities among the nodes of a mesh computing network,
regardless of geographical dispersion.
In this article, we introduce grid computing, its components
and types. We also examine the difference between grid computing and cloud
computing and compare them from different dimensions. If you work in the field
of information technology or are interested in learning its topics, we
recommend you to read this article.
What is grid computing
or grid computing?
Grid computing, also known as network computing or mesh
computing, is a network-based computing model that has the ability to process
large amounts of data with the help of a group of networked computers. In other
words, mesh computing is a vast network of interconnected computers that
coordinate with each other to solve a common problem and work on solving it by
dividing into several small units. These computers work together on a network
and under a specific protocol as a virtual supercomputer to solve a complex
problem.
Grid computing is a system for connecting a large number of
computer nodes in a distributed architecture that provides the necessary
computing resources to solve complex problems. These nodes can be servers or
personal computers from different geographic locations that are connected to each
other through the Internet or other networks. Grid computing uses the resources
available to each node to execute independent instructions that eventually lead
to a larger result.
How Grid Computing Works: It is a grid-based computing model
that has the ability to process large amounts of data with the help of a group
of networked computers.
In fact, mesh computing provides the necessary architecture
to create a virtual supercomputer using distributed nodes. Most grid computing
projects do not have a specific time and large projects are usually distributed
among different countries and continents. In many cases, a grid computing
system uses a free node in the network to perform grid activities; A process
known as cycle-scavenging or CPU-scavenging. These activities can run in the
background for weeks or months.
The participation of each node in a grid computing network is
possible through the implementation of a specialized grid computing software. A
grid network requires a controller node (usually a server) to manage operations
and schedule activities. In addition, the grid network uses middleware to
create and control the network, manage processes between nodes, and create
interaction between different components.
In a mesh computing environment, there is a high probability
of computing nodes being interrupted or removed. For this reason, organizations
using such networks should consider alternative nodes and robust recovery
models. Also, organizations should consider security measures for data protection
as well as quality control.
For example, in many grid networks, the controls are very
limited and it is very simple to create a grid project or join a project. While
if security and control issues are of high priority, the organization must
ensure appropriate controls are in place to implement and protect resources.
Key components of mesh
computing
The grid computing space contains a set of network members,
and according to the network design, some components may or may not always be
members of it. Although the composition of these components can be different
depending on the project, it is important to understand their role in the
network and solve the problems. The most important components of a grid network
are:
Key components of grid computing include user interface,
security, scheduling, data management, resource management, and activities.
User Interface: Nowadays, users are very familiar with web
portals. By providing a single interface, these portals allow users to view a
wide range of information. Similarly, a grid portal provides users with an
interface to run the required applications on the resources provided by the
network. It is a portal-style user interface that helps users to efficiently
search and perform various functions on grid networks. In fact, the mesh
network user sees a large virtual computer that provides the computing
resources he needs; Like an internet user viewing integrated content on the
web.
Security: Security is one of the main concerns in the grid
computing space and includes mechanisms such as authentication, authorization,
data encryption. The Grid Security Infrastructure (GSI) is a key element here,
enabling secret and tamper-free interactions between software entities running
on a Grid network. This infrastructure includes an OpenSSL implementation that
provides a login mechanism for users to perform their activity within the mesh
network. GSI ensures strong network security by providing authentication and
authorization mechanisms to protect the system.
Scheduler: After identifying grid network resources, the
second step is to schedule activities to run on them. A scheduler may not be
needed to run independent activities that are not interdependent. However, to
perform certain tasks at the same time that require interpersonal
communication, the use of a scheduler is necessary to coordinate the execution
of activities. Therefore, timers can operate at different levels in the
environment of grid networks.
Data management: Data management is a critical component for
grid computing. A secure and reliable mechanism for transferring or using data
between different nodes in a mesh computing network is of particular
importance. Data management tools use GSI to authenticate and authorize users
by creating different protocols, and each user in the network can transfer data
after authentication.
Resource and activity management: The resource and activity
management element enables the execution of a task on a specific resource,
checks its status and retrieves its results. These programs are aware of the
available and available resources of the Grid network and divide the activities
by considering the available facilities. This element helps to better manage
work and efficient resources active in grid networks.
What are the types of
Grid Computing?
Grid computing can be divided into several categories based
on its application and calculations, which include:
Computational grid
computing:
Today, computing grid networks account for the largest share
of network computing use among industries, and this trend is expected to
continue in the coming years. Computational grid computing is the best option
for performing activities that take more time than expected. In such cases, the
main activity is broken into a number of smaller activities, each of which is
executed in parallel on a grid network node. After all the processes are
completed, the results are combined together to create the result of the main
activity. In this way, the time to perform the main activity is reduced.
Computing networks are also used in the daily actions of
businesses. For example, using a computing network can increase the speed of
reporting for an online business. Since time is an important element for the
customer, such businesses can speed up the production of their reports from
minutes to seconds by using computing networks. Such networks lead to
significant performance improvements compared to traditional systems.
Data grid computing:
Data grids are networks that distribute data between
different computers. Similar to computing grids, data grids allow data to be
placed on a network of computers or memory resources. However, the grid network
treats all of this data as a single data, despite being split. Data grid
computing allows different users to simultaneously access, modify or transfer
distributed data.
For example, a data grid can be used as a large data center
where each website can store its data on the network. In this way, the network
enables the coordinated sharing of data among all network users. In addition,
with data grid, it is possible to transfer knowledge among network users.
Collaborative grid computing:
Collaborative grid computing solves the created problems by
creating a seamless collaboration. This type of calculation takes advantage of
various technologies that make it possible to do work between people. Since in
this network people have easy access to each other's activities and
information, the productivity and creativity of the workforce improves, which
ultimately benefits organizations.
In addition, this network overcomes geographical distances
and by creating the possibility of remote work for people, it ultimately leads
to a better experience and job satisfaction. For example, in a collaborative
network, all users can access and work on text documents, images, and other
work files simultaneously.
Manuscript grid
computing:
Manuscript grid computing is used in the management of images
and voluminous texts. These grids enable continuous storage of various images
and texts while processing and performing operations on previous data. This
grid is a simple grid computing framework in which large volumes of text or
images are processed in parallel.
Modular grid computing:
Modular mesh computing refers to the separation of computing
resources in a system, which can include storage, GPU, memory, and network. IT
teams can take advantage of the resources and computing power of this network
to support their specific applications or services.
In general, in a modular grid, a set of resources are
combined with a software to perform a specific action. For example, when the
CPU and GPU required by a project are located on different servers, they can be
connected to each other with maximum speed and minimum delay and build the
required system by using grid computing.
In this way, when a software is built, a set of computing
resources and services can be provided to support it. Conversely, when that
program expires, its processing resources are freed and used for other
programs. Original equipment manufacturers (OEMs) play a key role in the
formation of these networks, as connectivity between equipment is of
significant importance.
Network grids, utility grids, information grids, and service
grids are examples of mesh networks that are used in other cases.
The difference between
grid computing and cloud computing
Grid Computing (Grid Computing) and Cloud Computing (Cloud
Computing) have a relatively similar concept and therefore may be easily confused
with each other. In fact, both have the same vision of providing services to a
large number of users by sharing resources among them. Cloud computing and mesh
computing are both based on network technology and have multitasking
capabilities; This means that users can access one or more application
instances to perform different functions. But it must be said that cloud
computing is another type of distributed computing that is different from grid
computing and is somewhere between grid computing and supercomputers.
Clouds are much more granular than grid networks and can
handle time-dependent activities more effectively. Although cloud resources can
be geographically distributed, they are located in a more limited space
compared to thousands or millions of nodes scattered in a mesh computing
network.
Another difference between grid and cloud is that grid
computing can be considered the previous generation of cloud computing, which
has played a prominent role in global computing. In fact, cloud computing can
pose a serious threat to grid computing in the long run. Centralization of
servers in cloud computing creates less free time for servers and reduces the
number of unused systems.
Grid computing involves the virtualization of computing
resources to store massive amounts of data. While cloud computing is where the
application does not have direct access to resources; Rather, it accesses them
through one of the cloud computing services (PaaS, SaaS and IaaS). On the other
hand, resources in grid computing are distributed over the network; But in
cloud computing, resources are managed centrally.
The main differences between Grid Computing and Cloud
Computing can be summarized in the following table:
5 applications of grid
computing
Grid computing is used in government agencies, universities,
and various organizations to coordinate projects and solve problems. The
computational power of grid networks can be used for a variety of projects,
such as genetic research, drug compliance monitoring, government safety
programs, and historical research.
Grid computing can also be used to perform various analyses,
such as modeling financial risks, studying earthquake activity, or analyzing
weather patterns. In addition, mesh computing can play a key role in pervasive
computing; Where smart devices penetrate the environment without the user's
direct knowledge
Mesh computing architecture can provide enormous processing
power to solve a problem. However, the distributed model only works well for a
small set of applications.
Science
The field of experimental sciences is one of the practical
and growing fields of mesh computing, which has been rapidly adopted by various
disciplines such as computational biology, bioinformatics, genomics, and
neuroscience. Specialists in this field can access and collect and extract the
data they need by using grid computing. By using this network, it is possible
to run various simulations and analyzes on a large scale, and it is easy to
connect remote tools to the existing medical infrastructure.
Engineering science
The application of grid computing in reducing the costs
required for projects that require a lot of resources is very wide. Various
engineering service sectors that require joint efforts in designing and testing
large data, such as the automotive or aerospace industries, have chosen mesh
network technology. Grid computing is also effective in the development of this
sector and accelerates the trends related to the growth of engineering
programs.
Data oriented services
Today, data is everywhere; From sensors to smart gadgets and
all devices that are connected to IOT technology; In this situation of data
explosion, grades play a key role. These networks can be used to collect, store
and analyze data and simultaneously extract patterns from them to create
knowledge.
Scientific-research
collaborations
Universities and organizations participating in research
programs have a large amount of data to analyze and process. Organizations
involved in these projects need significant space to store large data and
advanced computing resources to process them.
In such cases, grid computing provides mechanisms for
resource sharing by creating a virtual organization to provide computing
capabilities. This virtual supercomputer facilitates the sharing of resources
on demand and creates a secure framework for data access and exchange.
Commercial applications
Grid networks support various business sectors that require
powerful computing resources, such as online games and the game industry. In these
grades, resources are considered based on computational needs; Factors such as
traffic and number of contributors.
In addition, the use of grid computing in the media industry
increases the visual appeal of movies by adding special effects. Also, in the
film production process, it reduces the production time by creating the
possibility of simultaneous processing of different parts.
final word
As mentioned, grid computing connects different computing
resources such as personal computers, servers, and physical memories to each
other and provides a unique mechanism to access and use them as a single and
integrated resource. This has been able to increase the efficiency of computing
services by providing transparent access to resources and speed things up.
Despite the differences between grid computing and cloud
computing, it is possible to use the cloud to support grid-based applications;
In the form of complete or combined infrastructure. In this way, organizations
can take advantage of the benefits of grid computing, and use some of the
benefits of cloud computing, such as scalability or service payment models.
This strategy can be useful for organizations that have already invested in
grid computing.
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