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AUTOMATIC GRID’S CHANGING POWER
SUPPLY UNIT INTERFACE WITH MOTORS AND
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ABSTRACT:
Grid” computing has emerged as an important new field, distinguished from conventional
distributed computing by its focus on large-scale resource sharing, innovative applications, and, in
some cases, high-performance orientation. In this article, we define this new field. First, we review
the “Grid problem,” which we define as flexible, secure, coordinated resource sharing among
dynamic collections of individuals, institutions, and resources—what we refer to as virtual
organizations. In such settings, we encounter unique authentication, authorization, resource access,
resource discovery, and other challenges. It is this class of problem that is addressed by Grid
technologies. Next, we present an extensible and open Grid architecture, in which protocols,
services, application programming interfaces, and software development kits are categorized
according to their roles in enabling resource sharing. We describe requirements that
we believe any such mechanisms must satisfy and we discuss the importance of defining a compact
set of intergrid protocols to enable interoperability among different Grid systems. Finally, we discuss
how Grid technologies relate to other contemporary technologies, including enterprise integration,
application service provider, storage service provider, and peer-to-peer computing. We maintain
that Grid concepts and technologies complement and have much to contribute to these other
approaches.
• Computational services. These are concerned with providing secure services for executing
application jobs on distributed computational resources individually or collectively. Resources
brokers provide the services for collective use of distributed resources. A Grid providing
computational services is often called a computational Grid. Some examples of computational Grids
are:the World Wide Grid and the NSF TeraGrid. • Data services. These are concerned with proving
secure access to distributed datasets and their management. To provide a scalable storage and
access to the data sets, they may be replicated, catalogued, and even different datasets stored in
different locations to create an illusion of mass storage. The processing of datasets is carried out
using computational Grid services and such a combination is commonly called data Grids. Sample
applications that need such services for management, sharing, and processing of large datasets are
high-energy physics [10] and accessing distributed chemical databases for drug design . • Application
services. These are concerned with application management and providing access to remote
software and libraries transparently. The emerging technologies such as Web services [12] are
expected to play a leading role in defining application services. They build on computational and
data services provided by the Grid. An example system that can be used to develop such services is
NetSolve . Information services. These are concerned with the extraction and presentation of data
with meaning by using the services of computational, data, and/or application services. The lowlevel details handled by this are the way that information is represented, stored, accessed, shared,
and maintained. Given its key role in many scientific endeavors, the Web is the obvious point of
departure for this level. • Knowledge services. These are concerned with the way that knowledge is
acquired, used, retrieved, published, and maintained to assist users in achieving their particular
goals and objectives. Knowledge is understood as information applied to achieve a goal, solve a
problem, or execute a decision. An example of this is data mining for automatically building a new
BLOCK DIAGRAM:
Phase role
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motor
H-bridge
Micro controller
switch
switch
Phase role