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ABSTRACT:
The computational grid is rapidly evolving into a service-oriented computing infrastructure that facilitates resource sharing and large-scale problem solving over the Internet. Service discovery becomes an issue of vital importance in utilizing grid facilities.
In this project presents ROSSE, a Rough sets-based search engine for grid service discovery. Building on the Rough sets theory, ROSSE is novel in its capability to deal with the uncertainty of properties when matching services. In this way, ROSSE can discover the services that are most relevant to a service query from a functional point of view. Since functionally matched services may have distinct nonfunctional properties related to the quality of service (QoS), ROSSE introduces a QoS model to further filter matched services with their QoS values to maximize user satisfaction in service discovery.
ROSSE is evaluated from the aspects of accuracy and efficiency in discovery of computing services.
This project Rough Sets based service matchmaking algorithm is used for service discovery that can deal with uncertainty of service properties. Experiment results show that the algorithm is more effective for service matchmaking than UDDI and OWL-S mechanisms.
MODULES:
1. Authentication
2. Remote configuration retrieval
3. Gathering Required Data.
4. Data Matching.
5. Data Analysis.
AUTHENTICATION:
While doing the inventory of several servers we realized that it was cumbersome to have to logon to and access the default System Information utility. At that time some of work should be confidential and important thing that should not done by all. So we done this module with two different users in that one has full privileges and another one has less.
REMOTE CONFIGURATION RETRIEVAL:
This module is a wrapper for some basic interfaces that provide detailed information about a system. The class allows the use of the current security context or a specified name and path to access either a remote or a local machine. Once connected the class is then populated with the values from the machine. This gives us an easy way of accessing the information without having worry about the calls to each of the interfaces and stores all the information.
REQUIRED DATA:
The administrators are used to prepare hardware to set up a configuration that will meet the needed performance, availability, and data integrity needs of applications and users. Hardware and Software ranges from low-cost minimum configurations that include only the components required for operation, to high-end configurations that include speed, storage, application and location.
Regardless of configuration, the use of required-quality hardware is recommended, as hardware malfunction is the primary cause of system down time.
Although all configurations provide availability, some configurations protect against every single point of failure. In addition, all configurations provide data integrity, but some configurations protect data under every failure condition. Therefore, administrators must fully understand the needs of their computing environment and also the availability and data integrity features of different hardware configurations in order to choose the system that will meet the proper requirements.
So here we made a pre preparation method that is to design a application that it get all the require data from the administrator and stores under a profile name for future ret rival purpose.
DATA MATCHING:
In this module we use rough set match making algorithm to find out the required configuration and is the one of important portion of this project. By select the profile and system id the matching will occur.
The rough set
The composed of the lower and upper approximation is called a rough set; thus, a rough set is composed of two crisp sets, one representing a lower boundary of the target set X and the other representing an upper boundary of the target set X.
The accuracy of the rough-set representation of the set X is given by the following:
That is, the accuracy of the rough set representation of X, αP(X), , is the ratio of the number of objects which can positively be placed in X to the number of objects that can possibly be placed in X – this provides a measure of how closely the rough set is approximating the target set. Clearly, when the upper and lower approximations are equal (i.e., boundary region empty), then αP(X) = 1, and the approximation is perfect; at the other extreme, whenever the lower approximation is empty, the accuracy is zero (regardless of the size of the upper approximation).
Boundary region
The boundary region, given by set difference, consists of those objects that can neither be ruled in nor ruled out as members of the target set X.
In summary, the lower approximation of a target set is a conservative approximation consisting of only those objects which can positively be identified as members of the set. (These objects have no indiscernible "clones" which are excluded by the target set.) The upper approximation is a liberal approximation which includes all objects that might be members of target set. (Some objects in the upper approximation may not be members of the target set.) From the perspective of, the lower approximation contains objects that are members of the target set with certainty (probability = 1), while the upper approximation contains objects that are members of the target set with non-zero probability (probability >0)