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Combining Efficiency, Fidelity, and Flexibility in Resource Information Services


Combining Efficiency, Fidelity, and Flexibility in
Resource Information Services

Abstract:

A large-scale resource sharing system (e.g., collaborative cloud computing and grid computing) creates a virtual supercomputer by providing an infrastructure for sharing tremendous amounts of resources (e.g., computing, storage, and data)distributed over the Internet. A resource information service, which collects resource data and provides resource search functionality for locating desired resources, is a crucial component of the resource sharing system. In addition to resource discovery speed and cost (i.e., efficiency), the ability to accurately locate all satisfying resources (i.e., fidelity) is also an important metric for evaluating service quality. Previously, a number of resource information service systems have been proposed based on Distributed Hash Tables (DHTs) that offer scalable key-based lookup functions. However, these systems either achieve high fidelity at low efficiency, or high efficiency at low fidelity. Moreover, some systems have limited flexibility by only providing exact-matching services or by describing a resource using a pre-defined list of attributes. This paper presents a resource information service that offers high efficiency and fidelity without restricting resource expressiveness, while also providing a similar-matching service. Extensive simulation and PlanetLab experimental results show that the proposed service outperforms other services in terms of efficiency, fidelity, and flexibility; it dramatically reduces overhead and yields significant enhancements in efficiency and fidelity.







Algorithm:

Ø Cooperative Game Theory.[Sharing]
User friendly file sharing
u1 + v2 ≥ α
u2 + v2 ≥ α
u3 + v1 ≥ α
Ø Upload, Download Algorithm.
File, image upload download.
Ø Distributed Hase Table.
Store The File.


Key points:

1.      File Uploading, Downloading.
2.      Data Sharing [user to user]







EXISTING SYSTEM
                        The system then maps the resource point to a DHT node. This guarantees that all existing resources that match a query are foundwith bounded costs in terms of the number ofmessages and nodes involved. A resource has a vector, the size of which is the number of dimensions. PIRD relies on an existing LSH technique in Euclidean spaces [32] to create a number of IDs for a resource, and then maps the resource to DHT nodes. In a system with a tremendous number of resource attributes, PIRD leads to dramatically high memory consumption and low efficiency of resource ID creation due to long resource vectors.


PROPOSED SYSTEM
Schmidt and Parashar  proposed a dimension reducing indexing scheme for resource discovery. They built a multidimensional space with each coordinate representing a resource attribute. The Fig shows an example of a 3-dimensional
keyword space. The resources are viewed as base- numbers, where is the total number of attributes in the grid system. Since one-point mapping  and PIRDbuild a pre-defined attribute list, they are not sufficiently flexible in dealing with new attributes. To overcome this problem, our proposed LIS builds new LSH functions to transform resources to resource IDs, which does not require a pre-defined attribute list. Thus, LIS significantly reduces memory consumption and improves the efficiency of resource ID creation. All methods approximately only need no more than 3 ms. This result indicates that our proposed load balancing algorithm only generates a very short latency.



Advantage
v Easy to Share files
v User to User File Sharing
v Provide files.


MODULE DESCRIPTION
          MODULE
                   Case Study and Data Collection
v User
v Admin Authentication
v Cloud

MODULE DESCRIPTION
Case Study and Data Collection

 We consider a case study of a web-based collaboration application for valuating performance. The application allows users to store, manage, and share documents and drawings related to large construction projects. 

 The service composition required for this application includes: 

 Firewall (x1), Intrusion Detection (x1), Load Balancer (x1), Web Server (x4), Application Server (x3), Database Server (x1), Database Reporting Server (x1), Email Server (x1), and Server Health Monitoring (x1). To meet these requirements, our objective is to find the best Cloud service composition




1.    USER
 A common approach to improve reliability and other QoS parameters of a service composition is by dynamic service selection at run time. In a dynamic service composition a set of functionally equivalent services exists for each service invocation and actual services are incorporated into the execution configuration depending on their most recent QoS parameters.  However, two dominant issues limit the application of dynamic compositions on a larger scale: service selection and detection of equivalent services. Since service selection at run time is bonded by additional constraints, like statefullness and composability, statebased reliability models need to be applied. However, such models are prone to state explosions, making it difficult to support more complex compositions. The other commonly used approach treats service selection as an optimization problem.

Ø Share Data
The user can share their data into another user in same group the data will translate by path setting data.
Ø Upload File
The user can upload the file to cloud. And the Admin can allow the data to store the cloud.

Ø Download File
The user also download the cloud file by the conditions.




2.    Admin Authentication
          we propose an iterative reliability improvement method for service compositions based on the extension of our previous work in [20]. The method consists of: reliability estimation, weak point recommendation and weak point strengthening steps, as defined by the overview. In the rest of this section, we briefly describe each of the stated steps.

Ø Accept user
The admin can accept the new user request and also black the users.
Ø Allow user file
The users can upload the file to cloud. And the admin can allow the files to cloud then only the file can store the cloud.

3.    CLOUD
 However, other SOA implementations can be expected to gain more traction in the coming years with the continuous proliferation of cloud computing and increasing popularity of software as a service (SaaS) platforms [4], [5]. One of the most pronounced benefits of SOA are service compositions, component-based applications built by combining the existing services. The concept of compositions makes SOA particularly popular in designing a large variety of  systems that benefit from clear separation of interests. For instance, when designing enterprise systems, different segments of functionality within a business process can be developed independently by different organizational units. However, designing service compositions also presents additional challenges as services can be deployed by third parties over which the composition developer has no supervision. A strong concern in such an environment is the necessity to design a composition with an adequate level of non-functional properties, like reliability, availability or other Quality of Service (QoS)parameters.





Software Requirements:

  • Technologies               : Asp .Net and C#.Net
  • Database                     : MS-SQL Server 2005/2008
  • IDE                             : Visual Studio 2008

Hardware Requirements:

  • Processor                     : Pentium IV
  • RAM                           : 1GB

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