A Based System For Diversifying

As a modular, self-describing type of software service, Web services are self-contained applications that can be:

Service management combines the required services with organizational solutions and thus new generation user service becomes simplified. These forthcoming services are necessitated to be interrelated and combined in order to meet the demand of socio- economic factors such as environment analysis, safety measurement, climate management, agriculture modernization

Now our service is to acquire the user who has the most likelihood to be recommended. First we use the probability function $P(r,u)$ using the communication between the user and recommender. If the probability is higher than our defined threshold value then grouped the user. All the nodes in this group are our expected nodes. Now for the nearest one we run BFS and extract the best one for recommendation.

The most easy and reliable way to locate good recommendations is to ask your personal friends or business associates. By asking people whom you know and have used the services before, you can further ask specific questions to your needs. Furthermore, your friends may even provide more insights on the service level.

Thus, the flexibility in SOA is made possible by loose coupling. However, optimization should be restricted to certain situations in SOA like when the Quality of Service (QoS) profiles are fixed and predetermined. Unfortunately, in the SOA environment optimization is an unfeasible expectation because of the dynamic environment of web services. Moreover, web service providers might not be willing to publish and create the profiles even though using QoS profiles are more reliable than other less-flexible optimization techniques. Thus, web services provide an organization with loosely coupled reusable software. Thus, this flexibility allows interoperability between systems to accomplish business functions. Further, business services can be completely allocated and decentralized over the Internet and collected by numerous communications

(Lewis et al., 2000: 107 sited Nkomo and Cox, 1996) Diversity is not only represented by

Cloud Computing and Web Services have become well known services that companies have been using. Web services provide the ways that organizations can use applications to communicate with each other across the internet. Cloud computing provides various types of service, such as software as a service, where a provider gives the company software that they can use for their business functions. While this may seem that both types of services are different from each other, they do share similarities that companies should take notice. Service oriented architecture has also started to become a useful feature that helps with integration in companies. One problem is that some companies have difficulty in implementing service oriented architecture, but cloud computing has started to become an option that has caused an impact in service oriented architecture and integration.

(3) Web Services in the Cloud - instead of delivering full applications, this service allows users to access APIs for added functionality.

A Web Service is software which is available over the internet and uses the standardized XML messaging system. To encode the communications, XML is used. Web services are self-contained, modular, distributed, dynamic applications that can be described, published, located, or invoked over the network to create products, processes, and supply chains. These applications can be local, distributed, or web-based. Web services are built on top of open standards such as TCP/IP, HTTP, Java, HTML, and XML. Web Services have three main components – Simple Object Access Protocol (SOAP), Universal Description Discovery and Integration (UDDI) and Web Services Description Language (WSDL) and they are built on XML and HTTP platforms.

As stated previously, the adoption of service-oriented architecture can bring forth various complexities due to the nature of its design. For this reason, it is necessary for organizations to make a considerable initial investment into adopting a service-oriented architecture design. In order to make service-oriented architecture work well for an organization, there must first be a substantial analysis of every facet of the initiative. “The construction of services can be expensive and time-consuming, introducing a more burdensome project delivery lifecycle, further compounded by some of the top-down analysis requirements that may need to be in place before services can be built” (Erl, 2008, p. 88). The complexities of analyzing, developing, and implementing a service-oriented architecture initiative coupled with the monetary expenses, time-constraints, and potentially vexatious project lifecycle

Collaborative filtering: The simplest and original implementation of this approach recommends to the active user the items that other users with similar tastes liked in the past. The similarity in taste of two users is calculated based on the similarity in the rating history of the users. This is the reason why collaborative filtering is often referred as “people-to-people correlation.” Collaborative filtering is considered to be the most popular and widely implemented technique in Recommendation systems. An item-item approach models the preference of a user to an item based on ratings of similar items by the same user. Nearest-neighbors methods enjoy considerable popularity due to their simplicity, efficiency, and their ability to produce accurate and personalized recommendations.

All of the known recommendation techniques have strengths and weaknesses, and many researchers have chosen to combine techniques in different ways. Hybridization can alleviate some of the problems associated with other recommendation techniques. This system combines different recommender systems to improve information retrieval and gain better performance with fewer of the drawbacks of any individual one and also it can avoid the major disadvantages of other methods. The combination can be done using several alternatives, for example: weighted, switching, mixed, feature combination or augmentation or cascading. Meta techniques avoid the problem of sparsity by compressing ratings over many examples into a model, which can be more easily compared across users. Weighted, Mixed, Switching and Feature Combination. With these hybrids, it does not make sense to talk about the order in which the techniques are applied .The main shortcoming of hybrid methods, however, is increased implementation complexity and expense [14]. Table 10 proposes an overview of hybrid Recommender systems and its

Abstract—The revolution of cloud computing in IT industries enable the access of their infrastructure and application services on a subscription basis. As a result, several enterprises like IBM, Microsoft, Google, and Amazon have started to offer different cloud services to their customers. Due to the vast diversity in the available cloud services, from the customer’s point of view, it is difficult for them to decide whose services they should use and what is the basis for their selection. Currently, there is no ontology based system that can allow customers to evaluate cloud offerings and rank them based on their ability to meet the user’s Quality of Service (QoS) requirements. For that our ontology based cloud service providers ranking system is used. We designed this system as three modules, they are (i) processing of functional requirements (ii) processing of non-functional requirements and (iii) ranking of service providers which make the work simple. The ontology based system have information with explicit meaning, making it easier to automatically process and integrate information available for comparison, selection and ranking of cloud service providers using Analytic Hierarchy Process (AHP) to prioritizes the cloud services which make a significant impact and will create healthy competition among cloud providers to satisfy their Service Level Agreement (SLA) and improve their Quality of Services (QoS).

Abstract— The rising popularity of cloud computing makes building high quality cloud applications a critical and urgently required research problem. QoS rankings provide valuable information for making optimal cloud service selection from a set of functionally equivalent service candidates. To obtain QoS values, real-world invocations on the service candidates are usually required. The existing system focuses on QoS ranking prediction for cloud services by taking advantage of the past service usage experiences of other consumers. This framework requires no additional invocations of cloud services when making QoS ranking prediction. Two personalized QoS ranking prediction approaches are proposed to predict the QoS rankings directly. The Proposed system is designed to improve the ranking accuracy of the approaches by exploiting additional techniques. When a user has multiple invocations of a cloud service at different time, the proposed will explore selection ranking prediction approaches for cloud services by employing information of service users and cloud services. Selection ranking prediction approach provides reliable service to the user.

Previous work on service dependency detection include many different approaches. In order to compare the previous work, we first introduce some desirable effectiveness criteria for a dependency detection approach. Later, we use these criteria for reviewing and classifying the previous work.