Toward a More Accurate Web Service Selection Using Modified Interval DEA Models with Undesirable Outputs

With the growing number of Web services on the internet, there is a challenge to select the best Web service which can offer more quality-of-service (QoS) values at the lowest price. Another challenge is the uncertainty of QoS values over time due to the unpredictable nature of the internet. In this paper, we modify the interval data envelopment analysis (DEA) models [Wang, Greatbanks and Yang (2005)] for QoS-aware Web service selection considering the uncertainty of QoS attributes in the presence of desirable and undesirable factors. We conduct a set of experiments using a synthesized dataset to show the capabilities of the proposed models. The experimental results show that the correlation between the proposed models and the interval DEA models is significant. Also, the proposed models provide almost robust results and represent more stable behavior than the interval DEA models against QoS variations. Finally, we demonstrate the usefulness of the proposed models for QoS-aware Web service composition. Experimental results indicate that the proposed models significantly improve the fitness of the resultant compositions when they filter out unsatisfactory candidate services for each abstract service in the preprocessing phase. These models help users to select the best possible cloud service considering the dynamic internet environment and they help service providers to improve their Web services in the marke

Survey of context provisioning middleware

In the scope of ubiquitous computing, one of the key issues is the awareness of context, which includes diverse aspects of the user's situation including his activities, physical surroundings, location, emotions and social relations, device and network characteristics and their interaction with each other. This contextual knowledge is typically acquired from physical, virtual or logical sensors. To overcome problems of heterogeneity and hide complexity, a significant number of middleware approaches have been proposed for systematic and coherent access to manifold context parameters. These frameworks deal particularly with context representation, context management and reasoning, i.e. deriving abstract knowledge from raw sensor data. This article surveys not only related work in these three categories but also the required evaluation principles. © 2009-2012 IEEE

Achieving Autonomic Web Service Compositions with Models at Runtime

Over the last years, Web services have become increasingly popular. It is because they allow businesses to share data and business process (BP) logic through a programmatic interface across networks. In order to reach the full potential of Web services, they can be combined to achieve specifi c functionalities. Web services run in complex contexts where arising events may compromise the quality of the system (e.g. a sudden security attack). As a result, it is desirable to count on mechanisms to adapt Web service compositions (or simply called service compositions) according to problematic events in the context. Since critical systems may require prompt responses, manual adaptations are unfeasible in large and intricate service compositions. Thus, it is suitable to have autonomic mechanisms to guide their self-adaptation. One way to achieve this is by implementing variability constructs at the language level. However, this approach may become tedious, difficult to manage, and error-prone as the number of con figurations for the service composition grows. The goal of this thesis is to provide a model-driven framework to guide autonomic adjustments of context-aware service compositions. This framework spans over design time and runtime to face arising known and unknown context events (i.e., foreseen and unforeseen at design time) in the close and open worlds respectively. At design time, we propose a methodology for creating the models that guide autonomic changes. Since Service-Oriented Architecture (SOA) lacks support for systematic reuse of service operations, we represent service operations as Software Product Line (SPL) features in a variability model. As a result, our approach can support the construction of service composition families in mass production-environments. In order to reach optimum adaptations, the variability model and its possible con figurations are verifi ed at design time using Constraint Programming (CP). At runtime, when problematic events arise in the context, the variability model is leveraged for guiding autonomic changes of the service composition. The activation and deactivation of features in the variability model result in changes in a composition model that abstracts the underlying service composition. Changes in the variability model are refl ected into the service composition by adding or removing fragments of Business Process Execution Language (WS-BPEL) code, which are deployed at runtime. Model-driven strategies guide the safe migration of running service composition instances. Under the closed-world assumption, the possible context events are fully known at design time. These events will eventually trigger the dynamic adaptation of the service composition. Nevertheless, it is diffi cult to foresee all the possible situations arising in uncertain contexts where service compositions run. Therefore, we extend our framework to cover the dynamic evolution of service compositions to deal with unexpected events in the open world. If model adaptations cannot solve uncertainty, the supporting models self-evolve according to abstract tactics that preserve expected requirements.Alférez Salinas, GH. (2013). Achieving Autonomic Web Service Compositions with Models at Runtime [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/34672TESI

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Web services robustness testing

Web services are a new paradigm for building software applications that has many advantages over the previous paradigms; however, Web Services are still not widely used because Service Requesters do not trust services that were built by others. Testing can assuage this problem because it can be used to assess the quality attributes of Web Services. This thesis proposes a framework and presents a proof of concept tool that can be used to test the robustness and other related attributes of a Web Service. The tool can be easily enhanced to assess other quality attributes. The framework is based on analyzing Web Services Description Language (WSDL) documents of Web Services to find what faults could affect the robustness quality attributes. After that using these faults to build test case generation rules to assess the robustness quality attribute of Web Services. This framework will give a better understanding of the faults that may affect the robustness quality attribute of Web Services, how these faults are related to the interface or the contract of a Web Service under test, and what testing techniques can be used to detect such faults. The approach used in this thesis for building test cases for Web Services was used with many examples in order to demonstrate its effectiveness; these examples have shown that the approach and the proof of concept tool are able to assess the robustness of Web Services implementation and Web Services platforms. Four hundred and two test clients were automatically built by the tool, based on the test cases rules, to assess the robustness of these Web Services examples. These test clients detected eleven robustness failures in the Web Services implementations and nine robustness failures in the Web Services platforms. Also the approach was able to help in comparing the robustness of two different Web Services platforms, namely Axis and GLUE. After deploying the same Web Services in both of these platforms; Axis showed less robustness and security failures than GLUE

Service oriented computing for dynamic virtual learning environments

Using the Internet for teaching and learning has become a trend in modern higher education, facilitated through the exploitation of advanced computing technologies. Virtual Learning Environment (VLE) applications support online learning over the Internet, and VLEs have thus emerged as e-learning domains that are essential prerequisites in cutting edge design and implementation technologies in education. Service Oriented Computing (SOC), as a novel software development and implementation approach, has become an active area of research and development. Web services, as an example of SOC, support the integration of software applications in an incremental way, using existing platforms and languages that utilize and adopt existing legacy systems. Thus, VLEs should be particularly well suited to Web ser- vices through the SOC approach. VLE services is a field subjected to continuous development but VLEs as Web services are still not generally accessible for academic institutions, although they have been adopted by some scientific projects. The next generation of VLEs should address the limitations of the current online systems by providing a richer context for online learning, one that is sensitive to the specific domain requirements of e-learning. Web Services Matching and Selection (WSMS), as a part of the functional requirements of Web services, has received less attention from SOC researchers. It involves discovering a set of semantically equivalent services by filtering a set of available services based on service metadata, and instantaneously selecting the best possible service. WSMS is the discovery of a service by a user, where correspondence is established between the objectives of the consumer and the capabilities of the service. It thereby aims to match and select the optimal service that best meets the requestor's needs. The main aim of this doctoral work is to explore novel architectural designs for VLEs, based on the SOC paradigm and its related techniques. In addition, this investigation aims to extend the core ideas behind VLE tools, which are gradually becoming dominant within academic institutes. Another aim is to devise a policy- based technique to enforce security requirements for VLEs and to build a test-bed for VLE security based on Modular Moodle. The fundamental contribution of this thesis that it demonstrates that VLEs can be considered as services, which can be published, discovered and composed as perceived in the SOC paradigm. An additional contribution to the knowledge is that it has built a new extension to the structure of Web services: the Web Services Matching and Selection (WSMS) system. Another contribution to the knowledge is that traditional security requirements have been modified to cater for the highly mobile and changeable environment of VLEs; this has been achieved through policy- based techniques. These contributions to the body of knowledge have been published in learned journals and at conferences

IMPROVING NETWORK POLICY ENFORCEMENT USING NATURAL LANGUAGE PROCESSING AND PROGRAMMABLE NETWORKS

Computer networks are becoming more complex and challenging to operate, manage, and protect. As a result, Network policies that define how network operators should manage the network are becoming more complex and nuanced. Unfortunately, network policies are often an undervalued part of network design, leaving network operators to guess at the intent of policies that are written and fill in the gaps where policies don’t exist. Organizations typically designate Policy Committees to write down the network policies in the policy documents using high-level natural languages. The policy documents describe both the acceptable and unacceptable uses of the network. Network operators then take the responsibility of enforcing the policies and verifying whether the enforcement achieves expected requirements. Network operators often encounter gaps and ambiguous statements when translating network policies into specific network configurations. An ill-structured network policy document may prevent network operators from implementing the true intent of the policies, and thus leads to incorrect enforcement. It is thus important to know the quality of the written network policies and to remove any ambiguity that may confuse the people who are responsible for reading and implementing them. Moreover, there is a need not only to prevent policy violations from occurring but also to check for any policy violations that may have occurred (i.e., the prevention mechanisms failed in some way), since unwanted packets or network traffic, were somehow allowed to enter the network. In addition, the emergence of programmable networks provides flexible network control. Enforcing network routing policies in an environment that contains both the traditional networks and programmable networks also becomes a challenge. This dissertation presents a set of methods designed to improve network policy enforcement. We begin by describing the design and implementation of a new Network Policy Analyzer (NPA), which analyzes the written quality of network policies and outputs a quality report that can be given to Policy Committees to improve their policies. Suggestions on how to write good network policies are also provided. We also present Network Policy Conversation Engine (NPCE), a chatbot for network operators to ask questions in natural languages that check whether there is any policy violation in the network. NPCE takes advantage of recent advances in Natural Language Processing (NLP) and modern database solutions to convert natural language questions into the corresponding database queries. Next, we discuss our work towards understanding how Internet ASes connect with each other at third-party locations such as IXPs and their business relationships. Such a graph is needed to write routing policies and to calculate available routes in the future. Lastly, we present how we successfully manage network policies in a hybrid network composed of both SDN and legacy devices, making network services available over the entire network

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation