Service-Oriented Architectures (SOA) enable the automatic creation of business applications from independently developed and deployed Web services. As Web services are inherently a priori unknown, how to deliver reliable Web services compositions is a significant and challenging problem. Services involved in an SOA often do not operate under a single processing environment and need to communicate using different protocols over a network. Under such conditions, designing a fault management system that is both efficient and extensible is a challenging task. In this article, we propose SFSS, a self-healing framework for SOA fault management. SFSS is predicting, identifying, and solving faults in SOAs. In SFSS, we identified a set of high-level exception handling strategies based on the QoS performances of different component services and the preferences articled by the service consumers. Multiple recovery plans are generated and evaluated according to the performance of the selected component services, and then we execute the best recovery plan. We assess the overall user dependence (i.e., the service is independent of other services) using the generated plan and the available invocation information of the component services. Due to the experiment results, the given technique enhances the service selection quality by choosing the services that have the highest score and betters the overall system performance. The experiment results indicate the applicability of SFSS and show improved performance in comparison to similar approaches.

Heterogeneous datacenters, with a variety of compute, memory, and network resources, are becoming increasingly popular to address the resource requirements of time-sensitive applications. One such application framework is the TensorFlow platform, which has become a platform of choice for running machine learning workloads. The state-of-the-art TensorFlow platform is oblivious to the availability and performance profiles of the underlying datacenter resources and does not incorporate resource requirements of the given workloads for distributed training. This leads to executing the training tasks on busy and resource-constrained worker nodes, which results in a significant increase in the overall training time. In this paper, we address this challenge and propose architectural improvements and new software modules in the default TensorFlow platform to make it aware of the availability and capabilities of the underlying datacenter resources. The proposed Infrastructure-Aware Tensor-Flow efficiently schedules the training tasks on the best possible resources for execution and reduces the overall training time. Our evaluation using the worker nodes with varying availability and performance profiles shows that the proposed enhancements yield up to 54 % reduced training time as compared to the default TensorFlow platform.

The proliferation of private clouds that are often underutilized and the tremendous computational potential of these clouds when combined has recently brought forth the idea of volunteer cloud computing (VCC), a computing model where cloud owners contribute underutilized computing and/or storage resources on their clouds to support the execution of applications of other members in the community. This model is particularly suitable to solve big data scientific problems. Scientists in data-intensive scientific fields increasingly recognize that sharing volunteered resources from several clouds is a cost-effective alternative to solve many complex, data- and/or compute-intensive science problems. Despite the promise of the idea of VCC, it still remains at the vision stage at best. Challenges include the heterogeneity and autonomy of member clouds, access control and security, complex inter-cloud virtual machine scheduling, etc. In this paper, we present CloudFinder, a system that supports the efficient execution of big data workloads on volunteered federated clouds (VFCs). Our evaluation of the system indicates that VFCs are a promising cost-effective approach to enable big data science.

Software as a service is a well accepted software deployment and distribution model that has grown exponentially in the last few years. One of the biggest benefits of SaaS is the automated composition of different services in a composite system. It allows users to automatically find and bind these services (to maximize the productivity), meeting both functional and non-functional requirements. In this paper, we present a framework for modeling the dependency relationships among different Quality of Service parameters of the component services. Our proposed approach considers the different invocation patterns of component services, and presents a service composition framework to model the dependencies and uses the global QoS for service selection. We evaluate the efficiency of our proposed technique on the WSDream-QoS Dataset [1] .

We provided an XACML-based implementation of a semantic-based privacy management framework that incorporates context into dynamic rule evaluation and decision enforcement. Our evaluation results are promising, and we believe that future enhancements on the current implementation can provide a foundation for modern health records infrastructures and inspire collaborative data sharing.

In this article, we propose a Statistical Cloud-Assisted Reputation Estimation (sCARE) approach for service-oriented environments in uncertain situations. sCARE uses the ratings from cooperating service consumers to uniformly describe the randomness and fuzziness of the different submitted ratings and their associated relationships in quantitative terms. We also define discriminant functions to model the honesty (or lack thereof) of the service raters. Experiment results show that our proposed model performs in a fairly accurate manner for a number of real-world scenarios. A comparison study with similar existing works is also provided to asses sCARE’s performance.

Crowdsourcing is emerging as a powerful paradigm to help perform a wide range of tedious tasks in various enterprise applications. As such applications become more complex, crowdsourcing systems often require the collaboration of several experts connected through professional/social networks and organized in various teams. For instance, a well-known car manufacturer asked fans to contribute ideas for the kinds of technologies that should be incorporated into one of its cars. For that purpose, fans needed to collaborate and form teams competing with each others to come up with the best ideas. However, once teams are formed, each one would like to provide the best solution and treat that solution as a “trade secret,” hence preventing any data leak to its competitors (i.e., the other teams). In this article, we propose a data leak--aware crowdsourcing system called SocialCrowd . We introduce a clustering algorithm that uses social relationships between crowd workers to discover all possible teams while avoiding interteam data leakage. We also define a ranking mechanism to select the “best” team configurations. Our mechanism is based on the semiring approach defined in the area of soft constraints programming. Finally, we present experiments to assess the efficiency of the proposed approach.

Service-oriented Architecture (SoA) is a layered architecture used to organize software resources as services that can be deployed, discovered and combined to produce new services. The interactions between services can be affected in situations where a destination service becomes unavailable. Herein, the Protocol service is introduced as a solution to coordinate interactions between services. The method is then extended to consider the automatic assignment of access control policies by the generation of a new service, called the Access Control Policies (AC_Policies) service, which is linked to the Protocol service. In this context, the Protocol service manages a large amount of data. The analysis of such data sets may help improving the Protocol service performance. Dealing with such a large data sets is referred recently as “Big Data”, is a term related to large set of data that is complicated to be analyzed using traditional applications. One of the most successful implementations of Big Data is the Hadoop Framework. This paper proposes an extension to automate the integration of the Hadoop platform. This aims to breaks individual problems down into multiple subtasks, using a simple programming model (MapReduce). After the analysis is computed, the result is submitted to a Score table linked with the Protocol service. The approach harnesses the capability of Model-Driven Architecture (MDA) to automate the creation, and integration of the architecture. As a proof of concept, the approach is then implemented as a tool.

In recent years, significant progress has been made to improve the power efficiency of mobile devices. In particular, new GPU architectures have made it possible to run compute-intensive applications directly on battery-powered mobile devices. In parallel, research is also being conducted in the area of application offloading, the process of running compute-intensive tasks on cloud servers and delivering the results of these computations to mobile devices through their wireless interfaces. It is important to understand the power consumption implications of each of these two options. In this paper, we use mobile cloud gaming as an example to evaluate and compare these two alternatives (running games on the cloud or on mobile devices.) Based on this comparison, we introduce the concept of Visualization as a Service (VaaS) as a new model to design and implement graphics-intensive applications for mobile devices. In this model, advanced visualization capabilities (e. g., interactive visualization of high resolution videos/images) would be provided to mobile users as a service via the Internet. We show through actual hardware specifications that, despite the recent introduction of ultra low power GPUs for mobile devices, it remains far more power efficient to offload graphics-intensive tasks to the cloud. The associated latency can still be tolerated in most applications.

To guarantee privacy in service oriented environments, it is essential to check for compatibility between a client's privacy requirements and a Web service privacy policies before invoking the Web service operation. In this paper, we focus on privacy at the Web service operation level. We present an approach that integrates k-Anonymity into a privacy management framework using Web Services Conversation Language (WSCL) definitions. In particular, we use the notion of k-Anonymity to determine the extent to which the invocation of an operation can be inferred if one knows that a downstream operation was invoked. We provide both a formal definition as well as an implementation of the proposed approach.

Cloud computing has become a major building block for Web applications where the service providers provide the costumers with Web services performing certain tasks so the system developers can use these services instead of implementing them. Service computing follows the "pay-per-use" as a model for pricing. Due to the variety of service providers, we have many different prices for services and these services have different performance characteristics. The problem here is how to find the best services in terms of cost and performance and is it feasible to switch from one service provider to another. In this paper we propose Wcost, a scheme to resolve this problem based on P-OCEA algorithm which is a combination of P-Optimality and Genetic algorithms which result in more efficient solution than Genetic Algorithm approaches. Our proposed scheme includes a flexible and easily customizable objective function that is suitable for different types of Web services to be composed.

Search engines are the fastest way to find and retrieve information on the web. Their usefulness is evident from the fact that three of the most visited web sites on internet are search engines. With advanced algorithms and the ability to go over millions of records in mere milliseconds, the most time consuming part of searching for the required information is not the search itself but the time it takes to sort through the searched results. Specially when the search query is vague, too broad or simply ill defined. This leads the user to a situation where the search results becomes unmanageably large and confusing. In this paper we present a novel search result clustering technique that relies on the information returned by the search query to extract keywords and to cluster the search results. Unlike most of the search result clustering techniques our proposed solution does not rely on any external knowledge bases, predefined categories or any peripheral knowledge. Thus our technique could be applied on any search domain. We developed a prototype to conduct experiments. Our preliminary results show the applicability and effectiveness of our approach.

Some fairly recent research has focused on providing XACML-based solutions for dynamic privacy policy management. In this regard, a number of works have provided enhancements to the performance of XACML policy enforcement point (PEP) component, but very few have focused on enhancing the accuracy of that component. This paper improves the accuracy of an XACML PEP by filling some gaps in the existing works. In particular, dynamically incorporating user access context into the privacy policy decision, and its enforcement. We provide an XACML-based implementation of a dynamic privacy policy management framework and an evaluation of the applicability of our system in comparison to some of the existing approaches.

Service Oriented Architecture (SOA) enables the creation of distributed applications from independently developed and deployed services. As with any component-based system, the overall performance and quality of the system is an aggregate function of its component services. In this article, we present a novel approach for managing bottom-up faults in service-based systems. Bottom-up faults are a special case of system-wide exceptions that are defined as abnormal conditions or defects occurring in component services, which if not detected and/or managed, may lead to runtime failures. Examples of bottom-up faults include network outage, server disruption, and changes to service provisioning (e.g., new operation parameter required) that may have an impact on the way component services are consumed. We propose a soft-state signaling-based approach to propagate these faults from participants to composite services. Soft-state refers to a class of protocols where the state of a service is constantly refreshed by periodic messages, and user/service takes up the responsibility of communicating and maintaining its state. Soft-state-based protocols have a number of advantages including implicit error recovery and easier fault management, resulting in high availability for systems. Although soft-state has been widely used in various Internet protocols, this work is the first (to the best of our knowledge) to adopt soft-state for fault management in composite services. The proposed approach includes protocols for fault propagation (pure soft-state and soft-state with explicit removal) and fault reaction (rule-based). We also present experiment results to assess the performance and applicability of our approach.

In volunteer cloud federations (VCFs), volunteers join and leave without restrictions and may collectively contribute a large number of heterogeneous virtual machine instances. A challenge is to efficiently allocate this dynamic, heterogeneous capacity to a flow of incoming virtual machine (VM) instantiation requests, i.e., maximize the number of virtual machines that may be placed on the VCF. Cloud federations may allocate VMs far more efficiently if they can accurately predict the demand in terms of VM instantiation requests. In this paper, we present a stochastic technique that forecasts future demand to efficiently allocate VMs to VM instantiation requests. Our approach uses a Markov Chain Monte Carlo (MCMC) simulation known as the Poisson-Gamma Gibbs (PGG) sampler. The PGG sampler is used to determine the arrival rate of each type of VM instantiation requests. This arrival rate is then used to determine an optimal VM placement for the incoming VM instantiation requests. We compared our approach to a solution that adopts a static smallest-fit approach. The experimental results showed that our solution reacts quickly to abrupt changes in the frequency of VM instantiation requests and performs 10% better than the static smallest-fit approach in terms of the total number of satisfied requests.

This paper investigates the problem of rating propagation in composite systems. We propose a method for reputation distribution among component services in Web service composition environments. The main idea lies in providing component services with the appropriate amount of share received for the overall rating. The amount should be proportional to the contribution and performance of the component service. The method ensures that any component service is neither over rated at the expense of a higher performing component nor penalized due to a low performing component. We make use of the textual information present in the service reviews to extract different aspects and their individual sentiments to provide a better rating distribution mechanism. The proposed method attempts to accurately distribute the rating so that it closely reflects the performance of each component in the system. The experimental results show the applicability of our approach and the improved ranking distribution.

The exponential growth of information has prompted the introduction of new technologies such as Semantic Web and Common Sense knowledge bases. To connect the different knowledge presentations together is a primary requirement, and ontologies are central we need for this transformation. In this paper we introduce ConceptOnto which is an ontology based on the ConceptNet knowledge base with extension of some of the other properties in some of the more acclaimed upper ontologies. Our goal in the creation of ConceptOnto is readability for humans, and maximizing the functionality while saving the generality of the ontology.

Extracting Ontop mapping rules and OWL ontology manually from a relational schema is a tedious task. We present an automatic approach for extracting Ontop mappings and OWL ontology from an existing database schema. The end users can access the underlying data source through SPARQL queries. A SPARQL

A modern organization's day to day operations rely heavily on the proper functioning of its IT systems. Increasingly, most of these systems are starting to be based on the Service Oriented Architectures (SOAs). SOAs enable the automatic creation of business applications from independently developed and deployable Web services. When a SOA component suffers failure it results in sub-par operational performance and ultimately hampers the revenue stream of the organization. There are two approaches to minimize the failure risk in SOA: (a) Fault Intolerance tries to eliminate the faults before it occurs in a system. (b) Fault-Tolerance techniques deal with faults that occur on run time and try averting that faults from turning into system failures. Most of the current fault management techniques focus on the latter. However, in our study we develop a hybrid model. The method proposed in this paper (FT-SOA) predicts the failure based on HSMM and clustering techniques for SOAs and Web services. We focus on failure prediction because it plays a major role in having a highly available system. The objective of the online fault prediction is to predict the occurrence of faults in the system, based on the current information such as: service weight, previous history and reputation. A new prediction method is created to assess the fault likelihood in the near future execution of a specific service in a composite solution. We compare our approach with similar techniques to show the applicability of our approach.

Due to the many advantages offered by the cloud computing paradigm, it is fast becoming an enabling technology for many organizations, and even individual users. Flexibility and availability are two of the most important features that promote the wide spread adoption of this technology. In cloud-based data storage scenarios, where the data is controlled by a third party (i.e. the cloud service provider), the data owner usually does not have full control of its data at all stages. Consequently, this poses a prime security threat, and a major challenge is the development of a secure protocol for data storage, sharing, and retrieval. In recent years, a number of research works have targeted this problem. In this paper, we discuss some of the major approaches for secure data sharing in the cloud computing environment. The goal is to provide a concise survey of existing solutions, discuss their benefits, and point out any shortcomings for future research. Specifically, we focus on the use of encryption schemes, and provide a comparative study of the major schemes, through implementation of some representative frameworks.

Cloud computing enables the automated creation of business applications from independently developed and deployed services. In this paper, we propose a technique for Cloud-based services to dynamically evaluate the performance of services based on their previous history, and user requirements. The likelihood of fault occurrence is then used to create recovery plans. The `best' recovery plan is then chosen to be either executed immediately, or saved for a later execution. We assess the overall system dependability using the generated plan and the available invocation information of the included services. Experiment results indicate the applicability of our technique, and show the improved performance in comparison with similar existing approaches.

In this paper, we propose a technique for service-oriented architectures (SOA) to dynamically evaluate the performance of Web services based on their previous history, and user requirements. The likelihood of fault occurrence is then used to create recovery plans. The `best' recovery plan is then chosen to be either executed immediately, or saved for a later execution. We assess the overall system dependability using the generated plan and the available invocation information of the included services. Experiment results indicate the applicability of our technique, and show the improved performance in comparison with similar existing approaches.

Web services are a popular choice for component oriented systems. Web services not only provide a platform independent architecture but also support dynamic compositions to facilitate on the fly creation of composite solutions. Automated negotiation among Web services provides an effective way for the services to bargain for their optimal customizations and allows the discovery of overlooked potential solutions. Dynamic and unique Quality of Service (QoS) requirements of diverse service consumers, pose challenges for effective approaches of service compositions with multiple QoS parameters. In this paper, we present a negotiation Web service that would be used by both the consumer and provider Web services for conducting negotiations for dependent QoS parameters. We use a genetic algorithm(GA) based approach for finding multiple Pareto optimal solutions in multi-party and multi-objective negotiation scenarios, that may satisfy user's requirements. Experimental results indicate the applicability of or our approach in facilitating the negotiations involved in a Web service composition process.

Automated negotiation among Web services not only provides an effective way for the services to bargain for their optimal customizations, but also allows the discovery of overlooked potential solutions. A number of negotiation supporting techniques have been used to find solutions that are acceptable to all parties in the negotiation. However, employing these solutions for automated negotiations among Web services has its own challenges. In this paper, we present the design of a Negotiation Web service that would be used by both the consumers and providers of Web services for conducting negotiations. This negotiation service uses a genetic algorithm(GA) based approach for finding acceptable solutions in multi-party and multi-objective negotiations. In addition to the traditional genetic operators of crossover and mutation, the search is enhanced using anew operator called the Norm. Norm operator represents the cumulative knowledge of all the parties involved in the negotiation process. GA performance with the new Norm operator is compared to the traditional GA, hill-climber and random search techniques. Experimental results indicate the practicality of our approach in facilitating the negotiations involved in a Web service composition process. Specifically, the proposed GA with Norm operator performs better than other approaches.

In this paper, we present Meerkat, a dynamic framework for preserving privacy in Web services. We define a Web service-aware privacy model that deals with the privacy of input data, output data, and operation usage. We introduce a matching protocol that caters for partial and total privacy compatibility. Finally, we propose a negotiation model to reconcile clients' requirements with providers' policies in case of incompatibility.

This paper presents a service-oriented digital government infrastructure focused on efficiently providing customized services to senior citizens. We designed and developed a Web Service Management System (WSMS), called WebSenior, which provides a service-centric framework to deliver government services to senior citizens. The proposed WSMS manages the entire life cycle of third-party web services. These act as proxies for real government services. Due to the specific requirements of our digital government application, we focus on the following key components of WebSenior: service composition, service optimization, and service privacy preservation. These components form the nucleus that achieves seamless cooperation among government agencies to provide prompt and customized services to senior citizens.

This paper investigates the problem of reputation management in composite services. Our focus is on developing a method of distribution of reputation received by a composite service to its component services. The proposed method enables the composite service to provide a fair distribution of reputation values so that a component service is neither penalized nor awarded for the bad and good performances respectively, of other component services. Experiment results show that the proposed technique propagates the "fair share" of reputation from the composite service to its component services.

We propose an approach to leverage the development life cycle of a semantic portal through the use of Object Management Group's model driven architecture (MDA). In our proposed approach, developers can use their prior knowledge about system modeling and UML to create semantic applications, without worrying much about various ontologic intricacies. We believe that our proposed combination of the semantic technologies and MDA-centric standardizations makes an ideal contrivance for the integration and development of standardized knowledge-based applications.

We present DIA, a Web services-based infrastructure for the Discovery, Integration, and Analysis of geoscience data, tools, and services. DIA provides a collaborative environment where scientists can share their resources (e.g., geochemical data, filtering services, etc.) by registering them through well-defined ontologies. We have developed a planetary materials ontology in OWL for this purpose. The ontology is used by different geoscientists (using Web services) to explore, extract, and integrate information from different heterogeneous data sets. The DIA system is now in its final pre-release phase. It is currently made accessible to a few geoscientists for conducting usability analyses, and it will eventually be made available to the community at large through the geoscience portal (GEON) at the San Diego Supercomputer Center.

The emergence of Web databases has introduced new challenges related to their organization, access, integration, and interoperability. New approaches and techniques are needed to provide across-the-board transparency for accessing and manipulating Web databases irrespective of their data models, platforms, locations, or systems. In meeting these needs, it is necessary to build a middleware infrastructure to support flexible tools for information space organization, communication facilities, information discovery, content description, and assembly of data from heterogeneous sources. In this paper, we describe a scalable middleware for efficient data and application access that we have built using the available technologies. The resulting system is called WebFINDIT. It is a scalable and uniform infrastructure for locating and accessing heterogeneous and autonomous databases and applications.

In this paper, we propose an approach for preserving trade secrets in B2B interactions among competitors. Customer information exchanged during Business-to-Business (B2B) collaborations is usually considered as a business asset not to be freely shared with other businesses. This customer information is in essence a business trade secret. The full automation of B2B interactions is now possible because of the wide deployment of such technologies as Web services. However, these advances also create greater opportunities for businesses in acquiring sensitive customer data from other transacting businesses, thus creating an impediment for potential B2B collaboration between competitors. This calls for techniques to protect disclosure of sensitive data. Our approach leverages psycholinguistic knowledge to perturb data to computationally impede the disclosure of privileged customer information. We present an analytical model and a set of experiments to demonstrate the robustness of the proposed techniques.