Trust enforcement through self-adapting cloud workflow orchestration

Providing runtime intelligence of a workflow in a highly dynamic cloud execution environment is a challenging task due the continuously changing cloud resources. Guaranteeing a certain level of workflow Quality of Service (QoS) during the execution will require continuous monitoring to detect any performance violation due to resource shortage or even cloud service interruption. Most of orchestration schemes are either configuration, or deployment dependent and they do not cope with dynamically changing environment resources. In this paper, we propose a workflow orchestration, monitoring, and adaptation model that relies on trust evaluation to detect QoS performance degradation and perform an automatic reconfiguration to guarantee QoS of the workflow. The monitoring and adaptation schemes are able to detect and repair different types of real time errors and trigger different adaptation actions including workflow reconfiguration, migration, and resource scaling. We formalize the cloud resource orchestration using state machine that efficiently captures different dynamic properties of the cloud execution environment. In addition, we use validation model checker to validate our model in terms of reachability, liveness, and safety properties. Extensive experimentation is performed using a health monitoring workflow we have developed to handle dataset from Intelligent Monitoring in Intensive Care III (MIMICIII) and deployed over Docker swarm cluster. A set of scenarios were carefully chosen to evaluate workflow monitoring and the different adaptation schemes we have implemented. The results prove that our automated workflow orchestration model is self-adapting, self-configuring, react efficiently to changes and adapt accordingly while supporting high level of Workflow QoS

Guest editorial

The advance in Information Technology knowledge and expertise has been tremendous during the last couple of decades. The shift from batch systems, to online systems, to standalone systems, to networked systems, to Service-Oriented systems, and then later to the cloud of everything had changed the everyday life of humanity. Humans do not expect the same services and quality from nowadays IT systems as they used to in the past. Industry and academia have been doing their best to meet these expectations. However, there are a lot of issues to be improved, others to be done from scratch, and other to be removed and or replaced

Mobility-aware selection of mobile web services

Quality of Web Service (QoWS) support for Mobility-aware Web services (MWS) is critical for mobile users since it relies on the available resources on mobile devices consuming these services. In this paper, we propose a selection model for MWS based on QoWS and device resources requirements. The main purpose of the model is to support the client in selecting MWS based on desired QoWS as well as on its device resources availability. We propose a verification scheme to verify the conformity of claimed MWS QoWS and required device resources compared to the published one. The verification is used to support selection of MWS. The implementation of our model is discussed and the importance of our verification scheme is highlighted

Multi-tier framework for management of web services\u27 quality

Web Services are new breed of applications that endorsed large support from main vendors from industry as well as academia. As the Web Services paradigm becomes more mature, its management is crucial to its adoption and success. Existing approaches are often limited to the platforms under which management features are provided. In this chapter, we propose an approach to provide a unique central console for management of both functional and nonfunctional aspects of Web Services. In fact, we aim at the development of a framework to provide management features to providers and clients by supporting management activities all along the lifecycle. The framework allows/forces providers to consider management activities while developing their Web Services. It allows clients to select appropriate Web Services using different criteria (name, quality). Clients make also use of the framework to check if the Web Services, they are actually using or planning to use, are behaving correctly. We evaluate the Web Services management features of our framework using a composite Web Service

A scalable QoS-aware Web Services Management Architecture (QoSMA)

As Web services are growing rapidly and as their adoption by a large number of business organizations is increasing, scalability and performance management of Web services environments are of paramount importance. This chapter proposes a scalable QoS-aware architecture, called QoSMA, for the management of QoS-aware Web services. The aim of this architecture is to provide QoS management support for both Web services’ providers and consumers. The proposed architecture is based on the commonly used notion of QoS brokerage service. The QoS broker mediates between service requestors and service providers. Its responsibilities include performance monitoring of Web services, supporting users in Web services selection based on their QoS requirements, and the negotiation of QoS issues between requestors and providers. The QoSMA architecture provides the following benefits: first, it allows the automation of QoS management and QoS monitoring for both providers and clients. Second, the scalability of the architecture allows for better handling of the increasing demand while maintaining the pre-agreed on QoS between service requestors and providers

Service Oriented Centered E-Health Solution for Monitoring and Preventing Chronic Diseases

The modern and continuously changing lifestyles in almost allparts of the world resulted in an increase in the incidence ofchronic diseases (CDs). To reduce risks associated with chronicdiseases, health professionals are studying various clinicalsolutions. As a result of recent advances in sensing technology,wireless communications, and distributed communication, themonitoring of patients' health condition and the elaboration ofprevention plans are considered the most promising solutions forthe treatment of chronic diseases. In this paper, we propose anovel framework for monitoring chronic diseases and trackingtheir vital signs. The framework relies on the service orientationconcepts and standards to integrate various subsystems.Monitoring of subjects’ health condition, using various sensorsand wireless devices, aims to proactively detect any risk ofchronic diseases. The system will allow generating andcustomizing preventive plans dynamically according to thesubject’s health profile and context while considering manyimpelling parameters. As a proof of concept of our monitoringand tracking schemes, we have considered a case study for whichwe have collected and analyzed preliminary data

A secure role-based service discovery technique for emergency intervention operations

[abstract not available

Towards a best-effort framework for developing smart mobile applications

Despite the rapid growth of the mobile technology, mobile devices are still considered as resource constrained with limited battery. Same computations are awkward to be undertaken on these devices with limited processing capabilities. Other processes are costly in terms of battery consumption. Ideally, mobile applications will have the possibility to decide either to do a computation locally or remotely depending on the current device capabilities status. Making such decision is very challenging as many interrelated factors are to be considered (e.g. network connection, battery level, and processing capabilities). In this paper, we propose a framework that supports developers in implementing such smartness fitness within their mobile applications. This solution provides approaches in form of algorithms to instrument code of mobile applications to behave in smart way. Incorporating these algorithms will allow for on-the-fly decision of local versus remote computation using a calculated cost function. We conducted some experimental scenarios to evaluate the usability and effectiveness of our decision-based algorithms. The results we have obtained prove that for the same computation, depending on the size of data, the network status and the device status, the decision of the engine may differ