A component-based middleware framework for configurable and reconfigurable Grid computing

Significant progress has been made in the design and development of Grid middleware which, in its present form, is founded on Web services technologies. However, we argue that present-day Grid middleware is severely limited in supporting projected next-generation applications which will involve pervasive and heterogeneous networked infrastructures, and advanced services such as collaborative distributed visualization. In this paper we discuss a new Grid middleware framework that features (i) support for advanced network services based on the novel concept of pluggable overlay networks, (ii) an architectural framework for constructing bespoke Grid middleware platforms in terms of 'middleware domains' such as extensible interaction types and resource discovery. We believe that such features will become increasingly essential with the emergence of next-generation e-Science applications. Copyright (c) 2005 John Wiley & Sons, Ltd

CloudHealth: A Model-Driven Approach to Watch the Health of Cloud Services

Cloud systems are complex and large systems where services provided by different operators must coexist and eventually cooperate. In such a complex environment, controlling the health of both the whole environment and the individual services is extremely important to timely and effectively react to misbehaviours, unexpected events, and failures. Although there are solutions to monitor cloud systems at different granularity levels, how to relate the many KPIs that can be collected about the health of the system and how health information can be properly reported to operators are open questions. This paper reports the early results we achieved in the challenge of monitoring the health of cloud systems. In particular we present CloudHealth, a model-based health monitoring approach that can be used by operators to watch specific quality attributes. The CloudHealth Monitoring Model describes how to operationalize high level monitoring goals by dividing them into subgoals, deriving metrics for the subgoals, and using probes to collect the metrics. We use the CloudHealth Monitoring Model to control the probes that must be deployed on the target system, the KPIs that are dynamically collected, and the visualization of the data in dashboards.Comment: 8 pages, 2 figures, 1 tabl

CloudOps: Towards the Operationalization of the Cloud Continuum: Concepts, Challenges and a Reference Framework

The current trend of developing highly distributed, context aware, heterogeneous computing intense and data-sensitive applications is changing the boundaries of cloud computing. Encouraged by the growing IoT paradigm and with flexible edge devices available, an ecosystem of a combination of resources, ranging from high density compute and storage to very lightweight embedded computers running on batteries or solar power, is available for DevOps teams from what is known as the Cloud Continuum. In this dynamic context, manageability is key, as well as controlled operations and resources monitoring for handling anomalies. Unfortunately, the operation and management of such heterogeneous computing environments (including edge, cloud and network services) is complex and operators face challenges such as the continuous optimization and autonomous (re-)deployment of context-aware stateless and stateful applications where, however, they must ensure service continuity while anticipating potential failures in the underlying infrastructure. In this paper, we propose a novel CloudOps workflow (extending the traditional DevOps pipeline), proposing techniques and methods for applications’ operators to fully embrace the possibilities of the Cloud Continuum. Our approach will support DevOps teams in the operationalization of the Cloud Continuum. Secondly, we provide an extensive explanation of the scope, possibilities and future of the CloudOps.This research was funded by the European project PIACERE (Horizon 2020 Research and Innovation Programme, under grant agreement No. 101000162)

CloudOps: Towards the Operationalization of the Cloud Continuum: Concepts, Challenges and a Reference Framework

The current trend of developing highly distributed, context aware, heterogeneous computing intense and data-sensitive applications is changing the boundaries of cloud computing. Encouraged by the growing IoT paradigm and with flexible edge devices available, an ecosystem of a combination of resources, ranging from high density compute and storage to very lightweight embedded computers running on batteries or solar power, is available for DevOps teams from what is known as the Cloud Continuum. In this dynamic context, manageability is key, as well as controlled operations and resources monitoring for handling anomalies. Unfortunately, the operation and management of such heterogeneous computing environments (including edge, cloud and network services) is complex and operators face challenges such as the continuous optimization and autonomous (re-)deployment of context-aware stateless and stateful applications where, however, they must ensure service continuity while anticipating potential failures in the underlying infrastructure. In this paper, we propose a novel CloudOps workflow (extending the traditional DevOps pipeline), proposing techniques and methods for applications’ operators to fully embrace the possibilities of the Cloud Continuum. Our approach will support DevOps teams in the operationalization of the Cloud Continuum. Secondly, we provide an extensive explanation of the scope, possibilities and future of the CloudOps.This research was funded by the European project PIACERE (Horizon 2020 Research and Innovation Programme, under grant agreement No. 101000162)

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

QoS Composition and Analysis in Reconfigurable Web Services Choreographies

International audienceQuality of Service (QoS) in orchestrated web services compositions have been well studied with probabilistic and multi-dimensional models. Choreographies that involve message passing among services, on the other hand, require further analysis. In this paper, we begin with the set of QoS domains that may be studied in case of choreographies and the algebraic rules for their composition. As choreographies manage QoS composition in a distributed fashion, techniques to enrich functional specifications with QoS are examined using the model proposed in the CHOReOS project. These are further analyzed with choreographies that may reconfigure due to functional or QoS requirements. Studies on the effects of such reconfiguration on multiple QoS domains can lead to better understanding of optimal runtime configurations along with associated tradeoffs. A goal programming approach is also proposed to choose Pareto optimal solutions with respect to diverse QoS domains

Model-based provisioning and management of adaptive distributed communication in mobile cooperative systems

Adaptation of communication is required to maintain the reliable connection and to ensure the minimum quality in collaborative activities. Within the framework of wireless environment, how can host entities be handled in the event of a sudden unexpected change in communication and reliable sources? This challenging issue is addressed in the context of Emergency rescue system carried out by mobile devices and robots during calamities or disaster. For this kind of scenario, this book proposes an adaptive middleware to support reconfigurable, reliable group communications. Here, the system structure has been viewed at two different states, a control center with high processing power and uninterrupted energy level is responsible for global task and entities like autonomous robots and firemen owning smart devices act locally in the mission. Adaptation at control center is handled by semantic modeling whereas at local entities, it is managed by a software module called communication agent (CA). Modeling follows the well-known SWRL instructions which establish the degree of importance of each communication link or component. Providing generic and scalable solutions for automated self-configuration is driven by rule-based reconfiguration policies. To perform dynamically in changing environment, a trigger mechanism should force this model to take an adaptive action in order to accomplish a certain task, for example, the group chosen in the beginning of a mission need not be the same one during the whole mission. Local entity adaptive mechanisms are handled by CA that manages internal service APIs to configure, set up, and monitors communication services and manages the internal resources to satisfy telecom service requirements

A Customizable Architecture for Application-Centric Management of Context-Aware Applications

[EN] Context-aware applications present common requirements (e.g., heterogeneity, scalability, adaptability, availability) in a variety of domains (e.g., healthcare, natural disaster prevention, smart factories). Besides, they do also present domain specific requirements, among which the application concept itself is included. Therefore, a platform in charge of managing their execution must be generic enough to cover common requirements, but it must also be adaptable enough to consider the domain aspects to meet the demands at application-level. Several approaches in the literature tackle some of these demands, but not all of them, and without considering the applications concept and the customization demands in different domains. This work proposes a generic and customizable management architecture that covers both types of requirements based on multi-agent technology and model-driven development. Multi-agent technology is used to enable the distributed intelligence needed to address many common requirements, whereas model-driven development allows to address domain specific particularities. On top of that, a customization methodology to develop specific platforms from this generic architecture is also presented. This methodology is assessed by means of a case study in the domain of eHealthCare. Finally, the performance of MAS-RECON is compared with the most popular tool for the orchestration of containerized applications.This work was supported in part by the Ministerio de Ciencia, Innovacion y Universidades (MCIU)/Agencia Estatal de Investigacion (AEI)/Fondo Europeo de Desarrollo Regional (FEDER), Union Europea (UE), under Grant RTI2018-096116-B-I00; and in part by the Gobierno Vasco (GV)/Eusko Jaurlaritza (EJ) under Grant IT1324-19