Gregory Van Seghbroeck

BonFIRE: A Multi-cloud Test Facility for Internet of Services Experimentation

BonFIRE offers a Future Internet, multi-site, cloud testbed, targeted at the Internet of Services community, that supports large scale testing of applications, services and systems over multiple, geographically distributed, heterogeneous cloud testbeds. The aim of BonFIRE is to provide an infrastructure that gives experimenters the ability to control and monitor the execution of their experiments to a degree that is not found in traditional cloud facilities.

BonFIRE: The Clouds and Services Testbed

BonFIRE is a multi-site test bed that supports testing of Cloud-based and distributed applications. BonFIRE breaks the mould of commercial Cloud offerings by providing unique functionality in terms of observability, control, advanced Cloud features and ease of use for experimentation. A number of successful use cases have been executed on BonFIRE, involving industrial and academic users and delivering impact in diverse areas, such as media, e-health, environment and manufacturing. The BonFIRE user-base is expanding through its free, Open Access scheme, daily carrying out important research, while the consortium is working to sustain the facility beyond 2014.

Tengu: An Experimentation Platform for Big Data Applications

Big data applications have stringent service requirements for scalability and fault-tolerance and involve high volumes of data, high processing speeds and large varieties of database technologies. In order to test big data management solutions, large experimentation facilities are needed, which are expensive in terms of both resource cost and configuration time. This paper presents Tengu, an experimentation platform for big data applications that can automatically be instantiated on GENI (US federation of test beds) and Fed FIRE (EU federation of test beds)compatible test beds. Tengu allows for automatic deployments of several data processing, storage and cloud technologies, including Hadoop, Storm and Open Stack. The paper discusses the Tengu architecture, the Tengu-as-a-service approach and a demonstration of an automated instantiation of the Tengu experimentation suite on the Virtual Wall, a large-scale Emulab testbed at the Minds research institute in Europe.

WS-Gesture, a gesture-based state-aware control framework

Industry has capitalized on peoples urge for gadgets and innovative appliances. The most popular devices are still entertainment systems, but home automation, e.g. automated heating and lighting control, is gaining more and more interest. To manage the vast amount of remote controls that come with these appliances, universal remote controls were created. These are unfortunately hard to configure and hard to use, due to the numerous buttons. This paper presents the architecture of the WS-Gesture framework. An alternative of interacting with this pervasive environment of devices, inspired by the success of Nintendo Wiis Remote, where the user interacts by means of gestures. To cope with the many different features of the devices, state-awareness is introduced in the system. Depending on which user is controlling a selected device and on the current state of that device, a particular gesture can result in different actions. Communication is based on Device Profile for Web Services, an emerging standard delivering discovery and dynamic selection capabilities. During implementation and evaluation special attention has been payed to accuracy and the responsiveness, very important for the user experience.

Model-driven deployment and management of workflows on analytics frameworks

The data science skills shortage means that those who have the knowledge are under constant pressure to do more with less. While the data science tools are improving at a staggering pace, the operational tools around them can not keep up. Even researchers at Google state that the issue of automatic configuration and dependency management of services is still an “open, hard problem”. This manifests itself in data scientists either constantly having to solve operational challenges or having to be in constant close collaboration with a skilled operations team. This paper addresses the operational challenges behind deploying and managing workflows on top of analytics platforms by starting from three key requirements: data scientists want to model their workflows in a reusable way, this model should be automatically deployed, managed and connected to other services, and this solution should be compatible with existing cloud modeling languages, infrastructure, analytics platforms and tools. The paper explores where the state-of-the-art falls short in meeting these requirements, proposes an architecture to solve the open challenges, and implements and evaluates this architecture.

BPMN Extensions for Decentralized Execution and Monitoring of Business Processes

Software-as-a-service (SaaS) providers are further expanding their offering by growing into the space of business process outsourcing (BPO). Therefore, the SaaS provider wants to administer and manage the business process steps according to a service level agreement. Outsourcing of business processes results in decentralized business workflows. However, current business process modeling languages, e.g. Business Process Execution Language (BPEL), Business Process Model and Notation (BPMN), are based highly on a centralized execution model and current BPMN engines offer limited constructs for federation and decentralized execution. To guarantee execution of business processes according to a service level agreement, different parties involved in a federated workflow must be able to inspect the state of external workflows. This requires advanced inspection interfaces and monitoring facilities. Current business process modeling languages must thus be extended to support monitoring in the s pecification, support modeling and support deployment of decentralized workflows. In this paper, correlation and monitoring extensions for BPMN are described. These extensions to BPMN are described such that the existing specification can still be used as is in a backwards compatible way.

WEB SERVICE CHOREOGRAPHY CONFORMANCE VERIFICATION THROUGH THE piX-MODEL

As the adoption of the Service Oriented Architecture paradigm has dramatically increased over the past few years, proper coordination of loosely coupled services becomes an important issue when building state-of-the-art applications. This coordination is typically organized through orchestration (requiring a central coordinating entity) or through choreographies. While the latter approach allows for a fully distributed coordination, the need also arises for a distributed conformance check, ensuring that each participant of the choreography behaves according to the general choreography. In this paper, a formalism is presented to ensure this conformance at design time, with possible extensions to deploy time and to runtime conformance checking. This formalism is referred to as the piX-model and it will be shown that the approach taken is inherently less complex, both in time and space, than the conventional π-calculus-based approach, whilst offering the same conformance guarantees. This gain in performance allows for a small design turnaround time, and also opens the avenue to runtime conformance checking by resource constrained devices.

Distributed Service Orchestration: Eventually Consistent Cloud Operation and Integration

Both researchers and industry players are facing the same obstacles when entering the big data field. Deploying and testing distributed data technologies requires a big up-front investment of both time and knowledge. Existing cloud automation solutions are not well suited for managing complex distributed data solutions. This paper proposes a distributed service orchestration architecture to better handle the complex orchestration logic needed in these cases. A novel service-engine based approach is proposed to cope with the versatility of the individual components. A hybrid integration approach bridges the gap between cloud modeling languages, automation artifacts, image-based schedulers and PaaS solutions. This approach is integrated in the distributed data experimentation platform Tengu, making it more flexible and robust.

Kameleo: Design of a new Platform-as-a-Service for flexible data management

Data is abundantly present in todays world and the amount of data we generate continues to grow. The representation and structure of this data, however, differs greatly depending on the software or platform. The wide variety of software available shows there is no one optimal way to model data for all software, but when you want to deploy software in the cloud using a Platform-as-a-Service (PaaS) provider, you are restricted to the data model chosen by the provider. We propose Kameleo, a new Platform-as-a-Service able to support several data models. This provides software developed by the customers with an optimal data model even when the requirements of the software change. In order to evaluate the platform, we compared three multitenancy models using a basic webshop application. Results show a clear difference in performance between the models.

Automated Instantiation and Extraction of Web Service Choreographies

Service choreographies describe the interactions that take place in a distributed service collaboration without central entity orchestrating these interactions. It is obvious that each partner will execute parts of the choreography to fulfill the global collaborative effort. This paper focuses on translating the global choreography to local projections at design time. These projections need to be implemented by each participating partner. The process is decomposed in two steps: instantiation and extraction. In the instantiation step the abstraction levels are automatically determined, ranging from the choreography level to its smallest building blocks, the channel instances. In the extraction step, we present a way to map these channel instances to WS-BPEL. It is shown that this results in small WS-BPEL processes with a very straightforward correlation set, allowing for even resource-limited devices to participate in the choreography.