Cloud Fabric Research Articles

Mobiles for Sensing Clouds: the SAaaS4Mobile Experience

Smart devices, and mobiles in particular, are at the forefront of several hot new trends in ICT, such as the Internet of Things and service computing. Cloud computing is another paradigm generating a great deal of offshoots, some of which are aimed at enabling novel services and applications by exploiting its ubiquity and flexibility in combination with sensors and the (meta)data they produce about phenomena, events and other interesting items about the physical world. In this context, the authors, propose a new way to orchestrate devices, in particular SNs and mobiles, as resources to build up Clouds of sensors, reverting the current wisdom about mobile Clouds, i.e. the integration of feature-rich devices into the Cloud fabric as mere clients to one where personal / wearable devices are actively involved into a Cloud, forming a fully feedback-enabled ecosystem. The main aim of the Sensing and Actuation as a Service (SAaaS) approach is therefore to implement such a Cloud by enrolling and aggregating sensing resources from sensor networks and personal, mobile devices. A device-centric approach is embraced as in IaaS Clouds: once collected, the physical (sensing) resources are abstracted and virtualised and then provided elastically, on-demand, as a service to end users, including facilities for customization of the (hosting) embedded platform. A key point of the SAaaS approach is the abstraction of resources, i.e. providing a uniform way to access to and interact with the underlying physical nodes. In this paper we focus on the low-level interaction with sensing resources in SAaaS, restricting the scope to mobiles, thus providing details on theoretical and design aspects as well as technical and implementation ones. In particular, we report on an implementation of the SAaaS low-level modules on Android devices, the SAaaS4Mobile one, providing architectural descriptions of the main modules, implementation guidelines and discussing through a preliminary implementation evaluation the effectiveness of the approach.

A framework for partitioning and execution of data stream applications in mobile cloud computing

The contribution of cloud computing and mobile computing technologies lead to the newly emerging mobile cloud computing paradigm. Three major approaches have been proposed for mobile cloud applications: 1) extending the access to cloud services to mobile devices; 2) enabling mobile devices to work collaboratively as cloud resource providers; 3) augmenting the execution of mobile applications on portable devices using cloud resources. In this paper, we focus on the third approach in supporting mobile data stream applications. More specifically, we study how to optimize the computation partitioning of a data stream application between mobile and cloud to achieve maximum speed/throughput in processing the streaming data. To the best of our knowledge, it is the first work to study the partitioning problem for mobile data stream applications, where the optimization is placed on achieving high throughput of processing the streaming data rather than minimizing the makespan of executions as in other applications. We first propose a framework to provide runtime support for the dynamic computation partitioning and execution of the application. Different from existing works, the framework not only allows the dynamic partitioning for a single user but also supports the sharing of computation instances among multiple users in the cloud to achieve efficient utilization of the underlying cloud resources. Meanwhile, the framework has better scalability because it is designed on the elastic cloud fabrics. Based on the framework, we design a genetic algorithm for optimal computation partition. Both numerical evaluation and real world experiment have been performed, and the results show that the partitioned application can achieve at least two times better performance in terms of throughput than the application without partitioning.

The AppScale Cloud Platform: Enabling Portable, Scalable Web Application Deployment

AppScale is an open source distributed software system that implements a cloud platform as a service (PaaS). AppScale makes cloud applications easy to deploy and scale over disparate cloud fabrics, implementing a set of APIs and architecture that also makes apps portable across the services they employ. AppScale is API-compatible with Google App Engine (GAE) and thus executes GAE applications on-premise or over other cloud infrastructures, without modification.

A cloud architecture for dynamic virtual organisations in transport logistics

This paper proposes a cloud-based data sharing architecture for dynamic virtual organisations in the domain of transport logistics. Such VOs are described as dynamic because its members can join and leave on demand, reflecting the behaviour of physical transport chains. Members of the VO implement a community type cloud by sharing amongst them data resources over a cloud fabric. Data about the status and activities of the transportation chain are replicated intelligently across the participating nodes. The data cloud architecture proposed in this paper ensures that transport chain data are always available on demand to those that require them, while minimising unnecessary exposure of data and thus security risks. The approach is illustrated with a case study of a multimodal transport logistics chain VO.

Language and Runtime Support for Automatic Configuration and Deployment of Scientific Computing Software over Cloud Fabrics

In this paper, we present the design and implementation of Neptune, a simple, domain-specific language based on the Ruby programming language. Neptune automates the configuration and deployment of scientific software frameworks over disparate cloud computing systems. Neptune integrates support for MPI, MapReduce, UPC, X10, StochKit, and others. We implement Neptune as a software overlay for the AppScale cloud platform and extend AppScale with support for elasticity and hybrid execution for scientific computing applications. Neptune imposes no overhead on application execution, yet significantly simplifies the application deployment process, enables portability across cloud systems, and promotes lock-in avoidance by specific cloud vendors.