Building the Future Internet through FIRE

The Internet as we know it today is the result of a continuous activity for improving network communications, end user services, computational processes and also information technology infrastructures. The Internet has become a critical infrastructure for the human-being by offering complex networking services and end-user applications that all together have transformed all aspects, mainly economical, of our lives. Recently, with the advent of new paradigms and the progress in wireless technology, sensor networks and information systems and also the inexorable shift towards everything connected paradigm, first as known as the Internet of Things and lately envisioning into the Internet of Everything, a data-driven society has been created. In a data-driven society, productivity, knowledge, and experience are dependent on increasingly open, dynamic, interdependent and complex Internet services. The challenge for the Internet of the Future design is to build robust enabling technologies, implement and deploy adaptive systems, to create business opportunities considering increasing uncertainties and emergent systemic behaviors where humans and machines seamlessly cooperate

Building the Future Internet through FIRE

The Internet as we know it today is the result of a continuous activity for improving network communications, end user services, computational processes and also information technology infrastructures. The Internet has become a critical infrastructure for the human-being by offering complex networking services and end-user applications that all together have transformed all aspects, mainly economical, of our lives. Recently, with the advent of new paradigms and the progress in wireless technology, sensor networks and information systems and also the inexorable shift towards everything connected paradigm, first as known as the Internet of Things and lately envisioning into the Internet of Everything, a data-driven society has been created. In a data-driven society, productivity, knowledge, and experience are dependent on increasingly open, dynamic, interdependent and complex Internet services. The challenge for the Internet of the Future design is to build robust enabling technologies, implement and deploy adaptive systems, to create business opportunities considering increasing uncertainties and emergent systemic behaviors where humans and machines seamlessly cooperate

PluralisMAC: a generic multi-MAC framework for heterogeneous, multiservice wireless networks, applied to smart containers

Developing energy-efficient MAC protocols for lightweight wireless systems has been a challenging task for decades because of the specific requirements of various applications and the varying environments in which wireless systems are deployed. Many MAC protocols for wireless networks have been proposed, often custom-made for a specific application. It is clear that one MAC does not fit all the requirements. So, how should a MAC layer deal with an application that has several modes (each with different requirements) or with the deployment of another application during the lifetime of the system? Especially in a mobile wireless system, like Smart Monitoring of Containers, we cannot know in advance the application state (empty container versus stuffed container). Dynamic switching between different energy-efficient MAC strategies is needed. Our architecture, called PluralisMAC, contains a generic multi-MAC framework and a generic neighbour monitoring and filtering framework. To validate the real-world feasibility of our architecture, we have implemented it in TinyOS and have done experiments on the TMote Sky nodes in the w-iLab.t testbed. Experimental results show that dynamic switching between MAC strategies is possible with minimal receive chain overhead, while meeting the various application requirements (reliability and low-energy consumption)

A Rapid Testing Framework for a Mobile Cloud Infrastructure

Abstract—Mobile clouds such as network-connected vehicles and satellite clusters are an emerging class of systems that are extensions to traditional real-time embedded systems: they provide long-term mission platforms made up of dynamic clusters of heterogeneous hardware nodes communicating over ad hoc wireless networks. Besides the inherent complexities entailed by a distributed architecture, developing software and testing these systems is difficult due to a number of other reasons, including the mobile nature of such systems, which can require a model of the physical dynamics of the system for accurate simulation and testing. This paper describes a rapid development and testing framework for a distributed satellite system. Our solutions include a modeling language for configuring and specifying an application’s interaction with the middleware layer, a physics simulator integrated with hardware in the loop to provide the system’s physical dynamics and the integration of a network traffic tool to dynamically vary the network bandwidth based on the physical dynamics. I

A systematic review on cloud testing

A systematic literature review is presented that surveyed the topic of cloud testing over the period (2012-2017). Cloud testing can refer either to testing cloud-based systems (testing of the cloud), or to leveraging the cloud for testing purposes (testing in the cloud): both approaches (and their combination into testing of the cloud in the cloud) have drawn research interest. An extensive paper search was conducted by both automated query of popular digital libraries and snowballing, which resulted into the final selection of 147 primary studies. Along the survey a framework has been incrementally derived that classifies cloud testing research along six main areas and their topics. The paper includes a detailed analysis of the selected primary studies to identify trends and gaps, as well as an extensive report of the state of art as it emerges by answering the identified Research Questions. We find that cloud testing is an active research field, although not all topics have received so far enough attention, and conclude by presenting the most relevant open research challenges for each area of the classification framework.This paper describes research work mostly undertaken in the context of the European Project H2020 731535: ElasTest. This work has also been partially supported by: the Italian MIUR PRIN 2015 Project: GAUSS; the Regional Government of Madrid (CM) under project Cloud4BigData (S2013/ICE-2894) cofunded by FSE & FEDER; and the Spanish Government under project LERNIM (RTC-2016-4674-7) cofunded by the Ministry of Economy and Competitiveness, FEDER & AEI

Doctor of Philosophy

dissertationNetwork emulation has become an indispensable tool for the conduct of research in networking and distributed systems. It offers more realism than simulation and more control and repeatability than experimentation on a live network. However, emulation testbeds face a number of challenges, most prominently realism and scale. Because emulation allows the creation of arbitrary networks exhibiting a wide range of conditions, there is no guarantee that emulated topologies reflect real networks; the burden of selecting parameters to create a realistic environment is on the experimenter. While there are a number of techniques for measuring the end-to-end properties of real networks, directly importing such properties into an emulation has been a challenge. Similarly, while there exist numerous models for creating realistic network topologies, the lack of addresses on these generated topologies has been a barrier to using them in emulators. Once an experimenter obtains a suitable topology, that topology must be mapped onto the physical resources of the testbed so that it can be instantiated. A number of restrictions make this an interesting problem: testbeds typically have heterogeneous hardware, scarce resources which must be conserved, and bottlenecks that must not be overused. User requests for particular types of nodes or links must also be met. In light of these constraints, the network testbed mapping problem is NP-hard. Though the complexity of the problem increases rapidly with the size of the experimenter's topology and the size of the physical network, the runtime of the mapper must not; long mapping times can hinder the usability of the testbed. This dissertation makes three contributions towards improving realism and scale in emulation testbeds. First, it meets the need for realistic network conditions by creating Flexlab, a hybrid environment that couples an emulation testbed with a live-network testbed, inheriting strengths from each. Second, it attends to the need for realistic topologies by presenting a set of algorithms for automatically annotating generated topologies with realistic IP addresses. Third, it presents a mapper, assign, that is capable of assigning experimenters' requested topologies to testbeds' physical resources in a manner that scales well enough to handle large environments