Giuseppe Caragnano

A Hybrid Cloud Infrastructure for the Optimization of VANET Simulations

Cloud computing is becoming increasingly popular for the provisioning of computing resources, in particular, through scientific tools that perform modeling or simulations. Vehicular Ad-Hoc Networks (VANETs) are mobile ad hoc networks which are meant to support primarily safety warnings and to manage challenging conditions to improve our transportation experience. This is a challenging context where large amounts of data need to be elaborated and analyzed in order to fully understand protocol and phenomena behaviors. In fact, VANET simulations are typically computationally intensive problems, and lend themselves for execution on distributed systems. A new hybrid cloud infrastructure is here presented to help and support the simulation science. This architecture optimizes the scheduling and execution of a batch of simulations, increasing the overall performance, in terms of simulation time and costs. Results clearly highlight the potentiality of this technology, proving as a valuable tool for network simulations.

Accessing Cloud Services through Biometrics Authentication

The adoption of Cloud computing involves many advantages in terms of flexibility, scalability and reliability, but also implies new challenges on security, data privacy and protection of personal data. Since more and more sensitive applications and data are moved to the cloud, the verification of the digital identity of the participants in the electronic communication has become a crucial challenge. Currently, the use of biometric techniques can be considered as an effective solution to ensure a significant increase of security in the authentication protocols managed by modern authentication servers. However the use of biometric data for the logical access to IT services is a more challenging and still unsolved problem. The project Cloud for SME integrates a biometric authentication based on fingerprints with a cloud computing platform, investigating how highly secure authentication methods can increase the adoption of cloud computing technologies among small and medium enterprises.

GNSS Based Services on Cloud Environment

The ionosphere is the single largest contributor to the GNSS (Global Navigation Satellite System) error budget and ionospheric scintillation (IS) in particular is one of its most harmful effects. The Ground Based Scintillation Climatology (GBSC) has been recently developed by INGV as a software tool to identify the main areas of the ionosphere in which IS is more likely to occur. Due to the high computational load required, GBSC is currently used only for scientific, offline, studies and not as a real time service. Recently, a collaboration was initiated between ISMB and INGV in order to identify which cloud service model (IaaS, PaaS or SaaS) is most suitable for implementing the GBSC technique within the cloud computing environment. The aims of this joined effort are twofold: i) to optimize the computational resources allocation strategy/plan for the GBSC service, ii) to fine tune the algorithm for dynamic and real time application, towards a service contributing to high precision professional applications for the GNSS-reliant business sectors. Preliminary result of the implementation of GBSC within the cloud environment will be shown.

Design and Quantitative Assessment of a Novel Hybrid Cloud Architecture for VANET Simulations

Vehicular Ad-hoc NETworks (VANETs) are ad hoc networks aimed at improving the safety and efficiency of transportation in the near future. Despite the availability of results from field trials, simulations still play an unequalled role in the comprehensive understanding of complex and crowded VANET scenarios. Even more, VANET simulations are typically computationally intensive problems and lend themselves for execution on distributed systems. This paper presents a new architecture optimizing the scheduling and execution of a batch of simulations over a hybrid cloud. Results reveal that, in case of multiple simulations to be executed, the overall performance can deeply benefit from a distributed approach, reducing time and costs.

A cloud-based approach for Gene Regulatory Networks dynamics simulations

Gene Regulatory Networks (GRNs) are one of the most investigated biological networks in Systems Biology because their work involves all living activities in the cell. A powerful but simple model of such GRNs are Boolean Networks (BN) that describe interactions among biological compounds in a qualitative manner. One of the most interesting outcomes about GRNss dynamics are the so called network attractors, since they seem to well represent the stable states of a living cell. Though collecting state space trajectories is a quite simple task when the network topology consists of few nodes, it becomes not so trivial when nodes are of the size of hundreds or thousands. Thus, we exploit the MapReduce algorithm in order to cope this complexity on a cloud architecture built for the purpose. We found that scaling-out the problem is a better solution rather than increasing resources on single machine, thus allowing simulations of large networks.

Emerging Models to Improve Storage Management Techniques in Cloud Computing Environments

Services based on cloud computing technologies have become increasingly widespread and pervasive. In recent years the number of cloud provider has increased and there is a growing need to differentiate themselves in the market with more advanced services. The most common services concern cloud platforms provide storage on demand and ensure customer capacity and flexibility through innovative models. Therefore, the technologies for the management and storage of data is undergoing major changes. New methodologies such as Software-Defined Storage and Storage Orchestration are crucial to better address the design and development of cloud architectures in last generation data center. In this paper has conducted an analysis of the methods and software tools for storage management that promise to be challenge in the short term as they improve the technological aspects of the storage systems.

Evaluating Scalability of a Cloud Based Platform for Biological Networks Analysis

Research centers performing biomedical research collide with the problem of the treatment of large amounts of data. Several scientific fields in biomedical adopt technologies that can analyze samples in a more accurate way thanks to the high granularity which the current equipment provide the results. The cloud computing technology allows to create scalable and flexible infrastructures and data management services. In recent years the number of solutions that can be included within the phenomenon of cloud computing has increased. There are many cases of distributed solutions with high storage and processing capacity and the possibility of serving a large number of users. In this paper authors describe a biological networks modeling tool, implemented with the aid of MapReduce algorithms that works on a cluster in a cloud computing infrastructure.

Automatic Dynamic Allocation of Cloud Storage for Scientific Applications

Particularly in scientific community the size of digital data to be stored is ramping up. For those application characterized by very dynamic workloads is difficult to estimate the real size of storage to be allocated and avoid over-provisioning, also in extremely elastic environments as cloud computing. DAViS (Dynamic Allocation of Virtual Storage) is a prototype of a system for dynamically providing virtual block storage to Virtual Machines (VMs), optimizing the physical resources utilization, through dynamic and autonomous resizing of the storage.

BUSSOLA: A Cloud Collaborative Platform of Oriented Services to Passengers

BUS Services Mobilizing Living Lab, namely BUSSOLA, focuses on means of transport for people to make a living lab on the move upon which to design, implement and test innovative services that uses and feeds platform SmartDataNet. In particular, thanks to several sensors and devices, the system pays attention on three main macro-areas: air quality monitoring, passengers monitoring and safety on board the vehicles. A distributed and modular architecture has been adopted, to communicate and manage heterogeneous information. The solution is based on cloud technology for a better flexibility and scalability system and an easier adaptation at future requirements in term of computational resource and storage. It allows to experiment and adopt paradigms typical of the world of the Internet of Things and the Internet of Services, it made available by the platform SmartDataNet starting from the processing of big data and open-data.

Implementing a Cloud-Based Service Supporting Biological Network Simulations

Biological analysis applications are usually high demanding in terms of computational power required. Cloud Computing infrastructures can be of great value supporting those type of applications, thanks to the high flexibility and performing hardware provided. The merging of solutions based on MapReduce and distributed file systems services, allows the creation of scalable infrastructures and data management services adaptable to various use cases and requirements. However, the implementation and deployment of cloud based services remains a complex task for biological researchers without specific skills. In this paper authors describe a service that allows the execution of a biological networks simulation tool in cloud, implemented with the aid of MapReduce algorithms called MaReX. The paper shows in detail the interactions occurring between the various components of the platform such as: database, process manager agent and graphical user interface.