Walter de Donato

Integration of Cloud computing and Internet of Things

Cloud computing and Internet of Things (IoT) are two very different technologies that are both already part of our life. Their adoption and use are expected to be more and more pervasive, making them important components of the Future Internet. A novel paradigm where Cloud and IoT are merged together is foreseen as disruptive and as an enabler of a large number of application scenarios.In this paper, we focus our attention on the integration of Cloud and IoT, which is what we call the CloudIoT paradigm. Many works in literature have surveyed Cloud and IoT separately and, more precisely, their main properties, features, underlying technologies, and open issues. However, to the best of our knowledge, these works lack a detailed analysis of the new CloudIoT paradigm, which involves completely new applications, challenges, and research issues. To bridge this gap, in this paper we provide a literature survey on the integration of Cloud and IoT. Starting by analyzing the basics of both IoT and Cloud Computing, we discuss their complementarity, detailing what is currently driving to their integration. Thanks to the adoption of the CloudIoT paradigm a number of applications are gaining momentum: we provide an up-to-date picture of CloudIoT applications in literature, with a focus on their specific research challenges. These challenges are then analyzed in details to show where the main body of research is currently heading. We also discuss what is already available in terms of platforms-both proprietary and open source-and projects implementing the CloudIoT paradigm. Finally, we identify open issues and future directions in this field, which we expect to play a leading role in the landscape of the Future Internet. Vision and motivations for the integration of Cloud computing and Internet of Things (IoT).Applications stemming from the integration of Cloud computing and IoT.Hot research topics and challenges in the integrated scenario of Cloud computing and IoT.Open issues and future directions for research in this scenario.

Survey Cloud monitoring: A survey

Nowadays, Cloud Computing is widely used to deliver services over the Internet for both technical and economical reasons. The number of Cloud-based services has increased rapidly and strongly in the last years, and so is increased the complexity of the infrastructures behind these services. To properly operate and manage such complex infrastructures effective and efficient monitoring is constantly needed. Many works in literature have surveyed Cloud properties, features, underlying technologies (e.g. virtualization), security and privacy. However, to the best of our knowledge, these surveys lack a detailed analysis of monitoring for the Cloud. To fill this gap, in this paper we provide a survey on Cloud monitoring. We start analyzing motivations for Cloud monitoring, providing also definitions and background for the following contributions. Then, we carefully analyze and discuss the properties of a monitoring system for the Cloud, the issues arising from such properties and how such issues have been tackled in literature. We also describe current platforms, both commercial and open source, and services for Cloud monitoring, underlining how they relate with the properties and issues identified before. Finally, we identify open issues, main challenges and future directions in the field of Cloud monitoring.

On the Integration of Cloud Computing and Internet of Things

Cloud computing and Internet of Things (IoT), two very different technologies, are both already part of our life. Their massive adoption and use is expected to increase further, making them important components of the Future Internet. A novel paradigm where Cloud and IoT are merged together is foreseen as disruptive and an enabler of a large number of application scenarios. In this paper we focus our attention on the integration of Cloud and IoT, which we call the CloudIoT paradigm. Many works in literature have surveyed Cloud and IoT separately: their main properties, features, underlying technologies, and open issues. However, to the best of our knowledge, these works lack a detailed analysis of the CloudIoT paradigm. To bridge this gap, in this paper we review the literature about the integration of Cloud and IoT. We start analyzing and discussing the need for integrating them, the challenges deriving from such integration, and how these issues have been tackled in literature. We then describe application scenarios that have been presented in literature, as well as platforms -- both commercial and open source -- and projects implementing the CloudIoT paradigm. Finally, we identify open issues, main challenges and future directions in this promising field.

D-ITG: Distributed Internet Traffic Generator

In this paper we present our traffic generation platform, named D-ITG (Distributed Internet Traffic Generator). Its features are first described. We believe that some capabilities are in fact innovative, since no other traffic generator offers them. Then, we describe the original architecture of D-ITG, which allows our traffic generator to achieve high performance. We hint at a comparison with other traffic generators and conclude with the ongoing work to add new features.

TIE: A Community-Oriented Traffic Classification Platform

The research on network traffic classification has recently become very active. The research community, moved by increasing difficulties in the automated identification of network traffic, started to investigate classification approaches alternative to port-based and payload-based techniques. Despite the large quantity of works published in the past few years on this topic, very few implementations targeting alternative approaches have been made available to the community. Moreover, most approaches proposed in literature suffer of problems related to the ability of evaluating and comparing them. In this paper we present a novel community-oriented software for traffic classification called TIE, which aims at becoming a common tool for the fair evaluation and comparison of different techniques and at fostering the sharing of common implementations and data. Moreover, TIE supports the combination of more classification plugins in order to build multi-classifier systems, and its architecture is designed to allow online traffic classification.

Cloud monitoring: Definitions, issues and future directions

Despite its importance for operating Cloud systems, Cloud monitoring has received limited attention from the research community. In this position paper, we provide an analysis of Cloud monitoring. More precisely, we discuss the main motivations, basic concepts and definitions, and point out open research issues and future directions for Cloud monitoring.

Measuring home broadband performance

We present the results from the first study of Internet access link performance measured directly from home routers. In conjunction with the Federal Communication Commissions study of broadband Internet access in the United States, we investigate the throughput and latency of network access links from about 4000 routers across eight ISPs. Our findings provide a snapshot of access network performance across the United States, offer insights on how access network performance should be measured and presented to users, and inform various ongoing efforts to evaluate the performance of access networks around the world.

Traffic identification engine: an open platform for traffic classification

The availability of open source traffic classification systems designed for both experimental and operational use, can facilitate collaboration, convergence on standard definitions and procedures, and reliable evaluation of techniques. In this article, we describe Traffic Identification Engine (TIE), an open source tool for network traffic classification, which we started developing in 2008 to promote sharing common implementations and data in this field. We designed TIE¿s architecture and functionalities focusing on the evaluation, comparison, and combination of different traffic classification techniques, which can be applied to both live traffic and previously captured traffic traces. Through scientific collaborations, and thanks to the support of the open source community, this platform gradually evolved over the past five years, supporting an increasing number of functionalities, some of which we highlight in this article through sample use cases.

Detecting third-party addresses in traceroute traces with IP timestamp option

Traceroute is one of the most famous and widely adopted diagnostic tool for computer networks. Although traceroute is often used to infer links between Autonomous Systems (ASes), the presence of the so-called third-party (TP) addresses may induce the inference of false AS-level links. In this paper, we propose a novel active probing technique based on the IP timestamp option able to identify TP addresses. For evaluating both the applicability and the utility of the proposed technique, we perform a large-scale measurement campaign targeting --- from multiple vantage points --- more than 327K destinations belonging to about 14K ASes. The results show how TP addresses are very common and affect about 17% of AS-level links extracted from traceroute traces. Compared to a previously proposed heuristic method, our technique allows to identify many more TP addresses and to re-interpret part of its results.

K-dimensional trees for continuous traffic classification

The network measurement community has proposed multiple machine learning (ML) methods for traffic classification during the last years. Although several research works have reported accuracies over 90%, most network operators still use either obsolete (e.g., port-based) or extremely expensive (e.g., pattern matching) methods for traffic classification. We argue that one of the barriers to the real deployment of ML-based methods is their time-consuming training phase. In this paper, we revisit the viability of using the Nearest Neighbor technique for traffic classification. We present an efficient implementation of this well-known technique based on multiple K-dimensional trees, which is characterized by short training times and high classification speed.This allows us not only to run the classifier online but also to continuously retrain it, without requiring human intervention, as the training data become obsolete. The proposed solution achieves very promising accuracy (>95%) while looking just at the size of the very first packets of a flow. We present an implementation of this method based on the TIE classification engine as a feasible and simple solution for network operators.