Antonio Pescapé

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.

Issues and future directions in traffic classification

Traffic classification technology has increased in relevance this decade, as it is now used in the definition and implementation of mechanisms for service differentiation, network design and engineering, security, accounting, advertising, and research. Over the past 10 years the research community and the networking industry have investigated, proposed and developed several classification approaches. While traffic classification techniques are improving in accuracy and efficiency, the continued proliferation of different Internet application behaviors, in addition to growing incentives to disguise some applications to avoid filtering or blocking, are among the reasons that traffic classification remains one of many open problems in Internet research. In this article we review recent achievements and discuss future directions in traffic classification, along with their trade-offs in applicability, reliability, and privacy. We outline the persistently unsolved challenges in the field over the last decade, and suggest several strategies for tackling these challenges to promote progress in the science of Internet traffic classification.

A tool for the generation of realistic network workload for emerging networking scenarios

Internet workload is a mix of many and complex sources. Therefore, its accurate and realistic replication is a difficult and challenging task. Such difficulties are exacerbated by the multidimensional heterogeneity and scale of the current Internet combined with its constant evolution. The study and generation of network workload is a moving target, both in terms of actors (devices, access networks, protocols, applications, services) and in terms of case studies (the interest expands from performance analysis to topics like network neutrality and security). In order to keep up with the new questions that arise and with the consequent new technical challenges, networking research needs to continuously update its tools. In this paper, we describe the main properties that a network workload generator should have today, and we present a tool for the generation of realistic network workload that can be used for the study of emerging networking scenarios. In particular, we discuss (i) how it tackles the main issues challenging the representative replication of network workload, and (ii) our design choices and its advanced features that make it suitable to analyze complex and emerging network scenarios. To highlight how our tool advances the state-of-the-art, we finally report some experimental results related to the study of hot topics like (a) broadband Internet performance and network neutrality violations; (b) RFC-based security and performance assessment of home network devices; (c) performance analysis of multimedia communications.

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.

Traffic analysis of peer-to-peer IPTV communities

The Internet is currently experiencing one of the most important challenges in terms of content distribution since its first uses as a medium for content delivery: users from passive downloaders and browsers are moving towards content producers and publishers. They often distribute and retrieve multimedia contents establishing network communities. This is the case of peer-to-peer IPTV communities. In this work we present a detailed study of P2P IPTV traffic, providing useful insights on both transport- and packet-level properties as well as on the behavior of the peers inside the network. In particular, we provide novel results on the (i) ports and protocols used; (ii) differences between signaling and video traffic; (iii) behavior of the traffic at different time scales; (iv) differences between TCP and UDP traffic; (v) traffic generated and received by peers; (vi) peers neighborhood and session duration. The knowledge gained thanks to this analysis is useful for several tasks, e.g. traffic identification, understanding the performance of different P2P IPTV technologies and the impact of such traffic on network nodes and links, and building more realistic models for simulations.

Classification of Network Traffic via Packet-Level Hidden Markov Models

Traffic classification and identification is a fertile research area. Beyond Quality of Service, service differentiation, and billing, one of the most important applications of traffic classification is in the field of network security. This paper proposes a packet-level traffic classification approach based on Hidden Markov Model (HMM). Classification is performed by using real network traffic and estimating - in a combined fashion - Packet Size (PS) and Inter Packet Time (IPT) characteristics, thus remaining applicable to encrypted traffic too. The effectiveness of the proposed approach is evaluated by considering several traffic typologies: we applied our model to real traffic traces of Age of Mythology and Counter Strike (two Multi Player Network Games), HTTP, SMTP, Edonkey, PPlive (a peer-to-peer IPTV application), and MSN Messenger. An analytical basis and the mathematical details regarding the model are given. Results show how the proposed approach is able to classify network traffic by using packet-level statistical properties and therefore it is a good candidate as a component for a multi-classification framework.

Seamless internetworking of WLANs and cellular networks: architecture and performance issues in a Mobile IPv6 scenario

We review the problem of network mobility and internetworking between heterogeneous data networks and present an approach to the integration of WLAN and cellular networks based on loose coupling and the use of emerging mobility protocols. The handoff performance of such an approach is studied, at the network and transport levels, in a realistic scenario along with the impact on global performance of transport protocols. Finally, a method of eliminating any packet loss at the network layer during handoff is presented and evaluated.

Early classification of network traffic through multi-classification

In thiswork we present and evaluate different automated combination techniques for traffic classification. We consider six intelligent combination algorithms applied to both traditional and more recent traffic classification techniques using either packet content or statistical properties of flows. Preliminary results show that, when selecting complementary classifiers, some combination algorithms allow a further improvement - in terms of classification accuracy - over already well-performing standalone classification techniques. Moreover, our experiments show that the positive impact of combination is particularly significant when there are early-classification constraints, that is, when the classification of a flow must be obtained in its early stage (e.g. first 1-4 packets) in order to perform network operations online.

Internet traffic modeling by means of Hidden Markov Models

In this work, we propose a Hidden Markov Model for Internet traffic sources at packet level, jointly analyzing Inter Packet Time and Packet Size. We give an analytical basis and the mathematical details regarding the model, and we test the flexibility of the proposed modeling approach with real traffic traces related to common Internet services with strong differences in terms of both applications/users and protocol behavior: SMTP, HTTP, a network game, and an instant messaging platform. The presented experimental analysis shows that, even maintaining a simple structure, the model is able to achieve good results in terms of estimation of statistical parameters and synthetic series generation, taking into account marginal distributions, mutual, and temporal dependencies. Moreover we show how, by exploiting such temporal dependencies, the model is able to perform short-term prediction by observing traffic from real sources.