Valerio Persico

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.

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.

Measuring network throughput in the cloud

Cloud providers employ sophisticated virtualization techniques and strategies for sharing resources among a large number of largely uncoordinated and mutually untrusted customers. The shared networking environment, in particular, dictates the need for mechanisms to partition network resources among virtual machines. At the same time, the performance of applications deployed over these virtual machines may be heavily impacted by the performance of the underlying network, and therefore by such mechanisms. Nevertheless, due to security and commercial reasons, providers rarely provide detailed information on network organization, performance, and mechanisms employed to regulate it. In addition, the scientific literature only provides a blurred image of the network performance inside the cloud. The few available pioneer works marginally focus on this aspect, use different methodologies, operate in few limited scenarios, or report conflicting results.In this paper, we present a detailed analysis of the performance of the internal network of Amazon EC2, performed by adopting a non-cooperative experimental evaluation approach (i.e.?not relying on provider support). Our aim is to provide a quantitative assessment of the networking performance as a function of the several variables available, such as geographic region, resource price or size. We propose a detailed methodology to perform this kind of analysis, which we believe is essential in a such complex and dynamic environment. During this analysis we have discovered and analyzed the limitations enforced by Amazon over customer traffic in terms of maximum throughput allowed. Thanks to our work it is possible to understand how the complex mechanisms enforced by the provider in order to manage its infrastructure impact the performance perceived by the cloud customers and potentially tamper with monitoring and controlling approaches previously proposed in literature. Leveraging our knowledge of the bandwidth-limiting mechanisms, we then present a clear picture of the maximum throughput achievable in Amazon EC2 network, shedding light on when and how such maximum throughput can be achieved and at which cost.

Don't trust traceroute (completely)

In this work, we propose a methodology based on the alias resolution process to demonstrate that the IP level view of the route provided by traceroute may be a poor representation of the real router-level route followed by the traffic. More precisely, we show how the traceroute output can lead one to (i) inaccurately reconstruct the route by overestimating the load balancers along the paths toward the destination and (ii) erroneously infer routing changes.

A sleep scheduling approach based on learning automata for WSN partialcoverage

Wireless sensor networks (WSNs) are currently adopted in a vast variety of domains where sensor energy consumption is a critical challenge because of the existing power constraints. Sleep scheduling approaches have recently attracted the interest of the scientific community, as they give the opportunity of turning off the redundant nodes of a network to save energy and prolong the lifetime of the network without suspending the monitoring activities performed by the WSN.Our study focuses on the problem of partial coverage, targeting scenarios in which the continuous monitoring of a limited portion of the area of interest is enough. In this paper we present PCLA, a novel algorithm that relies on Learning Automata to implement sleep scheduling approaches. It aims at minimizing the number of sensors to activate for covering a desired portion of the region of interest preserving the connectivity among sensors. Simulation results show how PCLA can select sensors in an efficient way to satisfy the imposed constraints, thus guaranteeing good performance in terms of time complexity, working-node ratio, scalability, and WSN lifetime. Moreover, compared to the state of the art, PCLA is able to guarantee better performance.

On Network Throughput Variability in Microsoft Azure Cloud

The dependence of the industry on cloud-based infrastructures has grown much faster than our understanding of the performance limits and dynamics of these environments. An aspect only marginally analyzed in the past is related to the performance of the intra-cloud network connecting the virtual machines (VMs) deployed in the same data center. The few available works either do not exhaustively describe the adopted methodology or employed different approaches causing the analyses to be hard to replicate, and the results to be hard to compare. In addition, cloud customers can today highly customize their cloud environments while previous works considered only a few of the scenarios in which a customer may operate. In this paper, we provide an intra-cloud network performance characterization of Microsoft (MS) Azure, a leading provider only preliminary investigated from this angle. We first propose and thoroughly detail a methodology to carry out similar analyses, thus encouraging its replication also in other contexts; then we apply this methodology to characterize the intra-cloud network performance in terms of maximum network throughput. More specifically, we investigate whether and how the achievable throughput between two VMs varies (i) over time; (ii) when the customer operates different decisions on VM size, network configuration, geographic region, and transport protocol; and (iii) when the customer operates the same decisions on these factors. Our analysis aims at addressing the gap existing in the literature by providing the most exhaustive and detailed results about the intra-cloud network performance for MS Azure today available.

Pythia: yet another active probing technique for alias resolution

An accurate and exhaustive knowledge of the Internet topology is essential for a deep understanding of such a complex and ever-evolving ecosystem. In this context, a well-known key challenge is represented by alias resolution, i.e. the process of grouping under a unique identifier the addresses owned by the same network layer device. While several techniques exist, each solution shows specific limitations such that the alias resolution problem appears far from being definitively solved. In this work, inspired by a previous technique and the lessons learned by experimenting with IP options, we present, evaluate and release Pythia, a novel active probing-based alias resolution technique. Pythia exploits a combination of (i) UDP packet probes and (ii) the IP Prespecified Timestamp option and it is purposely designed to reconstruct a specific category of routers. By using the reliable topological information provided by IGMP probing as a reference, we experimentally evaluate Pythia and compare it to previously proposed techniques according to multiple performance metrics. Experimental results show how Pythia reaches higher performance in terms of applicability and trustworthiness.

On the performance of the wide-area networks interconnecting public-cloud datacenters around the globe

According to current usage patterns, research trends, and latest reports, the performance of the wide-area networks interconnecting geographically distributed cloud nodes (i.e. inter-datacenter networks) is gaining more and more interest. In this paper we leverage only active approachesthus we do not rely on information restricted to providersand propose a deep analysis of these infrastructures for the two public-cloud leading providers: Amazon Web Services and Microsoft Azure. Our study provides an assessment of the performance of these networks as a function of the several configuration factors under the control of the customer and evidences specific cases of particular interest. The analysis of these cases and of their root causes, also related with service fees, provides insights on their impact on both the Quality of Service perceived by cloud customers and the outcomes of studies neglecting them.Our results show that Azure inter-datacenter infrastructure performs better than Amazons in terms of throughput (+56%, on average). On the other hand, the performance of the two providers is comparable in terms of latency, with the exception of limited specific cases. Moreover, some of the configuration factors cloud customers can leverage (such as larger more expensive VM sizes, advertised to have better network performance) may have no effect on the inter-datacenter network performance actually perceived. Counterintuitively, lower performance may even be related to higher costs for the customer. Experimental evidences show that public-cloud providers also rely on external network providers for some geographical regions, which is the cause of lower performance and higher costs. A comparison with previous works show that TCP throughput has not been improved recently, while evidences of higher link capacities have been found.

CloudSurf: A platform for monitoring public-cloud networks

Despite customers increasingly depend on cloud systems, that are not advertised with any information about either the design or the performance figures of the network infrastructures that support them. In this paper, we introduce CloudSurf, an open-source platform we have designed, implemented, and recently publicly released [5]. CloudSurf allows to monitor public-cloud networking infrastructures from the customer viewpoint throughnon-cooperative approaches, i.e. without relying on information restricted to the cloud provider or to entities playing a privileged role with respect to the provision of cloud services. After having identified a set of desirable features, we discuss the design of the platform, also showing its effectiveness through a set of use cases. Thanks to CloudSurf it is possible to go beyond the coarse information about public-cloud network performance today available. The information collected by CloudSurf can guide customers in performing cloud-services configuration, thus allowing them to improve cloud-network performance, to understand its variability, and to reduce costs.

A First Look at Public-Cloud Inter-Datacenter Network Performance

Abstract-Public-cloud providers do not disclose quantitative information about the performance of their inter- datacenter networks in spite of their importance and of the growing interest they are attracting. In this paper we propose an analysis of the inter-datacenter network of the two leading providers-Amazon Web Services and Microsoft Azure- only leveraging active monitoring approaches and thus not relying on information restricted to providers. Our results show that Azure inter- datacenter infrastructure performs better than Amazons in terms of throughput (+52%, on average). On the other hand, the performance of the two providers is comparable in terms of latency, with the exception of isolated cases. Counterintuitively, lower performance may be even related to higher costs for the customer. Network management policies that may severely impact both the performance perceived by the customers and the results of the measurement activities have been observed and characterized. Finally, a comparison with previous works shows that TCP throughput has not improved recently.