Network monitoring in public clouds: issues, methodologies, and applications

Cloud computing adoption is rapidly growing thanks to the carried large technical and economical advantages. Its effects can be observed also looking at the fast increase of cloud traffic: in accordance with recent forecasts, more than 75\% of the overall datacenter traffic will be cloud traffic by 2018. Accordingly, huge investments have been made by providers in network infrastructures. Networks of geographically distributed datacenters have been built, which require efficient and accurate monitoring activities to be operated. However, providers rarely expose information about the state of cloud networks or their design, and seldom make promises about their performance. In this scenario, cloud customers therefore have to cope with performance unpredictability in spite of the primary role played by the network. Indeed, according to the deployment practices adopted and the functional separation of the application layers often implemented, the network heavily influences the performance of the cloud services, also impacting costs and revenues. In this thesis cloud networks are investigated enforcing non-cooperative approaches, i.e.~that do not require access to any information restricted to entities involved in the cloud service provision. A platform to monitor cloud networks from the point of view of the customer is presented. Such a platform enables general customers---even those with limited expertise in the configuration and the management of cloud resources---to obtain valuable information about the state of the cloud network, according to a set of factors under their control. A detailed characterization of the cloud network and of its performance is provided, thanks to extensive experimentations performed during the last years on the infrastructures of the two leading cloud providers (Amazon Web Services and Microsoft Azure). The information base gathered by enforcing the proposed approaches allows customers to better understand the characteristics of these complex network infrastructures. Moreover, experimental results are also useful to the provider for understanding the quality of service perceived by customers. By properly interpreting the obtained results, usage guidelines can be devised which allow to enhance the achievable performance and reduce costs. As a particular case study, the thesis also shows how monitoring information can be leveraged by the customer to implement convenient mechanisms to scale cloud resources without any a priori knowledge. More in general, we believe that this thesis provides a better-defined picture of the characteristics of the complex cloud network infrastructures, also providing the scientific community with useful tools for characterizing them in the future

Prospective validation of the CLIP score: a new prognostic system for patient with cirrhosis and hepatocellular carcinoma

Prognosis of patients with cirrhosis and hepatocellular carcinoma (HCC) depends on both residual liver function and tumor extension. The CLIP score includes Child-Pugh stage, tumor morphology and extension, serum alfa-fetoprotein (AFP) levels, and portal vein thrombosis. We externally validated the CLIP score and compared its discriminatory ability and predictive power with that of the Okuda staging system in 196 patients with cirrhosis and HCC prospectively enrolled in a randomized trial. No significant associations were found between the CLIP score and the age, sex, and pattern of viral infection. There was a strong correlation between the CLIP score and the Okuda stage, As of June 1999, 150 patients (76.5%) had died. Median survival time was 11 months, overall, and it was 36, 22, 9, 7, and 3 months for CLIP categories 0, 1, 2, 3, and 4 to 6, respectively. In multivariate analysis, the CLIP score had additional explanatory power above that of the Okuda stage. This was true for both patients treated with locoregional therapy or not. A quantitative estimation of 2-year survival predictive power showed that the CLIP score explained 37% of survival variability, compared with 21% explained by Okuda stage. In conclusion, the CLIP score, compared with the Okuda staging system, gives more accurate prognostic information, is statistically more efficient, and has a greater survival predictive power. It could be useful in treatment planning by improving baseline prognostic evaluation of patients with RCC, and could be used in prospective therapeutic trials as a stratification variable, reducing the variability of results owing to patient selection

On the integration of cloud computing and internet of things

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. 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. Highlights (for review

Urinary p-Cresol in ASD

The etiology of autism encompasses a broad range of causative events, ranging from rare de novo high-penetrance mutations affecting genes such as NLGN3/4, SHANK3, NRXN1, and MECP2, to a complex mix of environmental and epigenetic factors acting upon a vulnerable genetic background. Increasing prevalence rates, decreasing heritability estimates, polygenic models with multiple incompletely penetrant de novo mutations, all suggest that, in addition to broader diagnostic criteria and increased awareness, also a real increase in incidence primarily due to greater gene-environment interactions may also be occurring. Notably, the phenotypic heterogeneity of ASD suggests the existence of many “autisms”, each characterized by specific etiopathogenetic underpinnings. Given the complex nature of this disorder, great effort is now underway aiming to define a reliable panel of biological markers able to assist clinicians in an early diagnosis. P-cresol (4-methylphenol) belongs to the cresol class of organic aromatic compounds. Environmental p-cresol is absorbed through the gastrointestinal and the respiratory tracts, as well as through the intact skin. Physiological sources of p-cresol are represented by some gut bacteria which express synthetic enzymes not found in human cells. Urinary p-cresol or its conjugated derivative p-cresylsulfate hold the promise of representing one of these biomarkers in small autistic children and could contribute to identify a subgroup of ASD children characterized by greater severity, to better delineate abnormal gut function in autism and perhaps the entire pathophysiology of the disease in at least some individuals

Autism genetics

Autism spectrum disorder (ASD) is a severe neuropsychiatric disease with strong genetic underpinnings. However, genetic contributions to autism are extremely heterogeneous, with many different loci underlying the disease to a different extent in different individuals. Moreover, the phenotypic expression (i.e., "penetrance") of these genetic components is also highly variable, ranging from fully penetrant point mutations to polygenic forms with multiple gene-gene and gene-environment interactions. Furthermore, many genes involved in ASD are also involved in intellectual disability, further underscoring their lack of specificity in phenotypic expression. We shall hereby review current knowledge on the genetic basis of ASD, spanning genetic/genomic syndromes associated with autism, monogenic forms due to copy number variants (CNVs) or rare point mutations, mitochondrial forms, and polygenic autisms. Finally, the recent contributions of genome-wide association and whole exome sequencing studies will be highlighted

Urinary p-cresol in autism spectrum disorder

Autism spectrum disorder (ASD) is a neuropsychiatric disorder with onset during early childhood and life-long consequences in most cases. It is characterized by impairment in social interaction and communication, as well as by restricted patterns of interest and stereotyped behaviors. The etiology of autism is highly heterogeneous, encompassing a large range of genetic and environmental factors. Several lines of evidence suggest that, in addition to broader diagnostic criteria and increased awareness, also a real increase in incidence primarily due to greater gene-environment interactions may also be occurring. Environmental exposure to the organic aromatic compound p-cresol (4-methylphenol) is relatively common and occurs through the skin, as well as the gastrointestinal and respiratory systems. However, the largest and most widespread source of this compound is represented by some gut bacteria which express p-cresol synthesizing enzymes not found in human cells. Urinary p-cresol and its conjugated derivative p-cresylsulfate have been found elevated in an initial sample and recently in a replica sample of autistic children below 8 years of age, where it is associated with female sex, greater clinical severity regardless of sex, and history of behavioral regression. Potential sources of p-cresol excess in ASD, such as gut infection, chronic constipation, antibiotics, abnormal intestinal permeability, and environmental exposure, are being investigated. P-cresol may contribute to worsen autism severity and gut dysfunction, often present in autistic children. It may also contribute to a multibiomarker diagnostic panel useful in small autistic children

Pythia: Yet another active probing technique for alias resolution

An accurate and exhaustive knowledge of the Internet topol-ogy is essential for a deep understanding of such a com-plex 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 ad-dresses owned by the same network layer device. While sev-eral techniques exist, each solution shows specific limitations such that the alias resolution problem appears far from be-ing 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 ex-ploits 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 com-pare it to previously proposed techniques according to mul-tiple performance metrics. Experimental results show how Pythia reaches higher performance in terms of applicability and trustworthiness