@inproceedings {Cas2015a,
	title = {On the Analysis of QoE in Cellular Networks: from Subjective Tests to Large-scale Traffic Measurements},
	booktitle = {6th International Workshop on Traffic Analysis and Characterization (TRAC)},
	year = {2015},
	author = {Pedro Casas and Martin Varela and Pierdomenico Fiadino and Mirko Schiavone and Helena Rivas and Raimund Schatz}
}
@inproceedings {Bae2015,
	title = {Cache Oblivious Scheduling of Shared Workloads},
	booktitle = {31st IEEE International Conference on Data Engineering (ICDE)},
	year = {2015},
	month = {05/2015},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Seoul, Korea},
	abstract = {<p>Shared workload optimization is feasible if the set of tasks to be executed is known in advance, as is the case in updating a set of materialized views or executing an extract-transform-load workflow. In this paper, we consider dataintensive shared workloads with precedence constraints arising from data dependencies, i.e., before executing some task, other tasks may have to run first and generate some data needed by the next task(s). While there has been previous work on identifying common subexpressions in shared workloads and task re-ordering to enable shared scans, in this paper we go a step further and solve the problem of scheduling shared data-intensive workloads in a cache-oblivious way. Our solution relies on a novel formulation of precedence constrained scheduling with the additional constraint that once a data item is in the cache, all tasks that require this data item should execute as soon as possible thereafter. The intuition behind this formulation is that the longer a data item remains in the cache, the more likely it is to be evicted regardless of the cache size. We give an optimal ordering algorithm using A* search over the space of possible orderings, and we propose efficient and effective heuristics that obtain nearly-optimal results in much less time. We present experimental results on real-life data warehouse workloads and the TCP-DS benchmark to validate our claims.</p>},
	author = {Arian B{\"a}r and Lukasz Golab and Stefan Ruehrup and Mirko Schiavone and Pedro Casas}
}
@inproceedings {Fia2015b,
	title = {Challenging Entropy-based Anomaly Detection and Diagnosis in Cellular Networks},
	booktitle = {ACM SIGCOMM},
	year = {2015},
	author = {Pierdomenico Fiadino and Alessandro D{\textquoteright}Alconzo and Mirko Schiavone and Pedro Casas}
}
@inproceedings {Cas2015,
	title = {QoMOSN - On the Analysis of Traffic and Quality of Experience in Mobile Online Social Networks},
	booktitle = {European Conference on Networks and Communications (EuCNC)},
	year = {2015},
	author = {Pedro Casas and Pierdomenico Fiadino and Mirko Schiavone}
}
@inproceedings {Fia2015a,
	title = {RCATool - A Framework for Detecting and Diagnosing Anomalies in Cellular Networks},
	booktitle = {27th International Teletraffic Congress (ITC)},
	year = {2015},
	author = {Pierdomenico Fiadino and Alessandro D{\textquoteright}Alconzo and Mirko Schiavone and Pedro Casas}
}
@inproceedings {Fia2015,
	title = {Towards Automatic Detection and Diagnosis of Internet Service Anomalies via DNS Traffic Analysis},
	booktitle = {6th International Workshop on Traffic Analysis and Characterization (TRAC)},
	year = {2015},
	author = {Pierdomenico Fiadino and Alessandro D{\textquoteright}Alconzo and Mirko Schiavone and Pedro Casas}
}
@inproceedings {Fiaa,
	title = {Vivisecting WhatsApp through Large-Scale Measurements in Mobile Networks},
	booktitle = {SIGCOMM 2014},
	year = {2014},
	month = {08/2014},
	publisher = {ACM},
	organization = {ACM},
	address = {Chicago, USA},
	abstract = {<p>WhatsApp, the new giant in instant multimedia messaging in mobile networks is rapidly increasing its popularity, taking over the traditional SMS/MMS messaging. In this paper we present the first large-scale characterization of WhatsApp, useful among others to ISPs willing to understand the impacts of this and similar applications on their networks. Through the combined analysis of passive measurements at the core of a national mobile network, worldwide geo-distributed active measurements, and traffic analysis at end devices, we show that: (i) the WhatsApp hosting architecture is highly centralized and exclusively located in the US; (ii) video sharing covers almost 40\% of the total WhatsApp traffic volume; (iii) flow characteristics depend on the OS of the end device; (iv) despite the big latencies to US servers, download throughputs are as high as 1.5 Mbps; (v) users react immediately and negatively to service outages through social networks feedbacks.</p>},
	keywords = {Large-Scale Measurements, mobile networks, WhatsApp},
	author = {Pierdomenico Fiadino and Mirko Schiavone and Pedro Casas}
}
