The cloud seems to be an excellent companion of mobile systems, to alleviate battery consumption on smartphones and to backup user's data on-the-fly. Indeed, many recent works focus on frameworks that enable mobile computation offloading to software clones of smartphones on the cloud and on designing cloud-based backup systems for the data stored in our devices. Both mobile computation offloading and data backup involve communication between the real devices and the cloud. This communication does certainly not come for free. It costs in terms of bandwidth (the traffic overhead to communicate with the cloud) and in terms of energy (computation and use of network interfaces on the device).In this work we study the feasibility of both mobile computation offloading and mobile software/data backups in real-life scenarios. In our study we assume an architecture where each real device is associated to a software clone on the cloud. We consider two types of clones: The off-clone, whose purpose is to support computation offloading, and the back-clone, which comes to use when a restore of user's data and apps is needed. We give a precise evaluation of the feasibility and costs of both off-clones and back-clones in terms of bandwidth and energy consumption on the real device. We achieve this through measurements done on a real testbed of 11 Android smartphones and an equal number of software clones running on the Amazon EC2 public cloud. The smartphones have been used as the primary mobile by the participants for the whole experiment duration. I. INTRODUCTIONThe advances in technology of the last decades have undoubtedly turned yesterday's must-have devices into today's stock. Think of the phones with aerials of the late '80, or the Pentium 4 PCs of a few years ago. None of them is comparable to the power of nowadays smartphones, whose recent worldwide market boost is undeniable. We use smartphones to do many of the jobs we used to do on desktops, and many new ones. We browse the Internet, send emails, organize our lives, watch videos, upload data on social networks, use online banking, find our way by using GPS and online maps, and communicate in revolutionary ways. New apps are coming out at an incredible pace. Apple iPhone commercial's call to action "There's an app for everything" says a lot on this Alessandro Mei is supported by a Marie Curie Outgoing International Fellowship funded by the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n. 253461. This work has been technically supported and partially funded by Telecom Italia within the Working Capital project.This work has been performed in the framework of the FP7 project TROPIC IST-318784 STP, which is funded by the European Community. The Authors would like to acknowledge the contributions of their colleagues from TROPIC Consortium (http://www.ict-tropic.eu).

Abstract-We propose the use of opportunistic delegation as a data traffic offload solution to the recent boost up of mobile data consumption in metropolitan areas, by investigating two main questions: (i) "How to gain insights into social mobile networking scenarios?" and (ii) "How to utilize such insights to design solutions to alleviate overloaded 3G networks?". The purpose of our solution is to leverage usage of mobile applications requiring large data transfers by channeling the traffic to a few, socially selected important users in the network called VIP delegates. The proposed VIP selection strategies are based on social network properties and are compared to the optimal solution (that covers 100% of users with minimum number of VIPs). Our extensive experiments with real and synthetic traces show the effectiveness of VIP delegation both in terms of coverage and required number of VIPs -down to 7% in average of VIPs are needed in campuslike scenarios to offload about 90% of the traffic.

No abstract

Abstract. We investigate the feasibility of mounting a de-anonymization attack against Tor and similar low-latency anonymous communication systems by using NetFlow records. Previous research has shown that adversaries with the ability to eavesdrop in real time at a few internet exchange points can effectively monitor a significant part of the network paths from Tor nodes to destination servers. However, the capacity of current networks makes packet-level monitoring at such a scale quite challenging. We hypothesize that adversaries could use less accurate but readily available monitoring facilities, such as Cisco's NetFlow, to mount large-scale traffic analysis attacks. In this paper, we assess the feasibility and effectiveness of traffic analysis attacks against Tor using NetFlow data. We present an active traffic analysis technique based on perturbing the characteristics of user traffic at the server side, and observing a similar perturbation at the client side through statistical correlation. We evaluate the accuracy of our method using both in-lab testing and data gathered from a public Tor relay serving hundreds of users. Our method revealed the actual sources of anonymous traffic with 100% accuracy for the in-lab tests, and achieved an overall accuracy of 81.6% for the real-world experiments with a false positive rate of 5.5%.

Mobile-cloud offloading mechanisms delegate heavy mobile computation to the cloud. In real life use, the energy tradeoff of computing the task locally or sending the input data and the code of the task to the cloud is often negative, especially with popular communication intensive jobs like socialnetworking, gaming, and emailing. We design and build a working implementation of CDroid, a system that tightly couples the device OS to its cloud counterpart. The cloud-side handles data traffic through the device efficiently and, at the same time, caches code and data optimally for possible future offloading. In our system, when offloading decision takes place, input and code are likely to be already on the cloud. CDroid makes mobile cloud offloading more practical enabling offloading of lightweight jobs and communication intensive apps. Our experiments with real users in everyday life show excellent results in terms of energy savings and user experience. This work has been performed in the framework of the FP7 project TROPIC IST-318784 STP, which is funded by the European Community. The Authors would like to acknowledge the contributions of their colleagues from TROPIC Consortium (http://www.ict-tropic.eu).