Network Traffic Measurements, Applications to Internet Services and Security

The Internet has become along the years a pervasive network interconnecting billions of users and is now playing the role of collector for a multitude of tasks, ranging from professional activities to personal interactions. From a technical standpoint, novel architectures, e.g., cloud-based services and content delivery networks, innovative devices, e.g., smartphones and connected wearables, and security threats, e.g., DDoS attacks, are posing new challenges in understanding network dynamics. In such complex scenario, network measurements play a central role to guide traffic management, improve network design, and evaluate application requirements. In addition, increasing importance is devoted to the quality of experience provided to final users, which requires thorough investigations on both the transport network and the design of Internet services. In this thesis, we stress the importance of users’ centrality by focusing on the traffic they exchange with the network. To do so, we design methodologies complementing passive and active measurements, as well as post-processing techniques belonging to the machine learning and statistics domains. Traffic exchanged by Internet users can be classified in three macro-groups: (i) Outbound, produced by users’ devices and pushed to the network; (ii) unsolicited, part of malicious attacks threatening users’ security; and (iii) inbound, directed to users’ devices and retrieved from remote servers. For each of the above categories, we address specific research topics consisting in the benchmarking of personal cloud storage services, the automatic identification of Internet threats, and the assessment of quality of experience in the Web domain, respectively. Results comprise several contributions in the scope of each research topic. In short, they shed light on (i) the interplay among design choices of cloud storage services, which severely impact the performance provided to end users; (ii) the feasibility of designing a general purpose classifier to detect malicious attacks, without chasing threat specificities; and (iii) the relevance of appropriate means to evaluate the perceived quality of Web pages delivery, strengthening the need of users’ feedbacks for a factual assessment

The SHARC framework:utilizing personal dropbox accounts to provide a scalable solution to the storage and sharing of community generated locative media

The emergence of personal cloud storage services provides a new paradigm for storing and sharing data. In this paper we present the design of the SHARC framework and in particular focus on the utilization of personal Dropbox accounts to provide a scalable solution to the storage and sharing of community generated locative media relating to a community's Cultural Heritage. In addition to scalability issues, the utilization of personal Dropbox storage also supports 'sense of ownership' (relating to community media) which has arisen as an important requirement during our on-going 'research-in-the-wild' working with the rural village community of Wray and involving public display deployments to support the display and sharing of community photos and stories. While the framework presented here is currently being tested with a particular place-based community (Wray), it has been designed to provide a general solution that should support other place-based communities

Energy Efficiency Analysis And Optimization For Mobile Platforms

The introduction of mobile devices changed the landscape of computing. Gradually, these devices are replacing traditional personal computer (PCs) to become the devices of choice for entertainment, connectivity, and productivity. There are currently at least 45.5 million people in the United States who own a mobile device, and that number is expected to increase to 1.5 billion by 2015. Users of mobile devices expect and mandate that their mobile devices have maximized performance while consuming minimal possible power. However, due to the battery size constraints, the amount of energy stored in these devices is limited and is only growing by 5% annually. As a result, we focused in this dissertation on energy efficiency analysis and optimization for mobile platforms. We specifically developed SoftPowerMon, a tool that can power profile Android platforms in order to expose the power consumption behavior of the CPU. We also performed an extensive set of case studies in order to determine energy inefficiencies of mobile applications. Through our case studies, we were able to propose optimization techniques in order to increase the energy efficiency of mobile devices and proposed guidelines for energy-efficient application development. In addition, we developed BatteryExtender, an adaptive user-guided tool for power management of mobile devices. The tool enables users to extend battery life on demand for a specific duration until a particular task is completed. Moreover, we examined the power consumption of System-on-Chips (SoCs) and observed the impact on the energy efficiency in the event of offloading tasks from the CPU to the specialized custom engines. Based on our case studies, we were able to demonstrate that current software-based power profiling techniques for SoCs can have an error rate close to 12%, which needs to be addressed in order to be able to optimize the energy consumption of the SoC. Finally, we summarize our contributions and outline possible direction for future research in this field

On I/O Performance and Cost Efficiency of Cloud Storage: A Client\u27s Perspective

Cloud storage has gained increasing popularity in the past few years. In cloud storage, data are stored in the service provider’s data centers; users access data via the network and pay the fees based on the service usage. For such a new storage model, our prior wisdom and optimization schemes on conventional storage may not remain valid nor applicable to the emerging cloud storage. In this dissertation, we focus on understanding and optimizing the I/O performance and cost efficiency of cloud storage from a client’s perspective. We first conduct a comprehensive study to gain insight into the I/O performance behaviors of cloud storage from the client side. Through extensive experiments, we have obtained several critical findings and useful implications for system optimization. We then design a client cache framework, called Pacaca, to further improve end-to-end performance of cloud storage. Pacaca seamlessly integrates parallelized prefetching and cost-aware caching by utilizing the parallelism potential and object correlations of cloud storage. In addition to improving system performance, we have also made efforts to reduce the monetary cost of using cloud storage services by proposing a latency- and cost-aware client caching scheme, called GDS-LC, which can achieve two optimization goals for using cloud storage services: low access latency and low monetary cost. Our experimental results show that our proposed client-side solutions significantly outperform traditional methods. Our study contributes to inspiring the community to reconsider system optimization methods in the cloud environment, especially for the purpose of integrating cloud storage into the current storage stack as a primary storage layer

Dissecting UbuntuOne: Autopsy of a Global-scale Personal Cloud Back-end

Personal Cloud services, such as Dropbox or Box, have been widely adopted by users. Unfortunately, very little is known about the internal operation and general characteristics of Personal Clouds since they are proprietary services. In this paper, we focus on understanding the nature of Personal Clouds by presenting the internal structure and a measurement study of UbuntuOne (U1). We first detail the U$1$ architecture, core components involved in the U1 metadata service hosted in the datacenter of Canonical, as well as the interactions of U$1$ with Amazon S3 to outsource data storage. To our knowledge, this is the first research work to describe the internals of a large-scale Personal Cloud. Second, by means of tracing the U$1$ servers, we provide an extensive analysis of its back-end activity for one month. Our analysis includes the study of the storage workload, the user behavior and the performance of the U1 metadata store. Moreover, based on our analysis, we suggest improvements to U1 that can also benefit similar Personal Cloud systems. Finally, we contribute our dataset to the community, which is the first to contain the back-end activity of a large-scale Personal Cloud. We believe that our dataset provides unique opportunities for extending research in the field

A Meta-Heuristic Load Balancer for Cloud Computing Systems

This paper introduces a strategy to allocate services on a cloud system without overloading the nodes and maintaining the system stability with minimum cost. We specify an abstract model of cloud resources utilization, including multiple types of resources as well as considerations for the service migration costs. A prototype meta-heuristic load balancer is demonstrated and experimental results are presented and discussed. We also propose a novel genetic algorithm, where population is seeded with the outputs of other meta-heuristic algorithms