Browsing by Subject "Cloud"

Security is always a big concern. According to the statistics, there are over 80,000 cyberattacks per day or over 30 million attacks per year. To make the Internet safe, both the industry and academia propose many solutions. However, these security solutions mainly concentrate on being effective, and ignore the other two features: deployment cost and usability. Therefore, though many works have been proposed to improve security, attacks still happen frequently.

Our goal is to improve network security with low-cost and easy-to-adopt solutions. In this thesis, we choose Distributed Denial-of-Services (DDoS) attack and I/O path malware attack as two representatives. Fueled by IoT botnets and DDoS-for-Hire services, DDoS attacks have reached a record high volume, and launching such attacks is increasingly easy and cheap. We speculate the main reasons why existing solutions still leave DDoS as the top threat are 1) Commercial DDoS protection services are costly. 2) Solutions that require upgrading the core Internet architecture turned out to be extremely difficult to deploy. Similarly, modern operating systems enable user-level malware to log a user's keystrokes or scrape a user's screen output, which usually contains user sensitive data. Solutions with trusted hardware, virtual machines, and mobile phone facilitation all have high costs of deployment and usability for non-expert users.

In this thesis, we present our low-cost and easy-to-adopt solutions to these two attacks. Specifically, 1) Dynashield, an on-demand DDoS defense architecture built on top of different cloud services. Dynashield introduces lower financial cost than Protection-as-a-Service product like Cloudflare, and is easier to adopt than network architecture based solutions. 2) Switchman, a framework to protect a user's I/O paths against user-level malware attacks stealing sensitive privacy data. Switchman helps non-expert users protect their sensitive data. It is easier to adopt than trusted hardware solutions like Intel SGX, and has higher usability compared to VM and additional devices based solutions.

Crowdsourced live video streaming from users is on the rise. Several factors such as social networks, streaming applications, smartphones with high-quality cameras, and ubiquitous wireless connectivity are contributing to this phenomenon. Unlike isolated professional videos, live streams emerge at an unprecedented scale, poorly captured, unorganized, and lack user context. To utilize the full potential of this medium and enable new services on top, immediate addressing of open challenges is required. Smartphones are resource constrained -- battery power is limited, bandwidth is scarce, on-board computing power and storage is insufficient to meet real-time demand. Therefore, mobile cloud computing is cited as an obvious alternative where cloud does the heavy-lifting for the smartphone. But, cloud resources are not cheap and real-time processing demands more than what the cloud can deliver.

This dissertation argues that throwing cloud resources at these problems and blindly offloading computation, while seemingly necessary, may not be sufficient. Opportunities need to be identified to streamline big-scale problems by leveraging in device capabilities, thereby making them amenable to a given cloud infrastructure. One of the key opportunities, we find, is the cross-correlation between different streams of information available in the cloud. We observe that inferences on a single information stream may often be difficult, but when viewed in conjunction with other information dimensions, the same problem often becomes tractable.

Cloud computing appears to be a paradigm shift in service oriented computing. Massively scalable Cloud architectures are spawned by new business and social applications as well as Internet driven economics. Besides being inherently large scale and highly distributed, Cloud systems are almost always virtualized and operate in automated shared environments. The deployed Cloud services are still in their infancy and a variety of research challenges need to be addressed to predict their long-term behavior. Performance and dependability of Cloud services are in general stochastic in nature and they are affected by a large number of factors, e.g., nature of workload and faultload, infrastructure characteristics and management policies. As a result, developing scalable and predictive analytics for Cloud becomes difficult and non-trivial. This dissertation presents the research framework needed to develop high fidelity stochastic models for large scale enterprise systems using Cloud computing as an example. Throughout the dissertation, we show how the developed models are used for: (i) performance and availability analysis, (ii) understanding of power-performance trade-offs, (ii) resiliency quantification, (iv) cost analysis and capacity planning, and (v) risk analysis of Cloud services. In general, the models and approaches presented in this thesis can be useful to a Cloud service provider for planning, forecasting, bottleneck detection, what-if analysis or overall optimization during design, development, testing and operational phases of a Cloud.