The Evolution of Internet interconnections

In 1995, the NSF officially shut down the NSFNet backbone, thereby ending the nascent Internets early architecture as a single backbone network. Today, the Internet is a group of loosely interconnected networks run by many diverse companies. These interconnections are in no way controlled by any industry or government agency, and are therefore held together only by the market demands of the Internet community. Although the FCC has traditionally maintained a stance of unregulation of all information and computer networks, they have increasingly show interest in ensuring the rapid deployment of Internet access. In addition, as more and more critical elements of communication are implemented on the Internet, some safeguards ensuring end-to-end connectivity, and therefore on maintaining the interconnection between networks, are needed. This paper discusses the history and evolution of Internet interconnections, compares and contrasts them to traditional telephony interconnections, and explores the possibility of regulation over such connections. This paper covers events up to the end of 1999

Efficient Interconnection Network Design for Heterogeneous Architectures

The onset of big data and deep learning applications, mixed with conventional general-purpose programs, have driven computer architecture to embrace heterogeneity with specialization. With the ever-increasing interconnected chip components, future architectures are required to operate under a stricter power budget and process emerging big data applications efficiently. Interconnection network as the communication backbone thus is facing the grand challenges of limited power envelope, data movement and performance scaling. This dissertation provides interconnect solutions that are specialized to application requirements towards power-/energy-efficient and high-performance computing for heterogeneous architectures. This dissertation examines the challenges of network-on-chip router power-gating techniques for general-purpose workloads to save static power. A voting approach is proposed as an adaptive power-gating policy that considers both local and global traffic status through router voting. In addition, low-latency routing algorithms are designed to guarantee performance in irregular power-gating networks. This holistic solution not only saves power but also avoids performance overhead. This research also introduces emerging computation paradigms to interconnects for big data applications to mitigate the pressure of data movement. Approximate network-on-chip is proposed to achieve high-throughput communication by means of lossy compression. Then, near-data processing is combined with in-network computing to further improve performance while reducing data movement. The two schemes are general to play as plug-ins for different network topologies and routing algorithms. To tackle the challenging computational requirements of deep learning workloads, this dissertation investigates the compelling opportunities of communication algorithm-architecture co-design to accelerate distributed deep learning. MultiTree allreduce algorithm is proposed to bond with message scheduling with network topology to achieve faster and contention-free communication. In addition, the interconnect hardware and flow control are also specialized to exploit deep learning communication characteristics and fulfill the algorithm needs, thereby effectively improving the performance and scalability. By considering application and algorithm characteristics, this research shows that interconnection network can be tailored accordingly to improve the power-/energy-efficiency and performance to satisfy heterogeneous computation and communication requirements

Selling The American People: Data, Technology, And The Calculated Transformation Of Advertising

This dissertation tells the history of a future imagined by advertisers as they interpreted and constructed the affordances of digital information technologies. It looks at how related efforts to predict and influence consumer habits and to package and sell audience attention helped orchestrate the marriage of behavioral science and big-data analytics that defines digital marketing today. My research shows how advertising and commercial media industries rebuilt their information infrastructures around electronic data processing, networked computing, and elaborate forms of quantitative analysis, beginning in the 1950s. Advertisers, agencies, and media companies accommodated their activities to increasingly calculated ways of thinking about consumers and audiences, and to more statistical and computational forms of judgement. Responding to existing priorities and challenges, and to perceived opportunities to move closer to underlying ambitions, a variety of actors envisioned the future of marketing and media through a set of possibilities that became central to the commercialization of digital communications. People involved in the television business today use the term “advanced advertising” to describe a set of abilities at the heart of internet and mobile marketing: programmability (automation), addressability (personalization), shoppability (interactive commerce), and accountability (measurement and analytics). In contrast to the perception that these are unique elements of a “new” digital media environment that emerged in the mid-1990s, I find that these themes appear conspicuously in designs for using and shaping information technologies over the course of the past six decades. I use these potential abilities as entry points for analyzing a broader shift in advertising and commercial media that began well before the popular arrival of the internet. Across the second half of the twentieth century, the advertising industry, a major cultural and economic institution, was reconstructed around the goal of expanding its abilities to account for and calculate more of social and personal life. This transformation sits at an intersection where the processing of data, the processing of commerce, and the processing of culture collide