With the surge of big data applications and the worsening of the memory-wall problem, the memory system, instead of the computing unit, becomes the commonly recognized major concern of computing. However, this "memory-centric" common understanding has a humble beginning. More than three decades ago, the memory-bounded speedup model is the first model recognizing memory as the bound of computing and provided a general bound of speedup and a computing-memory trade-off formulation. The memory-bounded model was well received even by then. It was immediately introduced in several advanced computer architecture and parallel computing textbooks in the 1990's as a must-know for scalable computing. These include Prof. Kai Hwang's book "Scalable Parallel Computing" in which he introduced the memory-bounded speedup model as the Sun-Ni's law, parallel with the Amdahl's and the Gustafson's law. Through the years, the impacts of this model have grown far beyond parallel processing and into the fundamental of computing. In this article, we revisit the memory-bounded speedup model and discuss its progress and impacts in depth to make a unique contribution to this special issue, to stimulate new solutions for big data applications, and to promote data-centric thinking and rethinking.
In a 1961 lecture to celebrate MIT’s centennial, John McCarthy proposed the vision of utility computing, including three key concepts of pay-per-use service, large computer and private computer. Six decades have passed, but McCarthy’s computing utility vision has not yet been fully realized, despite advances in grid computing, services computing and cloud computing. This paper presents a perspective of computing utility called Information Superbahn, building on recent advances in cloud computing. This Information Superbahn perspective retains McCarthy’s vision as much as possible, while making essential modern requirements more explicit, in the new context of a networked world of billions of users, trillions of devices, and zettabytes of data. Computing utility offers pay-per-use computing services through a 1) planet-scale, 2) low-entropy and 3) high-goodput utility. The three salient characteristics of computing utility are elaborated. Initial evidence is provided to support this viewpoint.
The artificial intelligence (AI) community has recently made tremendous progress in developing self-supervised learning (SSL) algorithms that can learn high-quality data representations from massive amounts of unlabeled data. These methods brought great results even to the fields outside of AI. Due to the joint efforts of researchers in various areas, new SSL methods come out daily. However, such a sheer number of publications make it difficult for beginners to see clearly how the subject progresses. This survey bridges this gap by carefully selecting a small portion of papers that we believe are milestones or essential work. We see these researches as the "dots" of SSL and connect them through how they evolve. Hopefully, by viewing the connections of these dots, readers will have a high-level picture of the development of SSL across multiple disciplines including natural language processing, computer vision, graph learning, audio processing, and protein learning.
The development of IP-based Internet of Things (IoT) networks would facilitate more effective end-to-end IP network architectures, but it remains a challenge. Network routing needs to be effectively addressed in the IoT environments of scarce computational and energy resources. Accordingly, the Internet Engineering Task Force (IETF) has specified the IPv6 Routing Protocol for Low Power and Lossy Network (RPL) to provide a bespoke IPv6-based routing framework for IoT networks. However, RPL comes with no Quality of Service (QoS) support which is an essential requirement for many IoT applications. The network research community has introduced a number of research proposals enhancing RPL with different QoS solutions. This paper presents a review of these proposed solutions and aims to establish a firm understanding of recent QoS developments for RPL and possible areas for future IoT routing research. The focus is on comprehending the protocol and networking properties that can affect QoS performance in RPL networks. Consideration is also given to different objective functions developed for addressing varying QoS aspects such as throughput, delay, and packet loss. RPL is also extended in a number of QoS solutions following different approaches at the MAC, network, and application layers. However, there is still a need for further developments to address effective QoS support, particularly for dynamic RPL networks.
Oblivious polynomial evaluation (OPE) is a two-party protocol that allows a receiver, R to learn an evaluation f(α), of a sender, S's polynomial f(x), whilst keeping both α and f(x) private. This protocol has attracted a lot of attention recently, as it has wide ranging applications in the field of cryptography.
In this article we review some of these applications and, additionally, take an in-depth look at the special case of information theoretic OPE. Specifically, we provide a current and critical review of the existing information theoretic OPE protocols in the literature. We divide these protocols into two distinct cases (three-party and distributed OPE) allowing for the easy distinction and classification of future information theoretic OPE protocols. In addition to this work, we also develop several modifications and extensions to existing schemes, resulting in increased security, flexibility and efficiency. Lastly, we also identify a security flaw in a previously published OPE scheme.