The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

We review a model of computation used in industrial practice in signal processing software environments and experimentally and other contexts. We give this model the name "dataflow process networks," and study its formal properties as well as its utility as a basis for programming language design. Variants of this model are used in commercial visual programming systems such as SPW from the Alta Group of Cadence (formerly Comdisco Systems), COSSAP from Synopsys (formerly Cadis), the DSP Station from Mentor Graphics, and Hypersignal from Hyperception. They are also used in research software such as Khoros from the University of New Mexico and Ptolemy from the University of California at Berkeley, among many others. Dataflow process networks are shown to be a special case of Kahn process networks, a model of computation where a number of concurrent processes communicate through unidirectional FIFO channels, where writes to the channel are nonblocking, and reads are blocking. In dataflow process networks, each process consists of repeated "firings" of a dataflow "actor." An actor defines a (often functional) quantum of computation. By dividing processes into actor firings, the considerable overhead of context switching incurred in most implementations of Kahn process networks is avoided. We relate dataflow process networks to other dataflow models, including those used in dataflow machines, such as static dataflow and the tagged-token model. We also relate dataflow process networks to functional languages such as Haskell, and show that modern language concepts such as higher-order functions and polymorphism can be used effectively in dataflow process networks. A number of programming examples using a visual syntax are given. >

/pdf/dataflow-process-networks-3mdwy0uhx2.pdf

Dataflow process networks

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper, we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

/pdf/deep-learning-in-mobile-and-wireless-networking-a-survey-ct8cy2kuqi.pdf

Deep Learning in Mobile and Wireless Networking: A Survey

Objects. The ~ b s t rac t ~ b j ect forms the fundamental base class for the entire design and all other classes are derived from this base class. The Abstract Object class defines and characterizes all the essential properties every class in this 404 OBJECT-ORIENTED SIMULATION

Handbook of Simulation

Network lifetime has become the key characteristic for evaluating sensor networks in an application-specific way. Especially the availability of nodes, the sensor coverage, and the connectivity have been included in discussions on network lifetime. Even quality of service measures can be reduced to lifetime considerations. A great number of algorithms and methods were proposed to increase the lifetime of a sensor network—while their evaluations were always based on a particular definition of network lifetime. Motivated by the great differences in existing definitions of sensor network lifetime that are used in relevant publications, we reviewed the state of the art in lifetime definitions, their differences, advantages, and limitations. This survey was the starting point for our work towards a generic definition of sensor network lifetime for use in analytic evaluations as well as in simulation models—focusing on a formal and concise definition of accumulated network lifetime and total network lifetime. Our definition incorporates the components of existing lifetime definitions, and introduces some additional measures. One new concept is the ability to express the service disruption tolerance of a network. Another new concept is the notion of time-integration: in many cases, it is sufficient if a requirement is fulfilled over a certain period of time, instead of at every point in time. In addition, we combine coverage and connectivity to form a single requirement called connected coverage. We show that connected coverage is different from requiring noncombined coverage and connectivity. Finally, our definition also supports the concept of graceful degradation by providing means of estimating the degree of compliance with the application requirements. We demonstrate the applicability of our definition based on the surveyed lifetime definitions as well as using some example scenarios to explain the various aspects influencing sensor network lifetime.

/pdf/on-the-lifetime-of-wireless-sensor-networks-59hdyqoqi3.pdf

On the lifetime of wireless sensor networks

Cybersecurity and High-Performance Computing Environments

Current shared-memory systems can feature tens of processing elements. The old assumption that coarse-grain synchronization is enough in a shared-memory system thus becomes invalid. To efficiently take advantage of such systems, we propose to use fine grain synchronization, with event-driven multithreading. To illustrate our point, we study a naive 5-point 2D stencil kernel. We provide several synchronization variants using our fine-grain multithreading environment, and compare it to a naive coarse-grain implementation using OpenMP. We conducted experiments on three different many-core compute nodes, with speedups ranging from 1.2\(\times \) to 1.75\(\times \).

The Importance of Efficient Fine-Grain Synchronization for Many-Core Systems

While High Performance Computing systems are increasingly based on heterogeneous cores, their effectiveness depends on how well the scheduler can allocate workloads onto appropriate computing devices and how communication and computation can be overlapped. With different types of resources integrated into one system, the complexity of the scheduler correspondingly increases. Moreover, for applications with varying problem sizes on different heterogeneous resources, the optimal scheduling approach may vary accordingly. We thus present PDAWL, an event-driven profile-based Iterative Dynamic Adaptive Work-Load balance scheduling approach to dynamically and adaptively adjust workload to efficiently utilize heterogeneous resources. It combines online scheduling (DAWL), which can adaptively adjust workload based on available real time heterogeneous resources, with an offline machine learning (profile-based estimation model) which can build a device-specific communication computation estimation model. Our scheduling approach is tested on control-regular applications, Stencil kernel (based on a Jacobi Algorithm) and Sparse Matrix-Vector Multiplication (SpMV) in an event-driven runtime system. Experimental results show that PDAWL is either on-par or far outperforms whichever yields the best results (CPU or GPU).

PDAWL: Profile-Based Iterative Dynamic Adaptive WorkLoad Balance on Heterogeneous Architectures

A network performance metric is introduced and evaluated on candidate networks for the Hughes data-flow machine, a programmable architecture constructed of multiple homogeneous processing elements for signal and image processing applications. The metric combines a traffic distribution pattern with network interconnections to statically measure the time required to flush the network of a given traffic volume. Candidate networks of the Hughes data-flow machine are described and analyzed using our performance metric to observe how the static performance of networks change with a range of traffic patterns, and with the size of the network. The candidate networks are compared based on the results of these static measurements. The HDFM allocator is then described and used to extract traffic patterns from actual applications. The performance metric is evaluated again as the allocator varies the ratio of parallelism vs communication load. The results enable a network comparison of the allocation complexity required to achieve equivalent performance levels.

Network design and allocation considerations in the Hughes data-flow machine☆

As processor design has been transiting from homogeneous multicore processor to heterogeneous multicore processor, traditional Amdahl’s law cannot meet the new challenges for asymmetric multicore system. In order to further investigate the impact factors related to the Overhead of Data Preparation (ODP) for Asymmetric multicore systems, we evaluate an asymmetric multicore system built with CPU-GPU by measuring the overheads of memory transfer, computing kernel, cache missing and synchronization. This paper demonstrates that decreasing the overhead of data preparation is a promising approach to improve the whole performance of heterogeneous system.

Jean-Luc Gaudiot

Papers

Cybersecurity and High-Performance Computing Environments

The Importance of Efficient Fine-Grain Synchronization for Many-Core Systems

PDAWL: Profile-Based Iterative Dynamic Adaptive WorkLoad Balance on Heterogeneous Architectures

Network design and allocation considerations in the Hughes data-flow machine☆

Reevaluating the overhead of data preparation for asymmetric multicore system on graphics processing