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

This paper surveys and demonstrates the power of non-strict evaluation in applications executed on distributed architectures. We present the design, implementation, and experimental evaluation of single assignment, incomplete data structures in a distributed memory architecture and Abstract Network Machine (ANM). Incremental Structures (IS), Incremental Structure Software Cache (ISSC), and Dynamic Incremental Structures (DIS) provide nonstrict data access and fully asynchronous operations that make them highly suited for the exploitation of fine-grain parallelism in distributed memory systems. We focus on split-phase memory operations and non-strict information processing under a distributed address space to improve the overall system performance. A novel technique of optimization at the communication level is proposed and described. We use partial evaluation of local and remote memory accesses not only to remove much of the excess overhead of message passing, but also to reduce the number of messages when some information about the input or part of the input is known. We show that split-phase transactions of IS, together with the ability of deferring reads, allow partial evaluation of distributed programs without losing determinacy. Our experimental evaluation indicates that commodity PC clusters with both IS and a caching mechanism, ISSC, are more robust. The system can deliver speedup for both regular and irregular applications. We also show that partial evaluation of memory accesses decreases the traffic in the interconnection network and improves the performance of MPI IS and MPI ISSC applications.

https://usuario.cicese.mx/%7Echernykh/papers/IJPP_nonstrict.pdf

Non-strict execution in parallel and distributed computing

Many computing systems today are heterogeneous in that they consist of a mix of different types of processing units (e.g., CPUs, GPUs). Each of these processing units has different performance capabilities and energy consumption characteristics. Job mapping and scheduling play a crucial role in such systems as they strongly affect the overall system performance, energy consumption, peak power and peak temperature. Allocating resources (e.g., core scaling, threads allocation) is another challenge since different sets of resources exhibit different behavior in terms of performance and energy consumption. Many studies have been conducted on job scheduling with an eye on performance improvement. However, few of them consider both performance and energy. We thus propose our approach we call Performance, Energy and Thermal aware Scheduler (PETS) to combine job mapping, core scaling, and threads allocation into one scheduler. We apply an evolutionary algorithm (a Genetic Algorithm — GA) to find an efficient job schedule in terms of both execution time and energy consumption, under peak power and peak temperature constraints. Compared to a performance-based GA and other schedulers, on average, our PETS scheduler can achieve up to a 4.7x of speedup and an energy saving of up to 195%.

PETS: Performance, energy and thermal aware scheduler for job mapping with resource allocation in heterogeneous systems

When you need to enable deep learning on low-cost embedded SoCs, is it better to port an existing deep learning framework or should you build one from scratch? In this paper, we share our practical experiences of building an embedded inference engine using ARM Compute Library (ACL). The results show that, contradictory to conventional wisdoms, for simple models, it takes much less development time to build an inference engine from scratch compared to porting existing frameworks. In addition, by utilizing ACL, we managed to build an inference engine that outperforms TensorFlow by 25%. Our conclusion is that, on embedded devices, we most likely will use very simple deep learning models for inference, and with well-developed building blocks such as ACL, it may be better in both performance and development time to build the engine from scratch.

Enabling Embedded Inference Engine with ARM Compute Library: A Case Study.

In this paper, we propose a heuristic for code partitioning for distributed memory multiprocessors (DMMs). Our method is data-flow based where all levels of parallelism can potentially be exploited. Given a weighted directed acyclic graph (DAG) representation of the program, our partitioning algorithm automatically determines the granularity of parallelism by partitioning the graph into tasks to be scheduled on the DMM. The granularity of parallelism depends only on the program to be executed and on the target machine parameters. The output of our algorithm is passed on as input to the scheduling phase. Unlike the scheduling problem as defined by Yang [A. Gerasoulis, T. Yang, IEEE Transactions on Parallel and Distributed Systems 4 (6) (1993) 686–701; T. Yang, Ph.D. Thesis, Rutgers University, New Brunswick, NJ, May 1993; T. Yang, A. Gerasoulis, IEEE Transactions on Parallel and Distributed Systems 5 (9) (1994) 951–967], the method presented in this paper uses task merging rather than task clustering . Finding an optimal solution to this problem is NP-complete. Due to the high cost of graph algorithms, it is nearly impossible to come up with close to optimal solutions that do not have very high cost (higher order polynomial). Therefore, our goal is to find a heuristic that gives good performance, and that has relatively low cost. Given a DAG with E edges and N nodes, the time complexity of our partitioning algorithm is O( E · N 3 ) in the worst case. For some cases, the average time complexity of the algorithm is O( N ( E + N )).

An efficient heuristic for code partitioning

We advocate creating a cross-disciplinary program that gives students a sufficient technical background and includes a capstone project to create a real-world autonomous machine from scratch by applying the knowledge learned in classes.

Jean-Luc Gaudiot

Papers

Non-strict execution in parallel and distributed computing

PETS: Performance, energy and thermal aware scheduler for job mapping with resource allocation in heterogeneous systems

Enabling Embedded Inference Engine with ARM Compute Library: A Case Study.

An efficient heuristic for code partitioning

Engineering Education in the Age of Autonomous Machines