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Practical Packet Analysis (3rd Edition) - An amazing book about analyzing network packets using Wireshark.Red Hat Enterprise Linux 7 Networking Guide - The official Red Hat's networking guide for Red Hat Enterprise Linux.

#Download pdf basics of networking book free#

Beej's Guide to Network Programming - A free digital book about socket programming by Brian "Beej" Hall.

Network Security Essentials: Applications and Standards (6th Edition) - A practical survey of network security applications and standards.

Internetworking with TCP/IP Volume One (6th Edition) - A rich introduction book to the TCP/IP protocols and Internetworking.

Computer Networking: A Top-Down Approach (7th Edition) - Excellent book for beginners (and advanced people) in networking.

#Download pdf basics of networking book manual#

MikroTik WinBox Manual - The official manual for MikroTik's WinBox software.Wireshark Tutorial for Beginners - TheNewBoston Wireshark Tutorial for Beginners.

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Networking - A series of YouTube tutorials about networking by Eli the Computer Guy.

The Complete Networking Fundamentals Course - The most popular Networking Essentials course on Udemy.

GNS3 Academy - A various number of courses related to networking with GNS3 software.

ipSpace - Contains excellent courses and webinars on various network topics, for both beginners and advanced people.

CBT Nuggets - An excellent place to start with the courses on various topics (Cisco, Juniper, CompTIA, etc.).

Cisco Networking Academy Courses - A various networking courses (Essentials, CCNA, CCNP, etc.).

Simple Network Management Protocol (SNMP).

Hypertext Transfer Protocol Version 2 (HTTP/2).

Dynamic Host Configuration Protocol (DHCP).

Note: Links without description are official RFCs from the Internet Engineering Task Force (IETF). An awesome collection of courses, books, tutorials, software, and other resources about networking. Based on these parameters the programmer can apply well-directed program changes to eliminate or alleviate every specific performance drawback as indicated by. These parameters can be selectively obtained for statements, loops, procedures, and the entire program furthermore, their effect with respect to individual processors can be examined. This includes work distribution, number of data transfers, amount of data transferred, transfer times, network contention, and number of cache misses. It computes at compile time a set of parallel program parameters that characterize the behavior of the parallel program. In this paper, we present P 3 T, which is a static and automatic performance estimator for data parallel programs.

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Most of the current compilers do not provide any support for estimating performance impacts of code changes that they apply. The area of parallelizing compilers for distributed memory multicomputers has seen considerable research activity during the last few years. We find that the cluster-specific efficiency rankings provide more meaningful benchmarks than the conventional approach of using the intensity weights obtained as a side-product of efficiency analysis.

StoNED can be seen as a stochastic extension of DEA that takes the noise term explicitly into account. We present a real world application to the regulation of electricity distribution networks in Finland, where the regulator uses the semi-nonparametric StoNED method (stochastic non-parametric envelopment of data). The inputs of clustering and efficiency analysis are user-specified and can be multi-dimensional. The proposed approach is flexible to combine any clustering method with any frontier estimation technique. The cluster-specific efficiency rankings enable the management to identify not only the most efficient benchmark, but also other peers that operate more efficiently within the same cluster. We then rank DMUs in the descending order of efficiency within each cluster. More specifically, we propose to apply some clustering methods to identify groups of DMUs that are similar in terms of their input–output profiles or other observed characteristics. In this paper we propose a new approach to benchmarking that combines the frontier estimation techniques with clustering methods. Further, DEA is sensitive to stochastic noise, which can affect the benchmarking exercise. Moreover, the DEA benchmarks may operate in a more favorable environment than the evaluated DMU.

As a result, the benchmark units may differ from the evaluated DMU in terms of their input–output profiles and the scale size. The benchmarks produced by DEA are obtained as a side-product of computing efficiency scores. Data envelopment analysis (DEA) is widely used as a benchmarking tool for improving productive performance of decision making units (DMUs).