silky smith

1632207189

How Do I Connect HP DeskJet 3700 to Wireless Network?

Get online HP DeskJet 3700 all in one series wireless setup without pay extra cost, Visit the Printer customer service website. We will provide you the best assistance and troubleshooting info including software, drivers, and manuals for your HP DeskJet 3700 All-in-One Printer series.

Read About:- How To Complete HP Laserjet P1102W Wireless Setup Mac

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Buddha Community

Mia Logan

1604314949

Printer Setup and Install || Easy Connect to Wi-Fi

This article brings to light the 123.hp.com/setup 4155 instructions and methods available to connect HP printer with MAC or Windows 10 computer at ease.

  1. To get the convenient and on-time printout, copy and scan it is essential to complete the HP Deskjet Plus 4155 setup process without hassles.
  2. The next essential step is to download and install HP Deskjet Plus 4155 driver in order to get the best print results.
  3. For more driver details, go to 123.hp.com/dj4155 and make things simpler.
  4. Finally, all that you have to do is **HP Deskjet plus 4155 wireless set**up, add your printer device and connect it with reliable internet to enjoy taking print outs from anywhere and at any time.

#123.hp.com/dj4155 #123.hp.com/setup 4155 #hp deskjet plus 4155 setup #hp deskjet plus 4155 driver #hp deskjet plus 4155 wireless setup

PostgreSQL Connection Pooling: Part 4 – PgBouncer vs. Pgpool-II

In our previous posts in this series, we spoke at length about using PgBouncer  and Pgpool-II , the connection pool architecture and pros and cons of leveraging one for your PostgreSQL deployment. In our final post, we will put them head-to-head in a detailed feature comparison and compare the results of PgBouncer vs. Pgpool-II performance for your PostgreSQL hosting !

The bottom line – Pgpool-II is a great tool if you need load-balancing and high availability. Connection pooling is almost a bonus you get alongside. PgBouncer does only one thing, but does it really well. If the objective is to limit the number of connections and reduce resource consumption, PgBouncer wins hands down.

It is also perfectly fine to use both PgBouncer and Pgpool-II in a chain – you can have a PgBouncer to provide connection pooling, which talks to a Pgpool-II instance that provides high availability and load balancing. This gives you the best of both worlds!

Using PgBouncer with Pgpool-II - Connection Pooling Diagram

PostgreSQL Connection Pooling: Part 4 – PgBouncer vs. Pgpool-II

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Performance Testing

While PgBouncer may seem to be the better option in theory, theory can often be misleading. So, we pitted the two connection poolers head-to-head, using the standard pgbench tool, to see which one provides better transactions per second throughput through a benchmark test. For good measure, we ran the same tests without a connection pooler too.

Testing Conditions

All of the PostgreSQL benchmark tests were run under the following conditions:

  1. Initialized pgbench using a scale factor of 100.
  2. Disabled auto-vacuuming on the PostgreSQL instance to prevent interference.
  3. No other workload was working at the time.
  4. Used the default pgbench script to run the tests.
  5. Used default settings for both PgBouncer and Pgpool-II, except max_children*. All PostgreSQL limits were also set to their defaults.
  6. All tests ran as a single thread, on a single-CPU, 2-core machine, for a duration of 5 minutes.
  7. Forced pgbench to create a new connection for each transaction using the -C option. This emulates modern web application workloads and is the whole reason to use a pooler!

We ran each iteration for 5 minutes to ensure any noise averaged out. Here is how the middleware was installed:

  • For PgBouncer, we installed it on the same box as the PostgreSQL server(s). This is the configuration we use in our managed PostgreSQL clusters. Since PgBouncer is a very light-weight process, installing it on the box has no impact on overall performance.
  • For Pgpool-II, we tested both when the Pgpool-II instance was installed on the same machine as PostgreSQL (on box column), and when it was installed on a different machine (off box column). As expected, the performance is much better when Pgpool-II is off the box as it doesn’t have to compete with the PostgreSQL server for resources.

Throughput Benchmark

Here are the transactions per second (TPS) results for each scenario across a range of number of clients:

#database #developer #performance #postgresql #connection control #connection pooler #connection pooler performance #connection queue #high availability #load balancing #number of connections #performance testing #pgbench #pgbouncer #pgbouncer and pgpool-ii #pgbouncer vs pgpool #pgpool-ii #pooling modes #postgresql connection pooling #postgresql limits #resource consumption #throughput benchmark #transactions per second #without pooling

123hpcom setup

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123.hp.com/dj2600 | HP DeskJet 2600 Setup | HP DeskJet 2600 Driver

Carry out the HP DeskJet 2600 wireless setup by loading the papers to the tray extender. With the help of the width slider, adjust the papers you are loading and ensure that the paper is reaching the bottom of the input tray. Once the paper is inserted accurately, you can receive a printed automatic printer alignment paper. Just visit our site 123.hp.com/setup for more information related to HP Deskjet 2600 Wireless setup, Driver Download and Troubleshooting via 123.hp.com/setup 2600 or call our support team number +1-850-761-8950

#123.hp.com/setup 2600 #hp deskjet 2600 wireless setup

Marlon  Boyle

Marlon Boyle

1594312560

Autonomous Driving Network (ADN) On Its Way

Talking about inspiration in the networking industry, nothing more than Autonomous Driving Network (ADN). You may hear about this and wondering what this is about, and does it have anything to do with autonomous driving vehicles? Your guess is right; the ADN concept is derived from or inspired by the rapid development of the autonomous driving car in recent years.

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Driverless Car of the Future, the advertisement for “America’s Electric Light and Power Companies,” Saturday Evening Post, the 1950s.

The vision of autonomous driving has been around for more than 70 years. But engineers continuously make attempts to achieve the idea without too much success. The concept stayed as a fiction for a long time. In 2004, the US Defense Advanced Research Projects Administration (DARPA) organized the Grand Challenge for autonomous vehicles for teams to compete for the grand prize of $1 million. I remembered watching TV and saw those competing vehicles, behaved like driven by drunk man, had a really tough time to drive by itself. I thought that autonomous driving vision would still have a long way to go. To my surprise, the next year, 2005, Stanford University’s vehicles autonomously drove 131 miles in California’s Mojave desert without a scratch and took the $1 million Grand Challenge prize. How was that possible? Later I learned that the secret ingredient to make this possible was using the latest ML (Machine Learning) enabled AI (Artificial Intelligent ) technology.

Since then, AI technologies advanced rapidly and been implemented in all verticals. Around the 2016 time frame, the concept of Autonomous Driving Network started to emerge by combining AI and network to achieve network operational autonomy. The automation concept is nothing new in the networking industry; network operations are continually being automated here and there. But this time, ADN is beyond automating mundane tasks; it reaches a whole new level. With the help of AI technologies and other critical ingredients advancement like SDN (Software Defined Network), autonomous networking has a great chance from a vision to future reality.

In this article, we will examine some critical components of the ADN, current landscape, and factors that are important for ADN to be a success.

The Vision

At the current stage, there are different terminologies to describe ADN vision by various organizations.
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Even though slightly different terminologies, the industry is moving towards some common terms and consensus called autonomous networks, e.g. TMF, ETSI, ITU-T, GSMA. The core vision includes business and network aspects. The autonomous network delivers the “hyper-loop” from business requirements all the way to network and device layers.

On the network layer, it contains the below critical aspects:

  • Intent-Driven: Understand the operator’s business intent and automatically translate it into necessary network operations. The operation can be a one-time operation like disconnect a connection service or continuous operations like maintaining a specified SLA (Service Level Agreement) at the all-time.
  • **Self-Discover: **Automatically discover hardware/software changes in the network and populate the changes to the necessary subsystems to maintain always-sync state.
  • **Self-Config/Self-Organize: **Whenever network changes happen, automatically configure corresponding hardware/software parameters such that the network is at the pre-defined target states.
  • **Self-Monitor: **Constantly monitor networks/services operation states and health conditions automatically.
  • Auto-Detect: Detect network faults, abnormalities, and intrusions automatically.
  • **Self-Diagnose: **Automatically conduct an inference process to figure out the root causes of issues.
  • **Self-Healing: **Automatically take necessary actions to address issues and bring the networks/services back to the desired state.
  • **Self-Report: **Automatically communicate with its environment and exchange necessary information.
  • Automated common operational scenarios: Automatically perform operations like network planning, customer and service onboarding, network change management.

On top of those, these capabilities need to be across multiple services, multiple domains, and the entire lifecycle(TMF, 2019).

No doubt, this is the most ambitious goal that the networking industry has ever aimed at. It has been described as the “end-state” and“ultimate goal” of networking evolution. This is not just a vision on PPT, the networking industry already on the move toward the goal.

David Wang, Huawei’s Executive Director of the Board and President of Products & Solutions, said in his 2018 Ultra-Broadband Forum(UBBF) keynote speech. (David W. 2018):

“In a fully connected and intelligent era, autonomous driving is becoming a reality. Industries like automotive, aerospace, and manufacturing are modernizing and renewing themselves by introducing autonomous technologies. However, the telecom sector is facing a major structural problem: Networks are growing year by year, but OPEX is growing faster than revenue. What’s more, it takes 100 times more effort for telecom operators to maintain their networks than OTT players. Therefore, it’s imperative that telecom operators build autonomous driving networks.”

Juniper CEO Rami Rahim said in his keynote at the company’s virtual AI event: (CRN, 2020)

“The goal now is a self-driving network. The call to action is to embrace the change. We can all benefit from putting more time into higher-layer activities, like keeping distributors out of the business. The future, I truly believe, is about getting the network out of the way. It is time for the infrastructure to take a back seat to the self-driving network.”

Is This Vision Achievable?

If you asked me this question 15 years ago, my answer would be “no chance” as I could not imagine an autonomous driving vehicle was possible then. But now, the vision is not far-fetch anymore not only because of ML/AI technology rapid advancement but other key building blocks are made significant progress, just name a few key building blocks:

  • software-defined networking (SDN) control
  • industry-standard models and open APIs
  • Real-time analytics/telemetry
  • big data processing
  • cross-domain orchestration
  • programmable infrastructure
  • cloud-native virtualized network functions (VNF)
  • DevOps agile development process
  • everything-as-service design paradigm
  • intelligent process automation
  • edge computing
  • cloud infrastructure
  • programing paradigm suitable for building an autonomous system . i.e., teleo-reactive programs, which is a set of reactive rules that continuously sense the environment and trigger actions whose continuous execution eventually leads the system to satisfy a goal. (Nils Nilsson, 1996)
  • open-source solutions

#network-automation #autonomous-network #ai-in-network #self-driving-network #neural-networks

Agnes  Sauer

Agnes Sauer

1597629780

30 Minutes or Less: Build a Wireless Sensor Network Using NodeMCU

“I want to have sensors everywhere!” In this article we’ll show you how to create a wireless sensor node using an inexpensive NodeMCU module.

NodeMCU is a tiny, low-cost WiFi-enabled microcontroller that supports direct connectivity to sensors via SPI, I2C, ADC, GPIO, etc. Programming of the module is made easier with support for the Arduino Integrated Development Environment (IDE). You can build up as many wireless sensor nodes as needed to create your wireless sensor network.

The wireless sensor nodes will send data to the Universal Sensor Hub that we showed you how to build in our prior article. We use the same sensor from the universal hub article, but feel free to experiment with other sensors — gas sensors, door switches, noise detectors, water level, soil moisture level, etc. The Universal Sensor Hub uses Machinechat JEDI One software running on the Raspberry Pi. JEDI One enables you to collect data from all the sensors and display the data on a dashboard accessible from any browser on the network.

Here is an example of a JEDI One system view of multiple sensors:

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We will show you in just 4 steps, how to get a wireless sensor network started. A few items you will need:

  1. NodeMCU modules — less than $5 each
  2. BME280 Environmental sensor module w/ wires — $12 This one is used in example below
  3. Micro USB cable — $5
  4. Arduino IDE — Free
  5. Universal Sensor Hub with Machinechat JEDI One installed

#networking #wireless #nodemcu #diy #raspberry-pi #neural networks