RESEARCH 》 Power consumption of my Home Lab devices for research

AMD RYZEN 3 1200 - FreeNAS Storage array build

CPU: AMD Ryzen 3 1200 (4 cores/4 threads)

RAM: Corsair Vengeance 8GB DDR4 LPX 2400MHz C16 Kit

Motherboard: Gigabyte GA-A320M-HD2 AM44

Graphics/Display: Asus Geforce 210GT 1GB DDR3

PSU: Circle CPH698V12-400

Storage: WDC WD10JPVX-75JC3T0 - WD 1TB HDD

System BIOS 53 watts

Idle System (Linux Ubuntu OS) 52 watts

Casual browsing 53 watts

Youtube video playback 60 watts

Kernel compilation with 4-threads "make -j4" (99% load) 74 watts

Kernel compilation with 3-threads "make -j3" (80% load) 71 watts

My Intel Core i7-5820K - Desktop build

CPU: Intel Core i7-5820K (6 cores/12 threads)

RAM: Corsair PC2800 DDR4 14GB Kit

Motherboard: Gigabyte X99-UD4

Graphics/Display: Asus Geforce 210GT 1GB DDR3

PSU: Corsair VS450

CPU Liquid Cooling system: Cooler Master Nepton 240m

Storage: Transcend TS128GSSD370 128GB SSD

Idle System (Linux Ubuntu OS) 70 watts

System BIOS 90 watts

Linux kernel compilation (80%) load 150 watts

My Intel Celeron CPU 1037U Mini PC WAN Optimization Device

CPU: Intel Celeron CPU 1037U

RAM: DDR3 PC3L 4GB

Storage: Transcend TS128GSSD370 128GB SSD

Idle System (Linux Ubuntu OS) 18-20 watts

System BIOS 16.5 watts

Linux kernel compilation (95%) load 21-24 watts

My HP Envy 15-J111TX Laptop

CPU: Intel Corei7-4700MQ

RAM: DDR3 PC3L 12GB

Storage: WD Blue 250GB Scorpio HDD

Idle System (Linux Ubuntu OS) charging 44 watts

Idle System (Linux Ubuntu OS) charged 15 watts

Poweroff charging 28 watts

Poweroff charged 0.1 watts

Poweron charged suspend 0.75 watts

Linux kernel compilation (95%) load charging 90 watts

Linux kernel compilation (95%) load charged 69 watts

My Dell 15R 5537 Laptop

CPU: Intel Corei7-4500U

RAM: DDR3 PC3L 8GB

Storage: Seagate 320GB Momentus HDD

Idle System (Linux Ubuntu OS) charging 42 watts

Idle System (Linux Ubuntu OS) charged 10 watts

Poweroff charging 29 watts

Poweroff charged 0.1 watts

Poweron charged suspend 0.70 watts

Linux kernel compilation (95%) load charging 60 watts

Linux kernel compilation (95%) load charged 30 watts

My Acer Aspire 4810T Laptop

CPU: Intel Core Solo SU3500 1.4 GHz

RAM: DDR3 PC3 4GB

Storage: WD Blue 250GB Scorpio HDD

* No Battery, so no charging.

Idle System (Linux Manjaro OS) 16.23 watts

System BIOS 24.30 watts

Casual Browsing 22.27 watts

Youtube Playback 22.45 watts

Raspberry Pi2 Device

Powered via 2Amp USB power-supply

Raspbian OS

USB mouse and USB keyboard connected

Casual browsing 2.6 - 3 watts

Youtube video playback (25% load) 3 - 3.5 watts

Kernel compilation with 4-threads "make -j4" (99% load) 3.9 - 4 watts

Kernel compilation with 3-threads "make -j3" 3.67 - 3.75 watts

idle device with no keyboard and no mouse 2.08 - 2.1 watts

NETGEAR RN104 ReadyNAS

2x 2.5'' Laptop HDD drives

2x 3.5'' Desktop HDD drives

Single x-RAID volume with 4 HDD drives

Device off but plugged-in 0.58 watts

Idle device after booting 28 watts

File copy (write operation) 28.7 watts

RAID Volume scrub operation 29.5 watts

APC BX600C-IN UPS - APC Back-UPS 600 (UPS not powered-on but connected to live power socket)

Standby Charging 13.5 watts

Standby not-Charging 7.8 watts

APC BX600CI-IN UPS - APC Back-UPS 600 (UPS not powered-on but connected to live power socket)

Standby Charging 9.5 watts

Standby not-Charging 10-0.9 watts

BenQ LED Monitor 24'' GW2470HM

off plugged-in 0.00 watts

Dim 11.7 watts

LG LCD TV Monitor 23'' M237WA-PT

off plugged-in 0.8 watts

Dim 33 watts

Bright 45 watts

Samsung LCD Monitor 22'' 2243NWX

off plugged-in 0.7 watts

Dim 20 watts

Bright 33.5 watts

Power consumption of my Home Lab devices for research

Here is my power-consumption measurements of various devices deployed within my home lab. I measured via my kill-a-watt sort of power-meter which is fairly reliable and accurate. I checked its accuracy with various standard load such as Philips LED laps and other constant power-consuming devices to make sure that the power-meter is precise.

So far I maintained this data in my personal Google drive spreadsheet documents. But now I thought perhaps its good to share these numbers so that it is useful for various users to access their equipment such as:

decide UPS and battery backup ratings
off-grid solar power installations
choose new upgraded hardware which consumes less power and deliver better performance such as SSD over traditional HDD, new CPU, new Monitor, new laptop, servers, desktops and so on. And discard obsolete old hardware.
choosing the right PSU (power supply unit) for your desktop PC build

Before posting this article I shot a VLOG regarding the same and posted in my Youtube channel The Linux Channel. You can kindly watch the same:

Explore my lab's historical month wise power-usage trends: I started logging my entire lab monthly power-consumption readings. You can read the article HERE.

Off-Grid Solar Power System for Raspberry Pi: When you choose to use your Raspberry Pi device as your IoT based remote weather station or if you are building Linux kernel (like kernel compilation) within the same, you need a good uninterrupted power source (UPS). But if you are using it on site or in some research camping location you can choose to power your Raspberry Pi device with your custom off-grid solar power source. Kindly read my complete article about the same HERE.

Suggested Topics:

Generic Home Lab Research

💎 TOFFEE-MOCHA new bootable ISO: Download

💎 TOFFEE Data-Center Big picture and Overview: Download PDF

Tópicos recomendados:

TEST CASES :: TEST RESULTS :: TOFFEE-Mocha-1.0.32 asymmetric constant packet delay feature ↗
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IP Header Compression in WAN Links and TOFFEE-DataCenter WAN Optimization ↗
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Demo TOFFEE_DataCenter WAN Optimization VM (in VirtualBox) Test Setup ↗
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Demo TOFFEE_DataCenter WAN Optimization VM (in VirtualBox) Test Setup

My sample Wireshark packet capture files for research ↗
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I have a huge repository (or collection) of sample Wireshark packet capture files for reference. I use them extensively for research and development of TOFFEE as well to understand various protocol PDUs and protocol standards. I personally collected various test captures via Wireshark during my test and experimental research setup during the course of TOFFEE development. Say if you are a student and learning Networking and or say VoIP data and VoIP packets, you can analyse my VoIP sample Wireshark captures. Or in other case assume you are doing some quick research (or development) and want to refer few handful of VoIP packets then you can download and analyse my sample packet capture files.

Raspberry Pi as a Networking Device ↗
Saturday' 13-Mar-2021

Raspberry Pi is often used as a single board computer for applications such as IoT, hobby projects, DIY, education aid, research and prototyping device. But apart from these applications Raspberry Pi can be used for real-world applications such as in making a full-fledged networking devices. Raspberry Pi is a single board ARM based hardware which is why it is also classified as ARM based SoC. Since it is ARM based it is highly efficient, tiny form-factor and lower in power consumption with moderate computational power. This will allow it to work several hours on emergency battery backup power supply such as low-cost domestic UPS and or some renewable energy source, which is a prerequisite for a typical networking device.

TOFFEE-Mocha WAN Emulation software development - Update: 17-June-2016 ↗
Saturday' 13-Mar-2021

Now I supported and finished complete GUI support of these parameters so that you can configure, store, reboot and the same will restore upon reboot. Besides I complete the TOFFEE-Mocha Big-Picture page. The Big picture is an interface where you can find all the configuration (or settings) of the TOFFEE-Mocha. This is almost similar to CISCO device show all command but in graphical representation. Sometimes a network admin can also print the Big Picture page and paste it near to the device to refer its settings.

Assista no Youtube - [889//1] 280 WAN Optimization - Animated demo of Packet Optimization in TOFFEE-DataCenter ↗

Optimization of network data (WAN Optimization) at various levels ↗
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WAN Network data can be optimized at various levels depending upon the network applications, protocols, topology and use-cases. So the amount of data you can optimize will depend on the strategy you choose to optimize. Such as: Network Packet level optimization, Session level optimization, File level optimization, etc.

Network MTU research and optimization of WAN Links ↗
Saturday' 13-Mar-2021

Network MTU research and optimization of WAN Links

TOFFEE (and TOFFEE-DataCenter) deployment in Large Infrastructure and or ISP Networks ↗
Saturday' 13-Mar-2021

Large Infrastructure or ISP setup: In case if you are an ISP and interested in deploying a large customer WAN Optimized network or an add-on enhanced (WAN Optimized) network for select few customers, then you can deploy something as shown below. Although this case is not meant for hobby/DIY users. This is a feasible solution for high-end professional application and the same can be deployed.

TOFFEE-DataCenter WAN Optimization :: TOFFEE-DATACENTER-1.3.25-1-portable ↗
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Download TOFFEE-DATACENTER-1.3.25-1-portable.tar.xz via Google Drive share: platform independent (portable) source: TOFFEE-DATACENTER-1.2.2-1-portable.tar.xz * Alternatively download from SOURCEFORGE project site. * Here are the TOFFEE-DataCenter supported features. * To know more about the project kindly refer TOFFEE-Datacenter Documentation, News and Updates

Featured Educational Video:

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TOFFEE-DataCenter WAN Optimization software development - Update: 19-Aug-2016 ↗
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This is my next software development update of TOFFEE-DataCenter which I am working since past few weeks. I was very busy in implementing the core TOFFEE-DataCenter components along with prototyping, benchmarking, implementing and testing the same. However today is the first time ever I did a fresh new CLI interface for the upcoming new TOFFEE-DataCenter.

Why TOFFEE is forked from TrafficSqueezer ↗
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TrafficSqueezer is an open-source WAN Optimization project. TrafficSqueezer is mainly a research project which is started around mid-2006. It is initially started as a research (or prototype) code even before it is officially registered in Sourceforge.net. But this code is just primitive user-space raw socket modules. This is later refined and a pre-alpha version is created. Followed by which Alpha release. This prototype code is moved from user-space to Linux Kernel (Kernel Space) and then the journey begin in terms of making a serious WAN Optimization solution. Once the pre-beta and beta releases are complete the mainstream series is started.

TOFFEE-Butterscotch Bandwidth saver software development - Update: 17-Nov-2016 ↗
Saturday' 13-Mar-2021

Here is my second software development update of TOFFEE-Butterscotch. In the previous update (28-Oct-2016) I discussed about the Alerts, etc. Whereas in my first TOFFEE-Butterscotch news update I have introduced about TOFFEE-Butterscotch research, project specifications, use-cases, etc.

TOFFEE (and TOFFEE-DataCenter) deployment in SD-WAN Applications ↗
Saturday' 13-Mar-2021

Software-Defined Wide Area Networking (SD-WAN) is a new innovative way to provide optimal application performance by redefining branch office networking. Unlike traditional expensive private WAN connection technologies such as MPLS, etc., SD-WAN delivers increased network performance and cost reduction. SD-WAN solution decouple network software services from the underlying hardware via software abstraction.

The TOFFEE Project :: TOFFEE-Butterscotch :: Save and Optimize your Internet/WAN bandwidth ↗
Saturday' 13-Mar-2021

TOFFEE-Butterscotch is an open-source software which can be used to save and optimize your Internet/WAN bandwidth. Unlike TOFFEE (and TOFFEE-DataCenter) TOFFEE-Butterscotch is a non peer-to-peer (and asymmetric) network optimization solution. This makes TOFFEE-Butterscotch an ideal tool for all Home and SOHO users.

Research :: Optimization of network data (WAN Optimization) at various levels:
Network File level network data WAN Optimization

Learn Linux Systems Software and Kernel Programming:
Linux, Kernel, Networking and Systems-Software online classes [CDN]

Hardware Compression and Decompression Accelerator Cards:
TOFFEE Architecture with Compression and Decompression Accelerator Card [CDN]

TOFFEE-DataCenter on a Dell Server - Intel Xeon E5645 CPU:
TOFFEE-DataCenter screenshots on a Dual CPU - Intel(R) Xeon(R) CPU E5645 @ 2.40GHz - Dell Server

💎 TOFFEE-MOCHA new bootable ISO:	Download

💎 TOFFEE Data-Center Big picture and Overview:	Download PDF