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

Рекомендуемые темы:

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Saturday' 13-Mar-2021

TOFFEE-Butterscotch yet another variant of TOFFEE can be used to save and optimize your Home/SOHO 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.

Bulk Ping Tests - WAN Acceleration ↗
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TOFFEE-Mocha WAN Emulation software development - Update: 1-July-2016 ↗
Saturday' 13-Mar-2021

Today I got a feature request from Jonathan Withers. Jonathan is from a company called MultiWave Australia. He said he is able to get the TOFFEE-Mocha Raspberry Pi setup up and with that he is able to emulate geostationary satellite link. But he requested me is there a way to extend the constant packet delay from 40mS to 500mS. So as a part of his request I supported the same in the current ongoing development version of TOFFEE-Mocha.

TOFFEE-Mocha WAN Emulation software development - Update: 20-Oct-2016 ↗
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I was doing some specific tests in my TOFFEE and TOFFEE-DataCenter (WAN optimization) scenarios such as variable upload and download speeds. And I was also doing some experiments with speedtest.net and I did some of these tests with TOFFEE-Mocha. I realized there is a case that I can introduce asymmetric constant delays so that you can get different download speed and a different upload speed. And in some cases much faster download speeds and relatively slower upload speeds.

TOFFEE deployment topology guide ↗
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Assume you have two sites (such as Site-A and Site-B) connected via slow/critical WAN link as shown below. You can optimize this link by saving the bandwidth as well possibly improve the speed. However, the WAN speed can be optimized only if the WAN link speeds are below that of the processing latency of your TOFFEE installed hardware. Assume your WAN link is 12Mbps, and assume the maximum WAN optimization speed/capacity of Raspberry Pi is 20Mbps, then your link will get speed optimization too. And in another case, assume your WAN link is 50Mbps, then using the Raspberry Pi as WAN Optimization device will actually increase the latency (i.e slows the WAN link). But in all the cases the bandwidth savings should be the same irrespective of the WAN link speed. In other words, if you want to cut down the WAN link costs via this WAN Optimization set up, you can always get it since it reduces the overall bandwidth in almost all the cases (including encrypted and pre-compressed data).

TOFFEE Benchmarks :: TOFFEE-1.1.28 ↗
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Here is the TOFFEE WAN Optimization benchmarks of the TOFFEE version: TOFFEE-1.1.28. This is the current TOFFEE development version till date (2-Jul-2016). This is a HPC TOFFEE variant meant for high-end custom build servers and high-end desktops (i.e High Performance Computing a.k.a HPC). TOFFEE built this way often needs customized kernel compilation and build such as processor specific and hardware specific tune-ups since it is highly CPU intensive (if not offloaded via Hardware Accelerator Cards).

My Lab Battery Purchase and Service logs for Research ↗
Saturday' 13-Mar-2021

Here is a complete log of my lab battery purchase, service record which I maintain in Google drive. These I use for my home (or my family generic use) as well as a part of my home lab. I maintain a detailed log this way to monitor the failure rate of these batteries. This will allow me to select a specific brand/model which has higher success rate and to monitor any premature failure/expiry. The service log helps me to monitor and schedule the next service routine so that I can maintain these batteries in tip-top condition.

TCP Tune-up and Performance Analysis Graphs - Congestion Control - Research - Dos and Don'ts ↗
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Replacing in Lab Intel Core i7 5820K Desktop PC with Intel Celeron 1037U Mini-PC ↗
Saturday' 13-Mar-2021

As a research experiment I replaced my Intel Core i7 5820K desktop PC with my Intel Celeron 1037U Mini-PC as my everyday desktop system. This is an attempt to reduce my overall monthly power consumption. As well an attempt to do feasibility tests and research to know how far Mini PC will dominate the market in future and to study the real potential of Mini PCs.

Featured Educational Video:

Watch on Youtube - [8613//1] x254 Kernel Init Code without Kernel Module - Kernel Programming Tip #linode ↗

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.

TOFFEE-Mocha WAN emulator Lab deployment and topology guide ↗
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Advantages of CDN - Content Delivery Networks or Content Distribution Networks ↗
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Network Latency and Bandwidth Assessment - for Network Admins and Infrastructure Architects ↗
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Watch on Youtube - [889//1] 280 WAN Optimization - Animated demo of Packet Optimization in TOFFEE-DataCenter ↗

TOFFEE-DataCenter :: Features Supported ↗
Saturday' 13-Mar-2021

Here is a list of TOFFEE-DataCenter features supported. TOFFEE-DataCenter currently supports some of the important features such as loss-less network data compression, Packet Deduplication (protocols/applications supported), Application Acceleration, TCP Acceleration, dynamic MTU optimization, data packaging, hardware offload support, etc.

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

Hardware Compression and Decompression Accelerator Cards:
TOFFEE Architecture with Compression and Decompression Accelerator Card

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