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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 BIOS53 watts
Idle System (Linux Ubuntu OS)52 watts
Casual browsing53 watts
Youtube video playback60 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 BIOS90 watts
Linux kernel compilation (80%) load150 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 BIOS16.5 watts
Linux kernel compilation (95%) load21-24 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) charging44 watts
Idle System (Linux Ubuntu OS) charged15 watts
Poweroff charging28 watts
Poweroff charged0.1 watts
Poweron charged suspend0.75 watts
Linux kernel compilation (95%) load charging90 watts
Linux kernel compilation (95%) load charged69 watts

My Dell 15R 5537 Laptop
  • CPU: Intel Corei7-4500U
  • RAM: DDR3 PC3L 8GB
  • Storage: Seagate 320GB Momentus HDD
Idle System (Linux Ubuntu OS) charging42 watts
Idle System (Linux Ubuntu OS) charged10 watts
Poweroff charging29 watts
Poweroff charged0.1 watts
Poweron charged suspend0.70 watts
Linux kernel compilation (95%) load charging60 watts
Linux kernel compilation (95%) load charged30 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 BIOS24.30 watts
Casual Browsing22.27 watts
Youtube Playback22.45 watts

Raspberry Pi2 Device
  • Powered via 2Amp USB power-supply
  • Raspbian OS
  • USB mouse and USB keyboard connected
Casual browsing2.6 - 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 mouse2.08 - 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-in0.58 watts
Idle device after booting28 watts
File copy (write operation)28.7 watts
RAID Volume scrub operation29.5 watts

APC BX600C-IN UPS - APC Back-UPS 600(UPS not powered-on but connected to live power socket)
Standby Charging13.5 watts
Standby not-Charging7.8 watts

APC BX600CI-IN UPS - APC Back-UPS 600(UPS not powered-on but connected to live power socket)
Standby Charging9.5 watts
Standby not-Charging10-0.9 watts

BenQ LED Monitor 24'' GW2470HM
off plugged-in0.00 watts
Dim11.7 watts

LG LCD TV Monitor 23'' M237WA-PT
off plugged-in0.8 watts
Dim33 watts
Bright45 watts

Samsung LCD Monitor 22'' 2243NWX
off plugged-in0.7 watts
Dim20 watts
Bright33.5 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.
Off-Grid Solar Power System for Raspberry Pi



Suggested Topics:


Generic Home Lab Research

💎 TOFFEE-MOCHA new bootable ISO: Download
💎 TOFFEE Data-Center Big picture and Overview: Download PDF


Recommended Topics:

Multi-dimensional (Multi-universe) Internet Technology - A Proposal ↗
Saturday' 13-Mar-2021
Currently what we have is a single homogeneous (sort of) WWW Internet. Which we can consider as a single-dimensional network. What I propose is that we can create complete independent multiple Internets with each Internet having its own IP-address space, Domain namespace and an authority to manage Domain names. And these networks/Internets can be entirely IPv4 only based or IPv6 only based.

TOFFEE Documentation :: TOFFEE-1.1.24-3-rpi2 ↗
Saturday' 13-Mar-2021
Here is my VLOG Youtube video of the same which includes details about version release notes, future road-map and so on. The TOFFEE release is highly optimized and customized for hardware platforms such as x86-64 based Intel NUC and other Intel mobile computing platforms such as laptops and so on. This version (or release) is not suited and so not recommended to be used for high-end desktop and server hardware platform.

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.

TOFFEE (and TOFFEE-DataCenter) optimized Wireless Mesh-Networks - B.A.T.M.A.N [open-mesh.org (Open Mesh)] ↗
Saturday' 13-Mar-2021
TOFFEE/TOFFEE-DataCenter can be used to optimize Ad-Hoc Mobile Wireless Mesh-Networks. To learn more about the same here are some references: B.A.T.M.A.N. - https://en.wikipedia.org/wiki/B.A.T.M.A.N. Mobile ad hoc network (MANET) - https://en.wikipedia.org/wiki/Mobile_ad_hoc_network Wireless ad hoc network (WANET) - https://en.wikipedia.org/wiki/Wireless_ad_hoc_network open-mesh.org (Open Mesh) Wiki - https://www.open-mesh.org/projects/open-mesh/wiki

TOFFEE-Mocha WAN Emulation software development - Update: 16-June-2016 ↗
Saturday' 13-Mar-2021
I started TOFFEE-Mocha WAN Emulation software development on 1-June-2016. I took the existing TOFFEE components as a base. Although the TOFFEE-Mocha is entirely an independent fresh Open-Source WAN Emulation solution. Ever since I am in the process of defining and inventing features. So far I come up with the most important feature which is expected to be present in any WAN Emulation software is the packet delay option.

TOFFEE-Mocha - WAN Emulator :: TOFFEE-MOCHA-2.0.3-0-10-nov-2018-x86-64.iso ↗
Saturday' 13-Mar-2021
Download TOFFEE-MOCHA-2.0.3-0-10-nov-2018-x86-64.iso via Google Drive share: Live bootable x86-64 Debian Stretch 9.5 with light-weight LXDE UI ISO (includes source-code): TOFFEE-MOCHA-2.0.3-0-10-nov-2018-x86-64.iso You can find the source tar-ball in the /root folder. To know more about the project kindly refer TOFFEE- Mocha: News and Updates - Documentation. To know more about current specific release, objectives, features, release notes/updates, quick demo and future road-map, you can watch my video below.



Network Latency and Bandwidth Assessment - for Network Admins and Infrastructure Architects ↗
Saturday' 13-Mar-2021

Introducing TOFFEE-Fudge - Network Packet Generator ↗
Saturday' 13-Mar-2021
TOFFEE Fudge is a simple intuitive Network Packet Generator which can be used to create custom test synthetic Network Packets and can be used in various applications such as networking research, network infrastructure troubleshooting, ethical hacking, as a network software development tool and so on.

TOFFEE deployment topology guide ↗
Saturday' 13-Mar-2021
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).

First TOFFEE Code Release ↗
Saturday' 13-Mar-2021
I started working on the new TOFFEE project (which is the fork of my earlier TrafficSqueezer open-source project) starting from 1st January 2016 onwards. Ever since I was busy in research and altering certain old features so that it is more minimal than TrafficSqueezer, a more focused agenda, deliver refined code and a broader vision. I have lined up more things to follow in the upcoming months. I want to focus about all aspects of WAN communication technologies not just on core WAN Optimization research and technology.



Featured Educational Video:
Watch on Youtube - [943//1] x23e TrueNAS ZFS Pool Resilver over and over again issue | ZFS NAS Storage | Forever Resilver ↗

Demo TOFFEE_DataCenter WAN Optimization VM (in VirtualBox) Test Setup ↗
Saturday' 13-Mar-2021
Demo TOFFEE_DataCenter WAN Optimization VM (in VirtualBox) Test Setup

TOFFEE-DataCenter Download :: TOFFEE-DATACENTER-1.2.2-1-portable ↗
Saturday' 13-Mar-2021

TOFFEE-DataCenter WAN Optimization :: TOFFEE-DATACENTER-1.3.25-1-portable ↗
Saturday' 13-Mar-2021
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

My sample Wireshark packet capture files for research ↗
Saturday' 13-Mar-2021
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.




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


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