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

Recommended Topics:

Live demo - Data Transfer - High bandwidth to Low bandwidth ↗
Saturday' 13-Mar-2021

I always wanted to do some real experiments and research on packet flow patterns from High-bandwidth to Low-bandwidth networks via networking devices. This is something can be analyzed via capturing Network stack buffer data and other parameters, bench-marking, and so on. But eventually the data-transfer nature and other aspects is often contaminated due to the underlying OS and the way Network stack is implemented. So to understand the nature of packet flow from Higher to Lower bandwidth and vice-versa such as Lower to higher bandwidth, I thought I experiment with various tools and things which physically we can observe this phenomena.

TOFFEE (and TOFFEE-DataCenter) deployment with VPN devices ↗
Saturday' 13-Mar-2021

In case if you need to deploy TOFFEE along with your existing VPN devices you can deploy the same as shown below. This will allow your VPN devices to encrypt your TOFFEE WAN Optimized network data. NOTE: Make sure about the VPN deployment topology done in the right order. Else TOFFEE (LAN side) may get VPN encrypted packets which may not be possible (and or difficult) to further optimize. Hence always make sure to deploy them in a topology suggested below so that TOFFEE devices are out of VPN tunnel.

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.

TOFFEE WAN Optimization software development, roadmap, live-demo - Update: 06-Nov-2016 ↗
Saturday' 13-Mar-2021

Here are some of the screenshots of the new upcoming TOFFEE WAN Optimization release and live demo.

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).

TOFFEE-DataCenter with GlusterFS Storage Cluster ↗
Saturday' 13-Mar-2021

TEST CASES :: TEST RESULTS :: TOFFEE-Mocha-1.0.32 asymmetric constant packet delay feature ↗
Saturday' 13-Mar-2021

TOFFEE (and TOFFEE-DataCenter) deployment with web-proxy cache ↗
Saturday' 13-Mar-2021

If you want to deploy TOFFEE along with a web-proxy cache (such as Squid Proxy) you can deploy the same as shown below. TOFFEE does not cache files. TOFFEE does packet level network optimization. So if you want caching your web content you can use transparent mode web-proxy cache intercepting your WAN links. A web-proxy may reduce amount of data being processed (optimized) within these TOFFEE devices and so reduce the CPU overheads and improve its performance.

Recording Lab Monthly power-consumption readings for Research ↗
Saturday' 13-Mar-2021

Here is my home lab monthly power-consumption readings for research. This will help to measure and monitor the overall power usage and assess the power requirements. This will help me in future purchases such as UPS, battery upgrades and so on. And as well remove replace old obsolete hardware with new less power-consuming devices.

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.

Featured Educational Video:

Watch on Youtube - [4073//1] 0x1c9 NAS OS | Expert's take on FreeNAS vs UNRAID | My two cents | Best Tips ↗

TOFFEE-DataCenter packet packaging feature for WAN Optimization ↗
Saturday' 13-Mar-2021

YouTube Video Network Traffic Optimization - WAN Optimization Demo ↗
Saturday' 13-Mar-2021

MySQL Database Network Data - WAN Acceleration ↗
Saturday' 13-Mar-2021

Here is a quick demo of TOFFEE WAN Optimization optimizing MySQL Data transfers of a MySQL Client to Server Remote Access.

TrueBench - Linux CPU Benchmarking system ↗
Saturday' 13-Mar-2021

TrueBench is an unique open-source benchmarking system in which the core system performance and efficiency parameters are measured at extreme high resolution in the order of several million/billion µ-seconds for a given specific task. TrueBench is a part of The TOFFEE Project research.

Building my own CDN - choosing a web-hosting to deploy my CDN - Update: 28-July-2016 ↗
Saturday' 13-Mar-2021

The TOFFEE Project website is hosted on Inmotion Hosting. And so I am looking for alternate hosting provider to build my first CDN node. My plan is to make multiple sub-domains of my website such as cdn1.the-toffee-project.org, cdn2.the-toffee-project.org and point each of this corresponding subdomain(s) to various alternative web hosting servers geographically spread across the world. Sometimes choosing the same vendor for multiple CDN nodes may result multiple servers existing in the data-center. And this becomes an issue if there is some catastrophic network disaster.

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