Snapshot img { margin: 10px !important; } Overview of the global data center microserver marketTechnavio’s market research analyst predicts that the global data center microserver market will grow at a spectacular CAGR of close to 29% during the forecast period. One of the key drivers for this market is the growing use of big data analytics by companies to make business decisions. Businesses use several applications to generate massive amounts of structured and unstructured data with a lot of frequency and variety. Big data has helped enterprises to generate higher sales, boost efficiency, improve operations and customer services, and manage risk better by interpreting collected data. Enterprises are investing in high-density infrastructure for their data centers, and microservers are expected to play an important role in such infrastructures. Replacing traditional servers with microservers to process vast volumes of information will play a significant role in promoting market growth during the forecast period.In this industry report, the analysts expect factors like the construction of green data centers to spur market growth during the forecast period. Data centers consume a lot of electricity, which results in high carbon emissions. Green facilities implement concepts such as renewable energies, free cooling, and recycling to improve performance while reducing their power consumption and carbon footprint. The rising demand for efficient data centers has resulted in the defining of metrics, standards, and certification of data center environments. Microserver usage in green facilities will play a vital role in enabling environmentally friendly operations, leading to positive growth in this market over the estimated period.Segmentation by end-user and analysis of the data center microserver marketCloud and hosting service providers Enterprises Telecommunication providersThe cloud and hosting service providers segment is the largest in the data center microserver market and will generate revenues of over USD 1 billion by the end of the forecast period. Since the introduction of cloud technology, many organizations have adopted cloud-based services like Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). This increase in the adoption of cloud-based services will lead to the growth of this market segment during the forecast period.Geographical segmentation of the data center microserver marketAmericas APAC EMEAThe Americas dominated the global data center microservers market in 2015 with a share of approximately 48%. Due to the rising need for efficient power infrastructures, many companies in the Americas have started to replace traditional servers with microservers as it helps to manage light workloads. This recent increase in the adoption of microservers will lead to the strong growth of this market in the Americas.Competitive landscape and key vendorsThe global data center microserver market has several component suppliers who are sub-contractors to the major vendors. The competitive landscape is moderate as the market is not very diverse as compared to other server-related segments. The probability of startups and large firms competing for market shares is average. Since the availability of high-density servers is quite ubiquitous, the increased adoption of microservers may be restricted in the market.Key vendors in the market are -Dell Fujitsu HP Huawei Quanta Computer Super Micro ComputerOther vendors in the market include Boston, Cavium, Marvell Technology, Penguin Computing, Plat’Home, Tyan Computer, and Wiwynn.Key questions answered in the report includeWhat will the market size and the growth rate be in 2020? What are the key factors driving the global data center microserver market? What are the key market trends influencing the growth of the global data center microserver market? What are the challenges to market growth? Who are the key vendors in the global data center microserver market? What are the market opportunities and threats faced by the vendors in the global data center microserver market? Trending factors influencing the market shares of the Americas, APAC, and EMEA. What are the key outcomes of the five forces analysis of the global data center microserver market?Technavio also offers customization on reports based on specific client requirement.Related reportsGlobal Data Center Server Market 2015-2019 Global Micro Server Market 2015-2019 Green Data Center Market in the US 2015-2019 Global Data Center Power Market 2015-2019 Global Green Data Center Market 2015-2019A microserver is a small server that is easy to install, configure, and maintain, and has the same functionalities as the traditional server. The concept of microserver, which puts multiple servers into one physical chassis for shared power and cooling, is similar to that of a conventional server. A micro server consists of inbuilt hardware and software and also inclu

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Green Data Center (GDC) Market Size 2025-2029

The green data center (gdc) market size is valued to increase by USD 90.65 billion, at a CAGR of 13.2% from 2024 to 2029. Increase in electricity consumption and cost will drive the green data center (gdc) market.

Market Insights

Europe dominated the market and accounted for a 44% growth during the 2025-2029.
By Component - IT infrastructure segment was valued at USD 25.79 billion in 2023
By End-user - BFSI segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 204.83 million 
Market Future Opportunities 2024: USD 90647.40 million
CAGR from 2024 to 2029 : 13.2%

Market Summary

The market has gained significant traction in recent years due to escalating electricity consumption and costs in the information technology sector. Companies are increasingly recognizing the need to reduce their carbon footprint and enhance operational efficiency. One key driver of the GDC market is the adoption of Data Center Infrastructure Management (DCIM) solutions and automation technologies. These tools enable organizations to optimize their power usage, cooling systems, and server utilization, thereby reducing energy consumption and costs. A leading retailer, for instance, implemented a GDC strategy to streamline its supply chain operations. By deploying renewable energy sources and energy-efficient hardware, the retailer was able to reduce its energy consumption and carbon emissions, while also ensuring compliance with various environmental regulations. The cost savings from energy efficiency initiatives allowed the retailer to invest in other areas of its business, ultimately enhancing its competitiveness in the market. Despite the benefits, the high cost of building and maintaining a GDC remains a challenge for many organizations. The initial investment required for constructing a GDC, including the cost of renewable energy infrastructure and energy-efficient hardware, can be substantial. However, the long-term cost savings from energy efficiency and reduced carbon emissions often outweigh the upfront investment. As the market for GDCs continues to grow, innovations in technology and financing models are expected to make these facilities more accessible and cost-effective for businesses of all sizes.

What will be the size of the Green Data Center (GDC) Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free SampleThe market continues to evolve, with companies increasingly prioritizing sustainable practices to reduce environmental impact and enhance operational efficiency. One significant trend is the integration of renewable energy sources into data center infrastructure. According to recent studies, the use of renewable energy in data centers is projected to increase by 15% annually, reaching up to 40% of total energy consumption by 2025. Green building practices, such as capacity management, energy modeling software, and cooling infrastructure optimization, are essential components of GDCs. These practices not only contribute to sustainability but also offer tangible business benefits. For instance, lifecycle cost analysis shows that energy-efficient data centers can save companies up to 30% on their electricity bills. Moreover, sustainability certifications, like LEED and BREEAM, have become essential for companies seeking to demonstrate their commitment to environmental stewardship. Incorporating green initiatives into data center design can also lead to improved brand reputation and customer loyalty. As companies explore ways to reduce their carbon footprint, they are also turning to innovative technologies like AI-powered cooling, power distribution units, and network optimization. These solutions not only contribute to energy savings but also enhance operational efficiency and reliability. In conclusion, the GDC market is witnessing significant growth as companies prioritize sustainability and operational efficiency. Renewable energy integration, green building practices, and advanced technologies are key areas of focus for organizations looking to minimize their environmental impact while maximizing their business benefits.

Unpacking the Green Data Center (GDC) Market Landscape

In the dynamic business landscape of data centers, the market stands out as a strategic priority for organizations seeking to optimize IT equipment efficiency, reduce carbon footprint, and enhance sustainability. Compared to traditional data centers, GDCs offer significant improvements in power usage effectiveness (PUE) by an average of 1.5, resulting in substantial cost savings. Furthermore, server rack optimization and network infrastructure design, including the adoption of fault tolerance systems and server virtualization, contribute to increased virtual machine density and ener

Purpose: The dataset aims to facilitate the development and testing of hybrid optimization models for HRM, particularly those leveraging IoT devices, edge computing, and advanced machine learning techniques.

Data Sources:

IoT Devices: Simulated data from IoT sensors monitoring employee activity, attendance, and work environment metrics. Performance Records: Synthetic data representing employee task efficiency, workload, and satisfaction. Edge Computing Metrics: Simulated latency and bandwidth usage metrics to reflect edge server performance.

Employee Information:

employee_id: Unique identifier for employees. department: Department to which the employee belongs (e.g., HR, IT, Sales, Operations). role_level: Job level (Junior, Mid-level, Senior). Performance Metrics:

task_completion_rate: Percentage of completed tasks. hours_worked_per_week: Total hours worked in a week. overtime_hours: Hours worked beyond standard work hours. task_efficiency: Ratio of tasks completed to time taken. IoT Metrics:

activity_level: Physical activity level monitored by IoT devices. attendance_rate: Percentage of days attended. avg_desk_time: Average daily desk time (in hours). response_time: Average response time for work-related queries. motion_intensity: IoT-measured movement intensity. stress_level: Employee stress level derived from IoT data. Edge Computing Metrics:

latency: Average edge server response time (in ms). bandwidth_usage: Data usage by IoT devices (in MB). Resource Allocation Metrics:

allocated_tasks: Number of tasks assigned. task_allocation_cost: Cost incurred in task allocation (in $). resource_utilization: Ratio of time utilized to tasks allocated. Derived Metrics:

performance_index: Combined metric for task efficiency and task completion rate. satisfaction_score: Employee satisfaction level (scale of 1–10). optimization_score: Overall optimization score for resource allocation. Target Variable:

promotion_eligibility: Binary (0 or 1), indicating whether an employee is eligible for promotion.

The Numenta Anomaly Benchmark (NAB) is a novel benchmark for evaluating algorithms for anomaly detection in streaming, online applications. It is comprised of over 50 labeled real-world and artificial timeseries data files plus a novel scoring mechanism designed for real-time applications. All of the data and code is fully open-source, with extensive documentation, and a scoreboard of anomaly detection algorithms: github.com/numenta/NAB. The full dataset is included here, but please go to the repo for details on how to evaluate anomaly detection algorithms on NAB.

NAB Data Corpus

The NAB corpus of 58 timeseries data files is designed to provide data for research in streaming anomaly detection. It is comprised of both real-world and artifical timeseries data containing labeled anomalous periods of behavior. Data are ordered, timestamped, single-valued metrics. All data files contain anomalies, unless otherwise noted.

The majority of the data is real-world from a variety of sources such as AWS server metrics, Twitter volume, advertisement clicking metrics, traffic data, and more. All data is included in the repository, with more details in the data readme. We are in the process of adding more data, and actively searching for more data. Please contact us at nab@numenta.org if you have similar data (ideally with known anomalies) that you would like to see incorporated into NAB.

The NAB version will be updated whenever new data (and corresponding labels) is added to the corpus; NAB is currently in v1.0.

Real data

• realAWSCloudwatch/

  AWS server metrics as collected by the AmazonCloudwatch service. Example metrics include CPU Utilization, Network Bytes In, and Disk Read Bytes.

• realAdExchange/

  Online advertisement clicking rates, where the metrics are cost-per-click (CPC) and cost per thousand impressions (CPM). One of the files is normal, without anomalies.

• realKnownCause/

  This is data for which we know the anomaly causes; no hand labeling.

  • ambient_temperature_system_failure.csv: The ambient temperature in an office setting.
  • cpu_utilization_asg_misconfiguration.csv: From Amazon Web Services (AWS) monitoring CPU usage – i.e. average CPU usage across a given cluster. When usage is high, AWS spins up a new machine, and uses fewer machines when usage is low.
  • ec2_request_latency_system_failure.csv: CPU usage data from a server in Amazon's East Coast datacenter. The dataset ends with complete system failure resulting from a documented failure of AWS API servers. There's an interesting story behind this data in the "http://numenta.com/blog/anomaly-of-the-week.html">Numenta blog.
  • machine_temperature_system_failure.csv: Temperature sensor data of an internal component of a large, industrial mahcine. The first anomaly is a planned shutdown of the machine. The second anomaly is difficult to detect and directly led to the third anomaly, a catastrophic failure of the machine.
  • nyc_taxi.csv: Number of NYC taxi passengers, where the five anomalies occur during the NYC marathon, Thanksgiving, Christmas, New Years day, and a snow storm. The raw data is from the NYC Taxi and Limousine Commission. The data file included here consists of aggregating the total number of taxi passengers into 30 minute buckets.
  • rogue_agent_key_hold.csv: Timing the key holds for several users of a computer, where the anomalies represent a change in the user.
  • rogue_agent_key_updown.csv: Timing the key strokes for several users of a computer, where the anomalies represent a change in the user.

• realTraffic/

  Real time traffic data from the Twin Cities Metro area in Minnesota, collected by the Minnesota Department of Transportation. Included metrics include occupancy, speed, and travel time from specific sensors.

• realTweets/

  A collection of Twitter mentions of large publicly-traded companies such as Google and IBM. The metric value represents the number of mentions for a given ticker symbol every 5 minutes.

Artificial data

• artificialNoAnomaly/

  Artifically-generated data without any anomalies.

• artificialWithAnomaly/

  Artifically-generated data with varying types of anomalies.

Acknowledgments

We encourage you to publish your results on running NAB, and share them with us at nab@numenta.org. Please cite the following publication when referring to NAB:

Lavin, Alexander and Ahmad, Subutai. "Evaluating Real-time Anomaly Detection Algorithms – the Numenta Anomaly Benchmark", Fourteenth International Conference on Machine Learning and Applications, December 2015. [PDF]

The data provided correspond to the average hourly consumption of the main electrical appliances in dwellings (Wh/h); in other words, monitoring the average hour-by-hour consumption of these devices. Depending on the equipment, consumption is calculated for the year and different seasonalities. These data correspond to exercise 1 of the Panel Elecdom project (April 2019-April 2020).

The Elecdom Panel

The overall objective of the PANEL ELECDOM project is to improve knowledge of electricity consumption in the residential sector, which, with 33% of French electricity consumption in 2017, is the most consuming sector.

This study focuses on the specific uses of electricity. Based on information collected in the field, this research system, which is unique in France, is intended to continue with the aim of dynamically assessing the impact of societal changes and consumption patterns (products, behaviour).

A communication system records, at a time step of 10 minutes in 100 housing units representative of the French stock, the electricity consumption of the appliances connected to the power outlets and those of the electrical outlets on the switchboard. The data is then sent daily to an ftp server. Each dwelling is equipped with an average of 24.8 measuring points.

For more information

Reuse * How much electricity do we consume and how to reduce our bills? Le Monde: published on 06 October 2022 at 12.20, updated on 26 January 2023 at 15.22 (republication of the article on 25 September 2022 at 06.00)

Context

I am developing my data science skills in areas outside of my previous work. An interesting problem for me was to identify which factors influence life expectancy on a national level. There is an existing Kaggle data set that explored this, but that information was corrupted. Part of the problem solving process is to step back periodically and ask "does this make sense?" Without reasonable data, it is harder to notice mistakes in my analysis code (as opposed to unusual behavior due to the data itself). I wanted to make a similar data set, but with reliable information.

This is my first time exploring life expectancy, so I had to guess which features might be of interest when making the data set. Some were included for comparison with the other Kaggle data set. A number of potentially interesting features (like air pollution) were left off due to limited year or country coverage. Since the data was collected from more than one server, some features are present more than once, to explore the differences.

Content

A goal of the World Health Organization (WHO) is to ensure that a billion more people are protected from health emergencies, and provided better health and well-being. They provide public data collected from many sources to identify and monitor factors that are important to reach this goal. This set was primarily made using GHO (Global Health Observatory) and UNESCO (United Nations Educational Scientific and Culture Organization) information. The set covers the years 2000-2016 for 183 countries, in a single CSV file. Missing data is left in place, for the user to decide how to deal with it.

Three notebooks are provided for my cursory analysis, a comparison with the other Kaggle set, and a template for creating this data set.

Inspiration

There is a lot to explore, if the user is interested. The GHO server alone has over 2000 "indicators". - How are the GHO and UNESCO life expectancies calculated, and what is causing the difference? That could also be asked for Gross National Income (GNI) and mortality features. - How does the life expectancy after age 60 compare to the life expectancy at birth? Is the relationship with the features in this data set different for those two targets? - What other indicators on the servers might be interesting to use? Some of the GHO indicators are different studies with different coverage. Can they be combined to make a more useful and robust data feature? - Unraveling the correlations between the features would take significant work.

Cyclistic Bikes: A Comparison Between Casual and Annual Memberships

As part of the Google Data Analytics Certificate, I have been asked to complete a case study on the maximisation of Annual memberships vs those who choose the single and day-pass options.

The business goal of Cyclistic is clear, convert more members to Annual in an attempt to boost profits. The question is whether such a goal is truly profitable in the long term.

For this task, I will take the previous 12 months of data available from a public AWS server, https://divvy-tripdata.s3.amazonaws.com/index.html, and use that to build a forecast for the following years, looking for trends and possible problems that may impede Cyclistic’s ultimate goal

Sources and Tools

Rstudio: Tidyverse - Lubridate https://divvy-tripdata.s3.amazonaws.com/index.html

Business Goal

Under the direction of Lily Moreno and, by extension Cyclistic, the aim of this case study will be to analyse the differences in usage between Casual and Annual members.

For clarity, Casual members will be those who use the Day and Single Use options when using Cyclistic, whilst Annual refers to those who purchase a 12 month subscription to the service.

The ultimate goal is to see if there is a clear business reason to push forward with a marketing campaign to convert Casual users into Annual memberships

Tasks and Data Storage

The data I will be using was previously stored on an AWS server at https://divvy-tripdata.s3.amazonaws.com/index.html. This location is publicly accessible but the data within can only be downloaded and edited locally.

For the purposes of this task, I have downloaded the data for the year 2022, 12 separate files that I then collated into a single zip file to upload to Rstudio for the purposes of cleaning, arranging and studying the information. The original files will be located on my PC and at the AWS link. As part of the process, a backup file will be created within Rstudio to ensure that the original data is always available.

Process

After uploading the dat to Rstudio and putting in a naming convention, Month, the next step was to compare and equate the names of the coloumns. As the information came from 2022, 2 years after Cyclistic updated their naming conventions, this step was more of a formality to ensure that the files could later be joined into one. No irregularities were found at this stage.

As all coloumn names matched, there was no need to rename them. furthermore, all ride_id fields were already in character format.

Once this check was complete, all tables were compiled into one, named all_trips

Cleaning

The first issue found was the number of fields used to identify the different member types. The files used a four coloumn approach with "member" and "subscriber" for Annual and "Customer" and "casual" for the casual users. These four fields were aggregated into 2, Member and Casual.

As the original files only measured ride-level, more fields were added in the form of day, week, month, year to enable more opportunites to aggregate the data.

ride_length was added for consistency and to provide a clearer output. After adding this coloumn, the data was morphed from Factor to Numeric to ensure that the final output could be measured.

Analysis

Here, I will provide the final code used to descirbe the final process

Descriptive analysis on ride_length (all figures in seconds)

mean(all_trips_v2$ride_length) #straight average (total ride length / rides) median(all_trips_v2$ride_length) #midpoint number in the ascending array of ride lengths max(all_trips_v2$ride_length) #longest ride min(all_trips_v2$ride_length) #shortest ride

All four lines condensced

summary(all_trips_v2$ride_length)

Compare members and casual users

aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = mean) aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = median) aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = max) aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = min)

See the average ride time by each day for members vs casual users

aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual + all_trips_v2$day_of_week, FUN = mean)

Fixing the issue of the days of week being out of order

all_trips_v2$day_of_week <- ordered(all_trips_v2$day_of_week, levels=c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"))

The average ride time by each day for members vs casual users

aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual + all_trips_v2$day_of_week, FUN = mean)

analyze ridership data by type and weekday

all_trips_v2 %>% mutate(weekday = wday(started_at, label = TRUE)) %>% #creates weekday field using wday() group_by(member_casual, weekday) %>% #groups by usertype and weekday summarise(number_of_rides = n() ...

Overview:

This is the raw data of a current consumption measurement campaign of an end-device implementing the novel LoRaWAN LR-FHSS mechanism. The measurements have been made implementing a complete network, which includes a gateway, end-device and network server all implementing the LoRaWAN LR-FHSS technology. We used the following equipment:

Gateway: Kerlink iBTS Compact

End-Device: LR1121DVK1TBKS

Network Server: ChirpStack

Power Analyzer: Keysight 14585A

The provided files are for uplink LR-FHSS transmission measurements with and without confirmation with different LR-FHSS DR configurations. The current consumption exclusively accounts for the radio interface.

The configuration of the end-device is the following:

FRM Payload Size: 4 bytes

Transmission Power: +14 dBm

This dataset is part of a published journal article: R. Sanchez-Vital, L. Casals, B. Heer-Salva, R. Vidal, C. Gomez, E. Garcia-Villegas, "Energy Performance of LR-FHSS: Analysis and Evaluation", Sensors 24, no. 17: 5770, Sep. 2024. https://doi.org/10.3390/s24175770

The manuscript provides current consumption measurements, an analytical model of the average current consumption, battery lifetime, and energy efficiency of data transmission, and the evaluation of several parameters.

Journal Article Abstract:

Long-range frequency hopping spread spectrum (LR-FHSS) is a pivotal advancement in the LoRaWAN protocol that is designed to enhance the network’s capacity and robustness, particularly in densely populated environments. Although energy consumption is paramount in LoRaWAN-based end devices, this is the first study in the literature, to our knowledge, that models the impact of this novel mechanism on energy consumption. In this article, we provide a comprehensive energy consumption analytical model of LR-FHSS, focusing on three critical metrics: average current consumption, battery lifetime, and energy efficiency of data transmission. The model is based on measurements performed on real hardware in a fully operational LR-FHSS network. While in our evaluation, LR-FHSS can show worse consumption figures than LoRa, we find that with optimal configuration, the battery lifetime of LR-FHSS end devices can reach 2.5 years for a 50 min notification period. For the most energy-efficient payload size, this lifespan can be extended to a theoretical maximum of up to 16 years with a one-day notification interval using a cell-coin battery.

Data structure:

Filenames:

ACK and noACK state the use (or not) of confirmation. DR8 to DR11 state the use of each of the LR-FHSS DR configurations.

CSV file structure:

The first three rows refer to metadata (Power Analyzer and End-Device models, utilization of ACK, DR configuration, Sampling Period and Measurement Date).

Then, the labels are in the fourth row (Time, Current).

The other rows refer to the actual measurements. Time instants are measured in seconds and current in Amperes.

Introduction

Greetings to the ML community! We are excited to introduce our comprehensive Network Traffic Dataset, a resource that includes not only network packet data curated for machine learning applications, but also the original PCAP files from which the dataset was derived. This dataset was born out of a rigorous lab setup and is ideal for a variety of use cases such as intrusion detection, network performance analysis, anomaly detection, and more.

Lab Setup

The data is sourced from a controlled lab environment featuring three key systems:

1. Kali Machine (10.20.30.103): Used as the primary source of attack traffic in our lab setup, leveraging Kali's extensive set of penetration testing and attack tools.
2. OWASP Broken Web Application (10.20.30.101): Hosts a wide variety of intentionally insecure web applications, providing a target for our Kali machine's attack traffic.
3. Normal Windows PC (10.20.30.1): Operates as a source of benign traffic for our dataset, representing normal user activity on a network.

Dataset Creation

The creation of the PCAP file, its transformation into a CSV file, and the addition of an "alert" feature were carried out in a systematic manner. First, we set up a controlled lab environment featuring a Kali Machine, an OWASP Broken Web Application server, and a Normal Windows PC. We then captured the network traffic between these systems using a packet sniffer, which resulted in a comprehensive PCAP file encompassing a wide array of network protocols and traffic scenarios, both benign and suspicious.

To transform this raw packet data into a more accessible and machine-readable format, we used Tshark, a network protocol analyzer. Through Tshark's powerful extraction capabilities, we parsed the PCAP file and transformed it into a CSV file. Each row in this CSV file corresponds to a single network packet, and each column represents a specific field extracted from that packet.

The final step was the addition of an "**alert**" feature to the dataset. This feature was designed to assist machine learning researchers in their work, particularly in areas such as anomaly detection or intrusion detection. To create this feature, we labeled each network packet as either "benign" or "suspicious" based on its origin and nature. The "**benign**" label represents normal network traffic primarily from the Normal Windows PC, while the "**suspicious**" label signifies potential attack traffic mainly sourced from the Kali machine attacking the OWASP server. This addition of the "alert" feature provides an important target variable for supervised machine learning models.

Content

This dataset includes two distinct parts:

1. Original PCAP Files: The original PCAP files, which capture the raw network traffic between the systems in our lab setup, serve as the source of our dataset.
2. CSV Data: The CSV file contains data extracted from the PCAP files using Tshark, a network protocol analyzer. The extracted data includes a wide range of fields relevant to various protocols such as Ethernet, IP, TCP, UDP, ICMP, HTTP, DNS, SMTP, POP, IMAP, and FTP.

Each row in the CSV dataset corresponds to a single network packet, with each column representing one of the fields extracted from that packet. These fields capture a comprehensive view of each packet's metadata and content, providing an extensive base for network traffic analysis.

An additional column has been added to label each row as either "**benign**" or "**suspicious**". The "benign" label represents normal network traffic, primarily from the Normal Windows PC, while the "suspicious" label represents potential attack traffic, mainly sourced from the Kali machine attacking the OWASP server.

Field Descriptions

Let's go through each field:

1. frame.number: The number of the packet within the capture file.
2. frame.len: The length of the packet.
3. frame.time: The timestamp of when the packet was captured.
4. frame.time_epoch: The timestamp in seconds since the epoch (Jan 1, 1970) when the packet was captured.
5. frame.protocols: List of all protocols used in the packet.
6. eth.src: The source MAC address.
7. eth.dst: The destination MAC address.
8. eth.type: The type field of the Ethernet frame.
9. ip.src: The source IP address.
10. ip.dst: The destination IP address.
11. ip.len: The total length of the IP packet, including headers and data.
12. ip.ttl: The time-to-live value for the IP packet.
13. ip.flags: The flags set in the IP header.
14. ip.frag_offset: The fragmentation offset for the IP packet.
15. ip.proto: The protocol used in the IP packet.
16. ip.version: The version of the IP protocol used (IPv4 or IPv6).
17. ip.dsfield: The Differentiated Services Field (used for Quality of Service).
18. ip.checksum: The checksum of the IP header.
19. tcp.srcport: The source port...

Google’s energy consumption has increased over the last few years, reaching 25.9 terawatt hours in 2023, up from 12.8 terawatt hours in 2019. The company has made efforts to make its data centers more efficient through customized high-performance servers, using smart temperature and lighting, advanced cooling techniques, and machine learning. Datacenters and energy Through its operations, Google pursues a more sustainable impact on the environment by creating efficient data centers that use less energy than the average, transitioning towards renewable energy, creating sustainable workplaces, and providing its users with the technological means towards a cleaner future for the future generations. Through its efficient data centers, Google has also managed to divert waste from its operations away from landfills. Reducing Google’s carbon footprint Google’s clean energy efforts is also related to their efforts to reduce their carbon footprint. Since their commitment to using 100 percent renewable energy, the company has met their targets largely through solar and wind energy power purchase agreements and buying renewable power from utilities. Google is one of the largest corporate purchasers of renewable energy in the world.

Multiple linear regression model for staff system use and facility characteristics.

These raw data have not been subjected to the National Ocean Service's quality control or quality assurance procedures and do not meet the criteria and standards of official National Ocean Service data. They are released for limited public use as preliminary data to be used only with appropriate caution. cdm_data_type=TimeSeries cdm_timeseries_variables=STATION_ID,BEGIN_DATE,END_DATE Conventions=COARDS, CF-1.6, ACDD-1.3 featureType=TimeSeries geospatial_lat_units=degrees_north geospatial_lon_units=degrees_east infoUrl=https://opendap.co-ops.nos.noaa.gov/ institution=NOAA NOS CO-OPS (Center for Operational Oceanographic Products and Services) keywords_vocabulary=GCMD Science Keywords sourceUrl=(source database) standard_name_vocabulary=CF Standard Name Table v70

EHRs usage indicators evaluated.

Frequency distribution for the facility characteristics (n = 213).

A mobile-data basket of *** gigabytes (* GB) cost around **** U.S. dollars per month on average in Russia in 2023, whereas the monthly price of a fixed-broadband basket cost was recorded at approximately *** U.S. dollars. For all baskets, the tariffs were less expensive than in the previous year. Determinants of low pricing One gigabyte of mobile internet in Russia amounted to **** U.S. dollars in 2022, which was one of the lowest prices in Central and Eastern Europe (CEE). Several factors influence the inexpensive mobile data in Russia, such as the generally lower income of the population and competition among internet providers. At the same time, Russia was one of few CEE countries where mobile internet prices in 2022 grew relative to the previous year, reflecting the impact of Western sanctions and accelerated inflation as a result of the Russia-Ukraine war. How good is the internet in Russia? In the fixed broadband internet speed ranking, Russia ranked ******* by download speed and ****** by upload speed in the CEE region. More specifically, data downloading in Russia amounted to approximately ** megabits per second (Mbps) while uploading stood at around ** Mbps as of May 2022. In terms of mobile broadband internet speed, Russia ranked second last among CEE countries. Data downloading via mobile internet in the country was recorded at approximately **** Mbps, which was more than ** Mbps lower than the speed of the top performer Bulgaria in May 2022. Furthermore, Russia’s mobile internet ping speed was measured at ** milliseconds, being the second lowest among CEE countries in 2022. In other words, there was a higher delay in data transmission between sending a signal to a server and receiving a response back than in most other countries in the region.

In 2022, private data centers in South Korea had an average Power Usage Effectiveness metric of ****. Compared with average PUE figures for data centers worldwide, South Korea's private data centers were not as energy efficient.

Equinix, a global leader in colocation data center services, listed *** International Business Exchange (IBX) data centers worldwide in 2024. This marked an increase of **** facilities from the previous year, reflecting the company's efforts to meet growing global demand for data center capacity. The Americas housed *** facilities in 2024, with the firm generating *** billion U.S. dollars in the region that year. Efficiency and Sustainability Efforts As Equinix expands its footprint, the company is also focused on improving operational efficiency. In 2023, the average annual Power Usage Effectiveness (PUE) of Equinix data centers worldwide decreased to **** from **** in 2022, indicating enhanced energy efficiency. Despite this improvement, global electricity consumption rose by over **** percent to ***** GWh in 2023, reflecting the challenges of balancing growth with sustainability. Notably, Equinix maintained its commitment to renewable energy, with ** percent of its total electricity consumption coming from renewable sources. Competitive Landscape in Data Center Equipment While Equinix focuses on providing data center infrastructure, the equipment within these facilities plays a crucial role in their performance. A 2023 survey revealed that Dell EMC was the leading manufacturer for both data center storage and server equipment among U.S. and European organizations. Hewlett Packard Enterprise (HPE) secured the second position in both categories, highlighting the competitive nature of the data center equipment market. This information underscores the importance of partnerships between data center operators like Equinix and equipment manufacturers to meet evolving customer needs.