Dataset Overview

Cellular communications, especially with the advent of 5G mobile networks, demand stringent adherence to high-reliability standards, ultra-low latency, increased capacity, enhanced security, and high-speed user connectivity. To fulfill these requirements, mobile operators require a programmable solution capable of supporting multiple independent tenants on a single physical infrastructure. The advent of 5G networks facilitates end-to-end resource allocation through Network Slicing (NS), which allows for the division of the network into distinct virtual slices.

Network slicing in 5G stands as a pivotal feature for next-generation wireless networks, delivering substantial benefits to both mobile operators and businesses. Developing a Machine Learning (ML) model is crucial for accurately predicting the optimal network slice based on key device parameters. Such a model also plays a vital role in managing network load balancing and addressing network slice failures.

Dataset Characteristics and Target Classes

The dataset is structured to support the development of an ML model that can classify the optimal network slice based on device parameters. The target output comprises three distinct classes:

1. Enhanced Mobile Broadband (eMBB):

   • Focuses on high-bandwidth and high-speed data transmission.
   • Facilitates activities such as high-definition video streaming, online gaming, and immersive media experiences.

2. Ultra-Reliable Low Latency Communication (URLLC):

   • Emphasizes extremely reliable and low-latency connections.
   • Supports critical applications like autonomous vehicles, industrial automation, and remote surgery.

3. Massive Machine Type Communication (mMTC):

   • Aims to support a massive number of connected devices.
   • Enables efficient communication between Internet of Things (IoT) devices, smart cities, and sensor networks.

File name: deepslice_data.csv

Data Attributes (Columns Desc)

• Device ID: Unique identifier for each device.
• Connection Type: Specifies the type of connection (e.g., LTE, 5G).
• Latency Requirements (ms): The maximum allowable latency for the device's operation.
• Bandwidth Requirements (Mbps): The bandwidth needed for optimal device performance.
• Reliability (%): The required reliability level for the device's connection.
• Data Rate (Mbps): The data rate the device can handle.
• Device Type: Categorizes the device (e.g., smartphone, IoT sensor).
• Mobility (Low/Medium/High): Indicates the mobility level of the device.
• Battery Life (hours): Expected battery life of the device.
• Application Type: The primary application for the device's connection (e.g., video streaming, industrial control).

Class Distribution

The dataset includes labeled instances categorized into the three target classes: eMBB, URLLC, and mMTC. Each instance corresponds to a specific device configuration and its optimal network slice.

Application and Relevance

Network slicing in 5G is instrumental in provisioning tailored network services for specific use cases, ensuring optimal performance, resource utilization, and user experiences based on the requirements of eMBB, URLLC, and mMTC applications. This dataset is invaluable for researchers and practitioners aiming to design and implement ML models for network slice prediction, thereby enhancing the operational efficiency and reliability of 5G networks.

Conclusion

This dataset is meticulously curated to facilitate the development of ML models for predicting the optimal 5G network slice. It encompasses a comprehensive set of attributes and target classes, ensuring that it meets the highest standards required for advanced research and practical applications in the field of cellular communications and network management.

The 6G Wireless Network Slicing QoS Prediction and Optimization Dataset contains a comprehensive collection of data designed to support the prediction and optimization of Quality of Service (QoS) in 6G network slices. The dataset includes 2345 rows and multiple features, capturing various network parameters such as traffic load, network utilization, latency, packet loss, signal strength, and more. These features are essential for understanding and improving the performance of network slices under different conditions, including varying traffic types, device types, and network failure scenarios.

The dataset is intended for use in machine learning models, particularly for predicting the throughput (QoS metric) and optimizing network resources. Preprocessing steps like Min-Max normalization and label encoding have been applied to ensure the data is ready for analysis. This dataset is ideal for researchers and engineers working on 6G networks, aiming to improve reliability, security, and efficiency.

Features Included: Traffic Load (bps): The network traffic load in bits per second. Network Utilization (%): The percentage of network resources being used. Latency (ms): The delay in the network, measured in milliseconds. Packet Loss Rate (%): The percentage of packets lost during transmission. Signal Strength (dBm): The strength of the wireless signal. Bandwidth Utilization (%): The percentage of available bandwidth used. Device Type: The type of device (e.g., Mobile, IoT). Traffic Type: The type of network traffic (e.g., Voice, Video, Data). Network Slice Failure: Indicates if there was a network slice failure. Overload Status: Indicates if the network is overloaded. QoS Metric (Throughput): The target column representing the Quality of Service metric (throughput in Mbps).

The Network Slicing Market Report is Segmented by Component (Infrastructure [RAN, Core, Transport] and Software [MANO, Analytics, Security]), Service (Professional [Consulting, Integration, Testing] and More), Application (Remote Monitoring & Surveillance, Network Function Virtualization and Cloud RAN, and More), End-User Industry (Healthcare, Automotive and Transportation, and More), and Geography.

Dataset Description

LTE/5g - User Equipment categories or classes to define the performance specifications

Packet Loss Rate - number of packets not received divided by the total number of packets sent.

Packet Delay - The time for a packet to be received.

Slice type - network configuration that allows multiple networks (virtualized and independent)

GBR - Guaranteed Bit Rate

Healthcare - Usage in Healthcare (1 or 0)

Industry 4.0 - Usage in Digital Enterprises(1 or 0)

IoT Devices - Usage

Public Safety - Usage for public welfare and safety purposes (1 or 0)

Smart City & Home - usage in daily household chores

Smart Transportation - usage in public transportation Smartphone - whether used for smartphone cellular data

BINS is based on a dataset called Business Intent and Network Slicing Correlation Dataset (BINS) within the context of intent-based networking. This dataset primarily includes user business intent description data, business intent annotation data, and data correlating business intents with network slices. As the latest dataset for network intent recognition, the deployment of BINS will aid in the development of intent-based network management systems and then promote the development of network automation.

Slicing of genomics data according to chromosome parts

Snapshot img

Network Slicing Market Size 2025-2029

The network slicing market size is valued to increase USD 1.8 billion, at a CAGR of 34.2% from 2024 to 2029. Development of smart infrastructures will drive the network slicing market.

Major Market Trends & Insights

Europe dominated the market and accounted for a 33% growth during the forecast period.
By Component - Solution segment was valued at USD 113.00 billion in 2023
By End-user - Communication service providers segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 1.00 million
Market Future Opportunities: USD 1803.60 million
CAGR : 34.2%
Europe: Largest market in 2023

Market Summary

The market represents a dynamic and evolving landscape, driven by the increasing adoption of core technologies such as 5G and edge computing. This market is characterized by various applications, including enhanced mobile broadband, ultra-reliable low-latency communications, and massive machine-type communications. Service types or product categories include network infrastructure, software platforms, and managed services. Regulations, such as those set by the International Telecommunication Union and regional telecom authorities, play a crucial role in shaping market dynamics. According to a recent study, the market is expected to account for over 25% of the total 5G core network infrastructure market by 2026.
The development of smart infrastructures and strategic collaborations among market participants are major drivers, while the threat of cybersecurity breaches poses a significant challenge. Despite these challenges, the market offers numerous opportunities for growth and innovation.

What will be the Size of the Network Slicing Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Network Slicing Market Segmented and what are the key trends of market segmentation?

The network slicing industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Solution
  Services


End-user

  Communication service providers
  Enterprises


Application

  Manufacturing
  Government
  Automotive
  Media and entertainment


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    The Netherlands
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Component Insights

The solution segment is estimated to witness significant growth during the forecast period.

Network slicing, a cutting-edge technology, enhances network efficiency by allocating dedicated network resources to specific applications or users based on their unique requirements. This approach ensures guaranteed Quality of Service (QoS) and optimizes latency for improved performance. Network slicing security safeguards data transmitted through these slices, enabling multi-access edge computing and software-defined networking. Transport network slicing and radio resource management are integral components of this architecture, providing scalable and flexible resource allocation. The 5G network, with its high bandwidth and low latency capabilities, is a catalyst for the market's growth. According to recent studies, the market for network slicing is projected to expand significantly, with up to 30% of mobile operators expected to deploy network slicing solutions by 2025.

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The Solution segment was valued at USD 113.00 billion in 2019 and showed a gradual increase during the forecast period.

Moreover, network slicing's potential applications span various sectors, including healthcare, manufacturing, and education, enabling efficient resource utilization and enhanced network agility. Policy-based slicing and dynamic resource allocation further augment network slicing's capabilities, ensuring service level agreements and infrastructure slicing for diverse industries. NFV orchestration and RAN slicing are essential elements of network slicing architecture, facilitating the instantiation and management of network slices. Network slicing's benefits extend beyond improved network efficiency, offering dynamic resource allocation, flexible network slicing, and end-to-end slicing for seamless connectivity. In the evolving technology landscape, network slicing's role in optimizing network performance and providing customized solutions for diverse industries continues to unfold, offering significant growth opportunities for market participants.

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Regional Analysis

Europe is estimated to contribute 33% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends

This dataset contains the raw experiment data and replication package for the ICSME'20 paper: "GenSlice: Generalized Semantic History Slicing". The replication package is also available at: https://github.com/Chenguang-Zhu/icsme20-artifact (2020-08-06)

Slice Coco Test Data

## Overview

Slice Coco Test Data is a dataset for object detection tasks - it contains Objects In Aerial Images annotations for 2,484 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Mobile Virtual Network Operators(MVNOs)

This dataset is designed to simulate the performance of 6G network slicing technology in the context of intelligent education applications. It provides a collection of data that includes various network parameters such as bandwidth allocation, latency, packet loss, and resource utilization for different educational services like virtual classrooms, AI tutoring, and VR learning.

The target column categorizes the Service Quality Assurance (SQA) of network slices into four distinct levels: Low, Medium, High, and Excellent. These categories reflect the overall network performance, considering key metrics like latency, packet loss, and resource utilization. This dataset is ideal for training machine learning models aimed at predicting network performance and optimizing network resource allocation for educational environments.

The dataset includes:

Slice_ID: A unique identifier for each network slice. Bandwidth (Mbps): Network bandwidth allocated to the slice. Latency (ms): Time delay in the network for the slice. Packet Loss (%): Percentage of data packets lost during transmission. User Count: The number of active users in the network slice. Application Type: The type of educational application (e.g., VR Learning, AI Tutoring). Reliability (%): The reliability of the network slice. Security Level: Security classification for the slice (Low, Medium, High). Resource Utilization (%): The percentage of resources used by the slice.

About this artifact

This artifact contains the implementation and the results of the evaluation of a static slicer for WebAssembly described in the ICSE 2022 paper titled "Static Stack-Preserving Intra-Procedural Slicing of WebAssembly Binaries".

The artifact contains a docker image (wassail-eval.tar.xz) that contains everything necessary to reproduce our evaluation, and the actual data resulting from our evaluation: 1. The implementation of our slicer (presented in Section 4.1) is included in the docker machine, and is available publicly here: https://github.com/acieroid/wassail/tree/icse2022 2. Test cases used for our evaluation of RQ1 are included in the docker machine and in the rq1.tar.xz archive. 3. The dataset used in RQ2, RQ3, and RQ4 is included in the docker machine. 4. The code needed to run our evaluation of RQ2, RQ3, and RQ4 is included in the docker machine. 5. The scripts used to generate the statistics and graphs that are included in the paper for RQ2, RQ3, and RQ4 are included in the docker machine and as the *.py files in this artifact. 6. The data of RQ5 that has been used in our manual investigation is included in the docker machine and in the rq5.tar.xz archive, along with rq5-manual.txt detailing our manual analysis findings.

How to obtain it

Our artifact is available on Zenodo at the following URL: https://zenodo.org/record/5821007

Setting up the Docker image

Downloading The Artifact

The artifact is available at the following URL: https://zenodo.org/record/5821007

Loading The Docker Image

Once the artifact is downloaded in the file icse2022slicing.tar.xz, it can be extracted and loaded into Docker as follows (this takes a few minutes): docker import icse2022slicing.tar.xz To simplify further commands, you can tag the image using the printed sha256 hash of the image: if the docker import command resulted in the hash 54aa9416a379a6c71b1c325985add8bf931752d754c8fb17872c05f4e4b52ea2, you can run: docker tag 54aa9416a379a6c71b1c325985add8bf931752d754c8fb17872c05f4e4b52ea2 wassail-eval

Once the Docker image has been loaded, you can run the following commands to obtain a shell in the appropriate environment: docker volume create result docker run -it -v result:/tmp/out/ wassail-eval bash su - opam

Reproducing results of RQ1

Our manual translations of the "classical" examples are included in the rq1/ directory (available in the docker image and in rq1.tar.xz). We include the slices computed by our implementation in the rq1/out/ directory.

A slice can be produced for each example in the docker image as follows, where the first argument is the name of the program being sliced, the second the function index being sliced, the third the slicing criterion (indicated as the instruction index, where instructions start at 1), and the last argument is the output file for the slice:

cd rq1/
wassail slice scam-mug.wat 5 8 scam-mug-slice.wat
wassail slice montreal-boat.wat 5 19 montreal-boat-slice.wat
wassail slice word-count.wat 1 41 word-count-slice1.wat
wassail slice word-count.wat 1 43 word-count-slice2.wat
wassail slice word-count.wat 1 39 word-count-slice3.wat
wassail slice word-count.wat 1 45 word-count-slice4.wat
wassail slice word-count.wat 1 37 word-count-slice5.wat
wassail slice agrawal-fig-3.wat 3 38 agrawal-fig-3-slice.wat
wassail slice agrawal-fig-5.wat 3 37 agrawal-fig-5-slice.wat

The slice results can then be inspected manually, and compared with the original version of the .wat program to see which instructions have been removed, or with the expected solutions in the out/ directory, e.g. by running: diff word-count-slice1.wat out/word-count-slice1.wat (No output is expected if the slice is correct)

Reproducing results of RQ2, RQ3, and RQ4

For these RQ, we include the data resulting from our evaluation, but we also allow reviewers to rerun the full evaluation if needed. However, such an evaluation requires a heavy machine and takes quite some time (4-5 days to run to completion with a 4 hours timeout). In our case, we used a machine with 256 GB of RAM and a 64-core processor with HyperThreading enabled, allowing us to run 128 slicing jobs in parallel.

Runnig the Evaluation

We explain how to run the full evaluation, or only a partial evaluation below. One can directly skip to the next section and reuse our raw evaluation results, provided alongside this artifact.

Running the Full Evaluation

In order to reproduce our evaluation, you can run the following commands in the docker image. It is recommended to run them in a tmux session if one wants to inspect other elements in parallel (tmux is installed in the docker image). The timeout (set to 4 hours per binary, like in the paper) can be decreased by editing the evaluate.sh script (vim is installed in the docker image).

This is expected to take 2-3 days of time, on a machine with 128 cores. In order to produce only partial results, see the next section.

cd filtered
cat ../supported.txt | parallel --bar -j 128 sh ../evaluate.sh {}

The results are outputted in the /tmp/out/ directory.

Running a Partial Evaluation

If one does not have access to a high-end machine with 128 cores nor the time to run the full evaluation, it is possible to produce partial results. To do so, the following commands can be run. This will run the evaluation on the full dataset in a random order, which can be stopped early to represent a partial view of our evaluation, on a random subset of the data. In order to gather more datapoints, it is also advised to decrease the timeout in the evaluate.sh file, for example to 20 minutes by setting TIMEOUT=20m with nano evaluate.sh. The number of slicing jobs running in parallel can also be decreased to match the number of processors on the machine running the experiments (the -j 128 argument in the following command runs 128 parallel jobs)

sudo chown opam:opam /tmp/out/
cd filtered
shuf ../supported.txt | parallel --bar -j 128 sh ../evaluate.sh {}

The evaluation results will be stored in the /tmp/out/ directory.

Skipping the Evaluation Run

Instead of rerunning the evaluation, one can rely on our full results included in the data.txt.xz and error.txt.xz archives. These can simply be downloaded from within the Docker machine and extracted in /tmp/out/:

cd /tmp/out/
wget https://zenodo.org/record/5821007/files/data.txt.xz
wget https://zenodo.org/record/5821007/files/error.txt.xz
unxz data.txt.7z
unxz error.txt.7z

Processing the data

In order to process this data, we included multiple python script. These require around 100GB of RAM to load the full dataset in memory. The scripts should be run with Python 3. When running this in the docker image, first run cd /tmp/out/ && cp /home/opam/*.py ./ - To count the number of functions sliced, run cut -d, -f 1,2 data.txt | sort -u | wc -l. This takes around 6 minutes to run on the full dataset. - To count the total number of slices encountered, run wc -l data.txt error.txt. This takes around 15 seconds to run. - To count the number of errors encountered, run wc -l error.txt. This takes around 1 second to run. - To produce data and graphs regarding the sizes and timing, run python3 statistics-and-plots.py. This will output the statistics presented in the paper, along with Figure 2 (rq2-sizes.pdf) and Figure 3 (rq2-times.pdf). This script takes around 35 minutes to run. - To find the executable slices that are larger than the original programs, run python3 larger-slices.py > larger.txt. This script takes around 2h30 to run. It will list the slice using the notation filename function-sliced slicing-criterion in the larger.txt file, from which the slice can be recomputed by running wassail slice function-sliced slicing-criterion output.wat in the docker image. It will also output statistics regarding these slices, which you can easily inspect by running tail larger.txt. - To investigate slices that could not be computed, run: sed -i error.txt -e 's/annotation,/annotation./' python3 errors.py This will take a few seconds to run and will print a summary of the errors encountered during the slicing process, and requires some manual sorting to map to the categories we discuss in the paper. Here is a summary of the errors encountered and their root cause:

Root Cause: Unsupported Usage of br_table

Error: (Failure"Invalid vstack when popping 2 values") Error: (Failure"Spec_inference.drop: not enough elements in stack") Error: (Failure"Spec_inference.take: not enough element in var list") Error: (Failure"unsupported in spec_inference: incompatible stack lengths (probably due to mismatches in br_table branches)")

Root Cause: Unreachable Code

Error: (Failure"Unsupported in slicing: cannot find an instruction. It probably is part of unreachable code.") Error: (Failure"bottom annotation") Error: (Failure"bottom annotation. this an unreachable instruction")

RQ5: Comparison to Slicing C Programs

For this RQ, we include the following data in the rq5.7z archive, and in the rq5/ directory in the docker image: - The slicing subjects in their C and textual wasm form in rq5/subjects/ - The CodeSurfer slices in their C and textual wasm form in rq5/codesurfer/ - Our slices in their wasm form in rq5/wasm-slices/

As this RQ requires heavy manual comparison, we do not expect the reviewers to reproduce all of our results. We include a summary of our manual investigation in rq5-manual.txt. In order to validate these manual findings, one can for example inspect a specific slice. For example, the following line in rq5-manual.txt:

adpcm_apl1_565_expr.c.wat INTERPROCEDURAL

can be validated as follows: ``` cd ~/

This generates a trimmed down version of the CodeSurfer slice, only containing the function of

SLA-Aware Slicing Market Outlook

According to our latest research, the SLA-Aware Slicing market size reached USD 1.48 billion globally in 2024, reflecting a robust momentum fueled by the ongoing digital transformation across industries. The market is forecasted to expand at a notable CAGR of 21.2% during the period from 2025 to 2033. By the end of 2033, the market is anticipated to attain a value of approximately USD 9.85 billion. This remarkable growth trajectory is driven by the increasing adoption of 5G networks, the proliferation of IoT devices, and the critical need for differentiated service levels tailored to diverse application requirements. The SLA-Aware Slicing market is experiencing a paradigm shift as organizations demand more granular control over network resources and service level agreements (SLAs) to meet evolving business and consumer expectations.

One of the primary growth factors propelling the SLA-Aware Slicing market is the rapid deployment of 5G technologies worldwide. 5G’s inherent capabilities, such as ultra-low latency, high throughput, and massive connectivity, have created a fertile ground for network slicing—a technique that enables the creation of multiple virtual networks atop a single physical infrastructure. SLA-aware slicing takes this a step further by embedding SLA guarantees into each slice, ensuring that mission-critical applications receive the necessary bandwidth, latency, and reliability. Industries such as healthcare, automotive, and manufacturing are leveraging these capabilities to enable advanced use cases like remote surgery, autonomous vehicles, and smart factories, thereby fueling market demand. As organizations strive to unlock the full potential of 5G, the need for intelligent, SLA-driven network management solutions is becoming increasingly pronounced.

Another significant driver of the SLA-Aware Slicing market is the exponential growth of IoT devices and applications. As billions of connected devices generate vast volumes of data and require varying levels of network performance, operators are under pressure to offer customized connectivity solutions. SLA-aware slicing empowers service providers to dynamically allocate network resources and enforce differentiated SLAs for diverse IoT applications, ranging from critical infrastructure monitoring to consumer wearables. This capability not only optimizes network efficiency but also enables new revenue streams through premium service offerings. Additionally, the rise of edge computing and private 5G networks is amplifying the need for SLA-aware slicing, as enterprises seek to deliver consistent, high-quality experiences across distributed environments.

The increasing emphasis on regulatory compliance and security is also shaping the growth trajectory of the SLA-Aware Slicing market. Governments and regulatory bodies are mandating stricter standards for data protection, network reliability, and service continuity, particularly in sectors such as finance, healthcare, and public safety. SLA-aware slicing enables organizations to meet these requirements by providing fine-grained control over network parameters, real-time monitoring, and automated remediation of SLA violations. This not only mitigates operational risks but also enhances customer trust and satisfaction. As digital ecosystems become more complex and interconnected, the ability to deliver assured network performance is emerging as a key differentiator for service providers and enterprises alike.

The emergence of Network Slicing Edge Appliance is revolutionizing how businesses approach network management and deployment. These appliances are designed to extend the capabilities of network slicing to the edge of the network, allowing for more efficient data processing and reduced latency. By bringing network slicing closer to the end-user, these appliances enable real-time data analytics and decision-making, which is crucial for applications such as autonomous vehicles and smart manufacturing. Furthermore, they provide enhanced security and privacy by processing sensitive data locally rather than transmitting it across the network. As the demand for edge computing grows, Network Slicing Edge Appliances are set to become a pivotal component in the architecture of next-generation networks, offering businesses the agility and performance needed to stay competitive in a rapi

Core Network Slicing Market Outlook

According to our latest research, the global Core Network Slicing market size is valued at USD 1.72 billion in 2024, reflecting a robust momentum driven by the proliferation of 5G deployments and the escalating need for network customization across various industries. The market is projected to expand at a CAGR of 32.1% from 2025 to 2033, reaching a forecasted market size of USD 21.3 billion by 2033. This remarkable growth is primarily attributed to the increasing adoption of advanced network architectures, dynamic service requirements, and the surge in data-intensive applications, which are propelling the demand for network slicing solutions globally.

One of the central growth factors for the Core Network Slicing market is the rapid roll-out of 5G networks worldwide. The deployment of 5G infrastructure is fundamentally transforming the telecommunications landscape, enabling operators to deliver highly differentiated services through network slicing. By allowing multiple virtual networks to operate on a shared physical infrastructure, core network slicing offers unprecedented flexibility, scalability, and efficiency. This capability is crucial for supporting diverse use cases such as enhanced mobile broadband, ultra-reliable low-latency communications, and massive IoT connectivity. As mobile operators and service providers strive to monetize their 5G investments, the implementation of core network slicing becomes a key enabler for delivering tailored services to various customer segments, thereby driving significant market growth.

Another pivotal driver is the increasing demand for enterprise digital transformation across sectors such as healthcare, manufacturing, and government. Organizations are seeking to leverage advanced connectivity solutions to optimize operations, improve productivity, and enhance customer experiences. Core network slicing empowers enterprises to deploy dedicated network slices with specific performance, security, and latency characteristics tailored to their unique requirements. This capability is particularly valuable in mission-critical applications, such as remote surgery, industrial automation, and smart city initiatives, where network reliability and customization are paramount. The growing recognition of these benefits is prompting enterprises to collaborate with network operators and solution providers, further accelerating the adoption of core network slicing technologies.

Moreover, the proliferation of IoT devices and the exponential growth in data traffic are intensifying the need for agile and efficient network management. Network slicing enables service providers to allocate resources dynamically and optimize network performance based on real-time demand. This is especially important as industries continue to embrace IoT-driven digital ecosystems, where billions of connected devices generate massive volumes of data. By leveraging core network slicing, operators can ensure seamless connectivity, enhanced security, and efficient resource utilization across diverse IoT applications. This growing trend is expected to fuel sustained investments in core network slicing solutions, reinforcing the market’s upward trajectory over the forecast period.

From a regional perspective, Asia Pacific is emerging as a dominant force in the Core Network Slicing market, driven by aggressive 5G roll-outs in countries like China, South Korea, and Japan. North America and Europe are also witnessing substantial growth, supported by strong investments in network infrastructure and a high concentration of technology-driven enterprises. Meanwhile, regions such as Latin America and the Middle East & Africa are gradually catching up, buoyed by strategic partnerships and government-led digitalization initiatives. The global landscape is characterized by a dynamic interplay of technological advancements, regulatory frameworks, and evolving end-user demands, which collectively shape the trajectory of the Core Network Slicing market.

Component Analysis

The Core Network Slicing market by component is broadly segmented into Solutions and Services. The solutions segment encompasses the software and platforms that enable the creation, management, and orchestration of network slices. These solutions are integral to the deployment of flexible, scalable, and secure network architectures required for next-generation connectivity. As the demand for customized and agile network services grows, solut

This is the replication package that accompanies the paper titled: "Dynamic Slicing of WebAssembly Binaries".

Slices dataset

Generating the dataset

The dynamic slices have been generated with P-ORBS. The steps and scripts to generate the dynamic slices are included in the slicing-steps/ directory. These steps also describe how to generate the stats.csv file that is included in this dataset.

The static slices have been generated with wassail. The scripts to generate the static slices are included in the current directory (generate-static-slices.sh which relies on run_wassail.sh). These steps generate the file static-stats.csv that is included in this dataset.

The original-size.csv file, included in this dataset, can be generated as follows: sh find subjects-wasm-extract-slice -name \*.c.wat -exec c_count {} \; | grep subjects > counts.txt echo 'slice,original fn slice' > evaluation/original-sizes.csv sed -E 's|^(.*) subjects-wasm-extract-slice/[^/]*/([^/]*)/.*$|\2,\1|' counts.txt >> evaluation/original-sizes.csv

The numbers in Table 1 of the paper (the list of programs in the dataset along with their sizes) can be generated as follows. For the WebAssembly files, we can count the function size:

find subjects-wasm-extract-slice -name \*.c.wat -exec c_count {} \; | grep subjects > counts.txt
sed -E 's|^(.*) subjects-wasm-extract-slice/([^/]*)/.*$|\1 \2|' counts.txt | python evaluation/table1-wasm-mean.py

or the full program size:

find subjects-wasm-extract-slice -name t.wat -exec c_count {} \; | grep subjects > counts.txt
sed -E 's|^(.*) subjects-wasm-extract-slice/([^/]*)/.*$|\1 \2|' counts.txt | python evaluation/table1-wasm-mean.py

Structure of the dataset

The dataset is structured as follows:

• subjects/ contains the instrumented .c source code, along with scripts to generate the dynamic slices. The original source code can be obtained by removing the line printf(" ORBS:%x .....
• subjects-wasm-extract-slice/ contains the original WebAssembly programs to slice. Each program has two files: t.wat is the full binary file, and name.c.wat is the binary code of the function containing the slicing criterion.
• all_slices/ contains the slices. For example, program adpcm_ah1_254_expr has the following files
  • adpcm/adpcm_ah1_254_expr/EWS_adpcm.wat: the EWS slice
  • adpcm/adpcm_ah1_254_expr/SEW_adpcm.wat: the SEW slice
  • adpcm/adpcm_ah1_254_expr/ESW_adpcm.wat: the ESW slice
  • adpcm/adpcm_ah1_254_expr/static_adpcm.wat.slice: the SWS slice The other files are produced by intermediary steps and can be ignored. They are:
  • adpcm/adpcm_ah1_254_expr/ESW_adpcm.wat.orig: original (unsliced) binary file from which SW and ESW slices are computed
  • adpcm/adpcm_ah1_254_expr/SEW_adpcm.wat.orig: original (unsliced) binary function from which SEW slice is computed
  • adpcm/adpcm_ah1_254_expr/SW_adpcm.wat: slice of entire binary file from which ESW slice is extracted
  • adpcm/adpcm_ah1_254_expr/WS_adpcm.wat: compiled (binary) version of dynamic C slice from which EWS slice is extracted

Research questions

RQ1

The script ./RQ1.py found in the evaluation/ directory generates:

• Figure 3 (time.pdf)
• The mean, min, max, and stddev of the times
• How many slices are computed below 10, 100, 1000, and 10000 seconds

RQ2

The script ./RQ2.py found in the evaluation/ directory generates:

• Figure 4 (loc.pdf)
• The mean, median, min, max, and stddev of the sizes
• The largest differences between the approaches
• The number of slices larger than the original program

RQ3 and RQ4

The process for these research questions is manual and requires comparing slices. It cannot be automated.
We did make heavy use of diff --side-by-side in this analysis.

Slicing of genomics data according to chromosome types

Fifth Generation Network Slicing Datasets

  Dataset Description

Per-slice traffic, latency, and throughput metrics for 5G network slices

  Dataset Information

Category: Emerging and Advanced Format: CSV, Parquet Rows: 300,000 Columns: 14 Date Generated: 2025-10-05 Location: data/fifth_generation_network_slicing_datasets/

  Schema

Column Type Sample Values

slice_id String SLC00000001

timestamp Datetime 2025-09-17 19:54:00

tower_id String… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/nigerian-telecom-fifth-generation-network-slicing-datasets.

Global trade data of Slicing machines under 8438500000, 8438500000 global trade data, trade data of Slicing machines from 80+ Countries.

The size of the Wireless Slicing market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX% during the forecast period.

Private 5G Network Slicing for Yards Market Outlook

According to our latest research, the global Private 5G Network Slicing for Yards market size reached USD 1.54 billion in 2024 and is expected to expand at a robust CAGR of 28.1% from 2025 to 2033. By 2033, the market is forecasted to reach USD 12.6 billion, driven by the accelerated adoption of digital transformation initiatives across logistics, manufacturing, and transportation industries. The surge in demand for ultra-reliable, low-latency connectivity and the need for highly customized, secure communication networks for critical yard operations are among the primary growth factors fueling this market.

The growth of the Private 5G Network Slicing for Yards market is underpinned by the increasing complexity and scale of modern yard operations, where traditional connectivity solutions often fall short. With the proliferation of IoT devices, autonomous vehicles, and real-time monitoring systems, yards such as ports, rail yards, and logistics hubs require tailored network slices to ensure seamless, uninterrupted, and secure data transfer. The ability of private 5G network slicing to allocate dedicated resources for specific applications—such as automated guided vehicles (AGVs), real-time asset tracking, and remote equipment control—enables operational efficiency, minimizes downtime, and enhances overall safety. As businesses continue to digitize and automate their yard processes, the demand for network slicing capabilities that can support mission-critical applications with guaranteed bandwidth and latency is expected to rise exponentially.

Another significant growth driver in the Private 5G Network Slicing for Yards market is the tightening regulatory landscape and growing emphasis on data privacy and security. Industries operating in yards and terminals handle sensitive information, ranging from cargo manifests to industrial control data. Private 5G network slicing empowers organizations to create isolated, secure virtual networks for different operational segments, mitigating the risks of cyber threats and data breaches. Furthermore, the technology’s scalability and flexibility make it possible to rapidly adapt to changing operational requirements, such as scaling up connectivity for peak periods or integrating new digital solutions without disrupting ongoing activities. This adaptability is particularly valuable for industries facing fluctuating demand and evolving regulatory requirements.

The evolution of Industry 4.0 and the rapid integration of artificial intelligence, machine learning, and edge computing into yard operations are also propelling the Private 5G Network Slicing for Yards market. Companies are leveraging these technologies to enable predictive maintenance, optimize asset utilization, and enhance decision-making through real-time analytics. Private 5G network slicing provides the robust, low-latency infrastructure necessary to support these advanced applications, ensuring that data is transmitted and processed with minimal delay. As a result, organizations can achieve higher productivity, reduce operational costs, and gain a competitive edge in an increasingly digitalized landscape.

Regionally, the Asia Pacific region is leading the adoption of Private 5G Network Slicing for Yards, accounting for the largest market share in 2024. This dominance is attributed to significant investments in smart port projects, rapid industrialization, and government initiatives aimed at enhancing logistics and transportation infrastructure. North America and Europe are also witnessing substantial growth, driven by the presence of major technology providers, early adoption of 5G technologies, and strong focus on automation and digital transformation in logistics and manufacturing sectors. Meanwhile, the Middle East & Africa and Latin America are emerging as promising markets, supported by rising investments in port modernization and transportation infrastructure development.

Component Analysis

The component seg