This data set contains the result of applying the NIST Statistical Test Suite on accelerometer data processed for random number generator seeding. The NIST Statistical Test Suite can be downloaded from: http://csrc.nist.gov/groups/ST/toolkit/rng/documentation_software.html. The format of the output is explained in http://csrc.nist.gov/publications/nistpubs/800-22-rev1a/SP800-22rev1a.pdf.

* Repository author: Tommaso Zanotti
* email: tommaso.zanotti@unimore.it or francescomaria.puglisi@unimore.it
* Version v1.0

This repository includes MATLAB files and datasets related to the IEEE IIRW 2023 conference proceeding:
T. Zanotti et al., "Reliability Analysis of Random Telegraph Noisebased True Random Number Generators," 2023 IEEE International Integrated Reliability Workshop (IIRW), South Lake Tahoe, CA, USA, 2023, pp. 1-6, doi: 10.1109/IIRW59383.2023.10477697

The repository includes:

• The data of the bitmaps reported in Fig. 4, i.e., the results of the simulation of the ideal RTN-based TRNG circuit for different reseeding strategies.
  To load and plot the data use the "plot_bitmaps.mat" file.
• The result of the circuit simulations considering the EvolvingRTN from the HfO2 device shown in Fig. 7, for two Rgain values. Specifically, the data is contained in the following csv files:
  • "Sim_TRNG_Circuit_HfO2_3_20s_Vth_210m_no_Noise_Ibias_11n.csv" (lower Rgain)
  • "Sim_TRNG_Circuit_HfO2_3_20s_Vth_210m_no_Noise_Ibias_4_8n.csv" (higher Rgain)
• The result of the circuit simulations considering the temporary RTN from the SiO2 device shown in Fig. 8. Specifically, the data is contained in the following csv files:
  • "Sim_TRNG_Circuit_SiO2_1c_300s_Vth_180m_Noise_Ibias_1.5n.csv" (ref. Rgain)
  • "Sim_TRNG_Circuit_SiO2_1c_100s_200s_Vth_180m_Noise_Ibias_1.575n.csv" (lower Rgain)
  • "Sim_TRNG_Circuit_SiO2_1c_100s_200s_Vth_180m_Noise_Ibias_1.425n.csv" (higher Rgain)

The global Rack Random Number Generator market is projected to witness significant expansion, reaching an estimated market size of XXX million USD by 2025, with a Compound Annual Growth Rate (CAGR) of XX% during the forecast period of 2025-2033. This robust growth is primarily fueled by the escalating demand for highly secure and unpredictable random numbers across various critical sectors. The burgeoning field of quantum computing, with its inherent reliance on true randomness for complex calculations and simulations, stands as a principal driver. Furthermore, the increasing adoption of advanced cryptographic protocols in cybersecurity, financial services, and governmental applications necessitates reliable and tamper-proof random number generation. The integration of quantum-based random number generators (QRNGs) into industrial processes, particularly in areas requiring unbiased data for machine learning algorithms and statistical analysis, is also a substantial contributor to market momentum. The need for enhanced data privacy and compliance with stringent regulations surrounding data security further bolsters the market for these sophisticated devices. The market is segmented into diverse applications, with Scientific Research and Industrial Use emerging as dominant segments due to their substantial investment in advanced technologies and the critical need for high-quality randomness. Entertainment, while a smaller segment, is also showing promise with the integration of RNGs in gaming and simulation technologies. In terms of types, both Portable and Integrated Generator segments are expected to experience growth, catering to different deployment needs and cost considerations. Geographically, Asia Pacific, led by China and India, is poised to be a rapidly expanding market, driven by rapid technological adoption and significant government initiatives in quantum technology research. North America and Europe, already established markets with a strong focus on cybersecurity and R&D, will continue to be major revenue contributors, with the United States and Germany leading the way. Emerging economies in South America and the Middle East & Africa are also anticipated to witness increasing adoption rates as awareness and investment in advanced RNG solutions grow.

The size of the Card Random Number Generator 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.

This is a JSON format file generated by a random number generator in python. The range is 0 to 1000, and numbers are float number.This data will be used by a python script for further transformation.

Analysis of sensor-based data processed for Random Number Generator seeding.

The Hardware Random Number Generator (HRNG) chip market is experiencing robust growth, driven by increasing demand for secure and reliable randomness in various applications. The market's expansion is fueled by the rising adoption of cryptography, especially in sectors like data security, IoT devices, and cloud computing, where unpredictable numbers are crucial for encryption and authentication. Government regulations mandating stronger security measures across different industries further bolster market growth. Trends point towards miniaturization of HRNG chips, integration with other security components on a single chip, and the development of more energy-efficient designs. While the market size in 2025 is difficult to estimate precisely without specific figures, considering a conservative CAGR of 15% (a common growth rate for specialized semiconductor markets) and assuming a 2025 market value of $250 million, we can project significant expansion. This growth is projected to continue through 2033, propelled by advancements in quantum-resistant cryptography and increasing sophistication in cybersecurity threats. Companies like ID Quantique, QuantumCTek, and others are leading the charge in innovation, expanding their product lines, and entering new markets. The key restraints to market growth include the relatively high cost of HRNG chips compared to pseudo-random number generators (PRNGs), as well as the need for extensive testing and validation to ensure the true randomness and security of the generated numbers. However, the increasing awareness of security risks and the rising value of data are overcoming these limitations. The market is segmented by chip type (e.g., true random number generator, quantum random number generator), application (e.g., data encryption, gaming, financial transactions), and end-user industry (e.g., aerospace, defense, healthcare). Regional variations in adoption rates will likely exist, with North America and Europe currently leading the market, followed by Asia-Pacific regions experiencing rapid growth. The forecast period of 2025-2033 promises exciting developments in HRNG technology, with continued innovation driving significant market expansion.

Title of program: SRNG Catalogue Id: ACIE_v1_0

Nature of problem An algorithm to generate a subset of random integers from a larger set of integers has been developed to minimize both the computing time and the memory space. The algorithm deals with the whole array at the same time, generates a subset of random integers for a given percentage of the range, and the generated subset of random integers is obtained as sequentially increasing numbers. Such random integers are used to specify thepositions of randomly dispersed impurity atoms or solute atoms in many ...

Versions of this program held in the CPC repository in Mendeley Data ACIE_v1_0; SRNG; 10.1016/0010-4655(76)90065-5

This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)

Explore the surging Hardware Random Number Generator Chips market forecast, driven by cybersecurity needs and IoT growth. Discover market size, CAGR, key drivers, and regional trends from 2025-2033.

Abstract A library containing highly portable implementations of most algorithms for (pseudo) random number generation has been developed, which might be used in any area of simulation which requires random number generators. Each generator is freely configurable by the user, so the RANEXP library is particularly well-suited for applications requiring different random number generators. The algorithms are implemented in C, but are callable from Fortran application program also. Title of program: RANEXP Catalogue Id: ACTB_v1_0 Nature of problem Any Monte Carlo simulation or statistical test requiring uniform pseudorandom numbers. Versions of this program held in the CPC repository in Mendeley Data ACTB_v1_0; RANEXP; 10.1016/0010-4655(94)90072-8 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)

The Hardware Random Number Generator (HRNG) market is experiencing robust growth, driven by increasing demand for secure and reliable randomness in various applications. The market size in 2025 is estimated at $250 million, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant growth is fueled by the rising adoption of cloud computing, the Internet of Things (IoT), and blockchain technologies, all of which require high-quality, unpredictable random numbers for security and cryptographic operations. Furthermore, stringent government regulations regarding data privacy and security are driving the demand for robust HRNG solutions. Key players like Synopsys, ID Quantique, Quside, Intel, Xiphera, Shanghai XT QUANTECH, and QuantumCTek are contributing to market expansion through technological advancements and strategic partnerships. The market's growth trajectory is expected to continue its upward trend throughout the forecast period (2025-2033), propelled by factors such as the increasing sophistication of cyberattacks and the growing need for secure authentication and encryption. However, challenges like high initial investment costs for HRNG implementation and the potential for vulnerabilities in poorly designed systems could act as restraints. Nevertheless, the long-term outlook remains positive, with significant opportunities for market expansion across various sectors including finance, healthcare, automotive, and defense, where data security is paramount. The increasing integration of HRNGs into diverse devices and systems is a major contributor to this promising future.

The Hardware Random Number Generator (HRNG) market is booming, projected to reach $1.8 billion by 2033 with a 15% CAGR. Driven by cloud computing, IoT, and stringent security regulations, this report analyzes market size, key players (Synopsys, ID Quantique, etc.), and regional trends. Discover the opportunities and challenges in this rapidly evolving sector.

The global Hardware Random Number Generator (HRNG) Chips market is poised for substantial expansion, driven by the escalating demand for robust security solutions across diverse industries. With a projected market size estimated to reach approximately $750 million by 2025, the market is expected to experience a Compound Annual Growth Rate (CAGR) of around 18% from 2025 to 2033. This robust growth is fueled by the critical need for true randomness in applications such as cybersecurity, cryptography, secure communication, and the burgeoning fields of artificial intelligence and quantum computing. The increasing sophistication of cyber threats necessitates the deployment of highly secure and unpredictable random number generation, making HRNG chips indispensable. Furthermore, the proliferation of connected devices and the Internet of Things (IoT) further amplifies this demand, as each connected node requires secure authentication and data encryption. The automotive sector, with its increasing reliance on connected and autonomous systems, is emerging as a significant application area, as is the financial terminal industry for secure transaction processing. The market is segmented into low-speed, medium-speed, and high-speed random number chips, catering to a spectrum of performance requirements. High-speed chips are gaining traction for demanding applications like large-scale data encryption and complex simulations. Key market drivers include the growing awareness of data privacy regulations globally, the advancements in quantum technologies, and the continuous innovation in semiconductor manufacturing enabling more efficient and cost-effective HRNG chip production. However, challenges such as the relatively high initial cost of implementation for some advanced HRNG solutions and the availability of software-based pseudo-random number generators (PRNGs) might pose some restraints. Prominent players like ID Quantique, Qrange, and Quside are at the forefront of innovation, introducing advanced HRNG solutions. Geographically, Asia Pacific, particularly China and India, is expected to witness the fastest growth due to rapid industrialization, increasing adoption of advanced technologies, and a burgeoning cybersecurity market. North America and Europe remain significant markets, driven by stringent security standards and a mature technology ecosystem.

Quantum Random Number Generator Appliance Market Outlook

According to our latest research, the quantum random number generator appliance market size reached USD 195.2 million globally in 2024, demonstrating a robust upward trend driven by escalating security needs and advancements in quantum technologies. The market is projected to expand at a CAGR of 31.8% from 2025 to 2033, reaching a forecasted value of USD 2.16 billion by the end of the forecast period. This remarkable growth is fueled by heightened demand for cryptographically secure random numbers in critical sectors such as BFSI, government, and telecommunications, as well as increased adoption of quantum-safe solutions to counter evolving cyber threats.

One of the primary growth factors for the quantum random number generator appliance market is the rising prevalence of sophisticated cyberattacks targeting both public and private sectors. As digital transformation accelerates globally, organizations are increasingly vulnerable to threats that can compromise sensitive data and digital infrastructure. Quantum random number generator appliances provide a fundamentally secure method of generating truly random numbers, which are essential for cryptographic protocols and secure communications. The growing awareness among enterprises regarding the limitations of classical random number generators, which are susceptible to prediction and manipulation, is further driving the adoption of quantum-based solutions. This shift is particularly notable in industries such as banking, financial services, and insurance (BFSI), where data integrity and confidentiality are paramount.

Another significant driver is the rapid evolution of quantum computing and its implications for conventional encryption techniques. As quantum computers become more powerful, traditional encryption algorithms are at risk of being rendered obsolete. Quantum random number generator appliances are emerging as a key element in the development of quantum-safe cryptographic systems, ensuring robust protection against future quantum-enabled attacks. Governments and regulatory bodies across various regions are increasingly mandating the use of advanced cryptographic methods, which is accelerating the integration of quantum random number generators into existing security frameworks. Additionally, the proliferation of Internet of Things (IoT) devices and the need for secure machine-to-machine communication are further expanding the addressable market for these appliances.

The expanding application landscape of quantum random number generator appliances is another crucial growth catalyst. Beyond cryptography, these devices are finding use in scientific research, gaming and gambling, and secure data transmission in IoT networks. For example, the gaming industry relies on high-quality random numbers to ensure fairness and unpredictability in online games and lotteries. Similarly, scientific research, particularly in fields such as simulations and statistical modeling, benefits from the enhanced randomness provided by quantum solutions. The versatility and reliability of quantum random number generator appliances are positioning them as indispensable tools across a wide array of sectors, further propelling global market growth.

From a regional perspective, North America currently dominates the quantum random number generator appliance market, accounting for the largest share in 2024. This leadership is attributed to the presence of major technology providers, strong government support for quantum research, and early adoption of advanced cybersecurity solutions across industries. Europe follows closely, driven by stringent data protection regulations and significant investments in quantum technologies. The Asia Pacific region is expected to witness the fastest growth during the forecast period, fueled by rapid digitalization, expanding IT infrastructure, and increasing awareness about quantum security. Latin America and the Middle East & Africa are also showing promising potential, albeit from a smaller base, as organizations in these regions begin to recognize the importance of quantum-safe security solutions.

Type Analysis

The quantum random number generator appliance market is segmented by type into hardware, software, and services. Hardware-based quantum random number generators have traditionally dominated the market due to their ability to generate truly random numbers by harnessing quantum mec

Quantum Random Number Generator Market Outlook

As per our latest research, the Quantum Random Number Generator (QRNG) market size reached USD 185 million in 2024 globally, reflecting a robust interest in quantum-based security solutions and randomization technologies. The market is experiencing a strong growth trajectory with a CAGR of 31.7% from 2025 to 2033, driven by the rising demand for ultra-secure cryptographic systems, advancements in quantum technologies, and the proliferation of IoT devices. At this impressive growth rate, the Quantum Random Number Generator market is forecasted to reach USD 2.16 billion by 2033. The surge in digital threats and the need for next-generation security infrastructure are primary factors propelling this marketÂ’s expansion.

The primary growth factor for the Quantum Random Number Generator market is the escalating need for high-quality randomness in cryptographic applications. As cyber threats become more sophisticated, traditional pseudo-random number generators are increasingly vulnerable to attacks, making quantum-based solutions essential for ensuring data integrity and privacy. QRNGs leverage the inherent unpredictability of quantum phenomena, providing true randomness that is virtually impossible to predict or replicate. This capability is particularly vital for sectors such as banking, government, and defense, where the consequences of compromised encryption can be catastrophic. The integration of QRNGs into existing security protocols is being prioritized by organizations seeking to future-proof their digital infrastructure against emerging quantum computing threats.

Another significant driver is the rapid advancement and commercialization of quantum technology. With increasing investments from both public and private sectors, quantum research has transitioned from theoretical exploration to practical deployment. The miniaturization of quantum hardware and the development of software-based QRNGs have made these solutions more accessible and cost-effective. Furthermore, the growing ecosystem of quantum startups and collaborations with established tech giants are accelerating product innovation and market penetration. This environment fosters the development of QRNGs that are not only more efficient but also compatible with a wide range of applications, from secure communications to scientific simulations.

The proliferation of IoT and connected devices is also fueling the demand for quantum random number generators. As billions of devices become interconnected, the attack surface for cybercriminals expands, necessitating stronger security measures. QRNGs offer a scalable solution for embedding high-entropy randomness into devices, ensuring secure authentication, data transmission, and system integrity. Industries such as healthcare, smart infrastructure, and autonomous vehicles are increasingly adopting quantum randomization technologies to safeguard sensitive data and maintain regulatory compliance. The convergence of quantum technology with IoT is expected to be a pivotal trend shaping the future of the QRNG market.

Regionally, North America continues to lead the Quantum Random Number Generator market due to its early adoption of quantum technologies, robust cybersecurity infrastructure, and significant government and private sector investments. Europe follows closely, propelled by stringent data protection regulations and a strong focus on research and development. The Asia Pacific region is witnessing the fastest growth, driven by expanding digital economies, government initiatives in quantum research, and the increasing deployment of IoT devices. Latin America and the Middle East & Africa are gradually catching up, supported by digital transformation agendas and growing awareness of quantum security solutions.

Type Analysis

The Type segment of the Quantum Random Number Generator market is primarily categorized into hardware and software soluti

According to Cognitive Market Research, the global Quantum Random Number Generator RNG market size is USD 555.9 million in 2024. It will expand at a compound annual growth rate (CAGR) of 72.60% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 222.36 million in 2024 and will grow at a compound annual growth rate (CAGR) of 70.8% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 166.77 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 127.86 million in 2024 and will grow at a compound annual growth rate (CAGR) of 74.6% from 2024 to 2031.
Latin America had a market share for more than 5% of the global revenue with a market size of USD 27.80 million in 2024 and will grow at a compound annual growth rate (CAGR) of 72.0% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 11.12 million in 2024 and will grow at a compound annual growth rate (CAGR) of 72.3% from 2024 to 2031.
Cloud held the dominant segment in the Quantum Random Number Generator RNG market in 2024.

Market Dynamics of Quantum Random Number Generator RNG Market

Key Drivers for Quantum Random Number Generator RNG Market

Increasing need for random numbers in cryptography or compute applications

The QRNG is an ideal random key generator since it generates entropy using intrinsic quantum physics properties. Nowadays, applications demand a huge number of keys and randomization to achieve total security. It could include key vaults, games, IoT devices, AI/ML, blockchains, simulations, and vital infrastructure. QRNG is the source of these applications in which trust in randomness is prevalent. Furthermore, it is utilized in encryption for a wide range of applications, including cryptography, numerical simulation, gambling, and game design.

Growing adoption of quantum computing

The increasing use of quantum computing is boosting the market for Quantum Random Number Generators (RNG) as it creates a need for improved random number generation capabilities. The accurate abilities of quantum computing enable RNGs to produce truly random numbers, essential for secure communication and encryption. Advancements in quantum computing will lead to a higher demand for dependable RNGs, driving market expansion to meet the changing requirements of cybersecurity and data encryption.

Restraint Factor for the Quantum Random Number Generator RNG Market

High initial investment

A significant initial investment hinders the Quantum Random Number Generator (RNG) Market, creating a barrier for new entrants and small companies looking to invest in RNG generation. The significant initial costs involved in the research, development, and deployment of quantum RNG solutions may discourage potential entrants from joining the market. This limitation impedes the growth of the market by limiting innovation and competition, potentially hindering progress in the era of RNG and constraining the market's growth

Impact of Covid-19 on the Quantum Random Number Generator RNG Market

The effect of COVID-19 on the Quantum Random Number Generator RNG Market was merged. Although the pandemic initially caused disruptions in supply chains and slowed down certain trends, the increased focus on cybersecurity and data protection during remote work and digital interactions enhanced the need for secure communication solutions such as quantum RNGs. With a focus on safeguarding information, both organizations and governments fueled growth in the Quantum RNG market despite pandemic-related obstacles. Introduction of the Quantum Random Number Generator RNG Market

The Quantum Random Number Generator (QRNG) is a highly sophisticated engineering innovation that combines the power of complex deep-tech technologies like semiconductors, optoelectronics, high-precision electronics, and quantum physics to achieve the highest level of randomness possible. QRNG has shown to be a critical enabling technology for quantum-level security in mobile devices, data centres, and medical implants. They provide consumers with a significant enhancement over ordinary random number generators (RNGs), which have been utilized for years in a variety of business applications. Several factors, including th...

Creating a robust employee dataset for data analysis and visualization involves several key fields that capture different aspects of an employee's information. Here's a list of fields you might consider including: Employee ID: A unique identifier for each employee. Name: First name and last name of the employee. Gender: Male, female, non-binary, etc. Date of Birth: Birthdate of the employee. Email Address: Contact email of the employee. Phone Number: Contact number of the employee. Address: Home or work address of the employee. Department: The department the employee belongs to (e.g., HR, Marketing, Engineering, etc.). Job Title: The specific job title of the employee. Manager ID: ID of the employee's manager. Hire Date: Date when the employee was hired. Salary: Employee's salary or compensation. Employment Status: Full-time, part-time, contractor, etc. Employee Type: Regular, temporary, contract, etc. Education Level: Highest level of education attained by the employee. Certifications: Any relevant certifications the employee holds. Skills: Specific skills or expertise possessed by the employee. Performance Ratings: Ratings or evaluations of employee performance. Work Experience: Previous work experience of the employee. Benefits Enrollment: Information on benefits chosen by the employee (e.g., healthcare plan, retirement plan, etc.). Work Location: Physical location where the employee works. Work Hours: Regular working hours or shifts of the employee. Employee Status: Active, on leave, terminated, etc. Emergency Contact: Contact information of the employee's emergency contact person. Employee Satisfaction Survey Responses: Data from employee satisfaction surveys, if applicable.

Code Url: https://github.com/intellisenseCodez/faker-data-generator

An integrated circuit (1..1''',1a..1c) with a true random number generator (2..2'''), which comprises at least one instable physical uncloneable function (3..3''',3a,3a') for generating true random numbers (8). Hence each device of a group of devices can be provided with a unique true random generator so that each device of the group is provided with different true random numbers even when said devices are applied to identical environmental conditions. Such a random number generator may be part of a smart card as well as of a module for near field communication for example.

Excel sheets in order: The sheet entitled “Hens Original Data” contains the results of an experiment conducted to study the response of laying hens during initial phase of egg production subjected to different intakes of dietary threonine. The sheet entitled “Simulated data & fitting values” contains the 10 simulated data sets that were generated using a standard procedure of random number generator. The predicted values obtained by the new three-parameter and conventional four-parameter logistic models were also appeared in this sheet. (XLSX)

Artifacts for the paper titled Root Cause Analysis for Microservice System based on Causal Inference: How Far Are We?.

This artifact repository contains 9 compressed folders, as follows:

ID File Name Description

1 syn_circa.zip CIRCA10, and CIRCA50 datasets for Causal Discovery

2 syn_rcd.zip RCD10, and RCD50 datasets for Causal Discovery

3 syn_causil.zip CausIL10, and CausIL50 datasets for Causal Discovery

4 rca_circa.zip CIRCA10, and CIRCA50 datasets for RCA

5 rca_rcd.zip RCD10, and RCD50 datasets for RCA

6 online-boutique.zip Online Boutique dataset for RCA

7 sock-shop-1.zip Sock Shop 1 dataset for RCA

8 sock-shop-2.zip Sock Shop 2 dataset for RCA

9 train-ticket.zip Train Ticket dataset for RCA

Each zip file contains the generated/collected data from the corresponding data generator or microservice benchmark systems (e.g., online-boutique.zip contains metrics data collected from the Online Boutique system).

Details about the generation of our datasets

1. Synthetic datasets

We use three different synthetic data generators from three previous RCA studies [15, 25, 28] to create the synthetic datasets: CIRCA, RCD, and CausIL data generators. Their mechanisms are as follows:1. CIRCA datagenerator [28] generates a random causal directed acyclic graph (DAG) based on a given number of nodes and edges. From this DAG, time series data for each node is generated using a vector auto-regression (VAR) model. A fault is injected into a node by altering the noise term in the VAR model for two timestamps. 2. RCD data generator [25] uses the pyAgrum package [3] to generate a random DAG based on a given number of nodes, subsequently generating discrete time series data for each node, with values ranging from 0 to 5. A fault is introduced into a node by changing its conditional probability distribution.3. CausIL data generator [15] generates causal graphs and time series data that simulate the behavior of microservice systems. It first constructs a DAG of services and metrics based on domain knowledge, then generates metric data for each node of the DAG using regressors trained on real metrics data. Unlike the CIRCA and RCD data generators, the CausIL data generator does not have the capability to inject faults.To create our synthetic datasets, we first generate 10 DAGs whose nodes range from 10 to 50 for each of the synthetic data generators. Next, we generate fault-free datasets using these DAGs with different seedings, resulting in 100 cases for the CIRCA and RCD generators and 10 cases for the CausIL generator. We then create faulty datasets by introducing ten faults into each DAG and generating the corresponding faulty data, yielding 100 cases for the CIRCA and RCD data generators. The fault-free datasets (e.g. syn_rcd, syn_circa) are used to evaluate causal discovery methods, while the faulty datasets (e.g. rca_rcd, rca_circa) are used to assess RCA methods.

1. Data collected from benchmark microservice systems

We deploy three popular benchmark microservice systems: Sock Shop [6], Online Boutique [4], and Train Ticket [8], on a four-node Kubernetes cluster hosted by AWS. Next, we use the Istio service mesh [2] with Prometheus [5] and cAdvisor [1] to monitor and collect resource-level and service-level metrics of all services, as in previous works [ 25 , 39, 59 ]. To generate traffic, we use the load generators provided by these systems and customise them to explore all services with 100 to 200 users concurrently. We then introduce five common faults (CPU hog, memory leak, disk IO stress, network delay, and packet loss) into five different services within each system. Finally, we collect metrics data before and after the fault injection operation. An overview of our setup is presented in the Figure below.

Code

The code to reproduce the experimental results in the paper is available at https://github.com/phamquiluan/RCAEval.

References

As in our paper.