Abstract The Domain Name System (DNS) is a critical foundation of internet functionality, yet its reliability remains vulnerable to physical, logical, and operational failures. While substantial research exists on DNS security and topology, large scale assessments of country code top level domains (ccTLDs) remain limited. This study presents a comprehensive empirical analysis of DNS reliability and web reachability within the German .de domain ecosystem. Utilizing... The Domain Name System (DNS) is a critical foundation of internet functionality, yet its reliability remains vulnerable to physical, logical, and operational failures. While substantial research exists on DNS security and topology, large scale assessments of country code top level domains (ccTLDs) remain limited. This study presents a comprehensive empirical analysis of DNS reliability and web reachability within the German .de domain ecosystem. Utilizing a stratified 10% random sample (N = 636,853) from a verified inventory of 6.38 million. de domains (derived from Common Crawl data spanning 2019-2026), we evaluated sequential DNS resolution and HTTP/HTTPS accessibility. Descriptive and inferential statistical methods, along with a novel DNS Reliability Index (DRI), were used to quantify operational health. Results indicate a DNS resolution rate of 66.24% (95% CI [66.12%, 66.37%]), but a significant attrition to full web reachability, with only 35.42% of the total sample (or ~42.2% of actively checked domains) returning successful HTTP 200 responses. DNS failures (ERR_DNS, 28.36%) and network timeouts (ERR_TIMEOUT, 11.94%) emerged as the primary barriers to accessibility. The DRI remained stable (~66.2%) across domain name lengths, though temporal anomalies highlighted batch specific probing effects. These findings Shown that domain registration does not guarantee service availability, highlighting the need for continuous monitoring and improved registration maintenance to enhance the resilience of the web ecosystem. Read More Read Less

Abstract Cloud-rendered Augmented Reality (AR) and Extended Reality (XR) offload intensive computational tasks from lightweight head mounted displays to the cloud, enabling high fidelity immersive experiences. However, this paradigm imposes stringent Quality of Service (QoS) requirements, including ultra low latency, high throughput, and minimal jitter, to prevent cybersickness and preserve the sense of presence. While high level network requirements for XR are well documented, there... Cloud-rendered Augmented Reality (AR) and Extended Reality (XR) offload intensive computational tasks from lightweight head mounted displays to the cloud, enabling high fidelity immersive experiences. However, this paradigm imposes stringent Quality of Service (QoS) requirements, including ultra low latency, high throughput, and minimal jitter, to prevent cybersickness and preserve the sense of presence. While high level network requirements for XR are well documented, there is a distinct lack of empirical, packet level traffic characterization necessary for accurate network modeling, simulation, and resource allocation in 5G/6G environments. This paper presents a comprehensive empirical characterization of cloud rendered AR network traffic. Utilising real world packet level traces from the IEEE DataPort dataset alongside synthetic validation, we analyse critical traffic features, including packet size distributions, latency, jitter, and packet loss rates. Our analysis reveals a multimodal, heavy tailed packet size distribution driven by low latency H.264 encoding and UDP packetization. Furthermore, we demonstrate a near normal latency distribution contrasted with a right-skewed jitter profile, exhibiting a moderate positive correlation that highlights shared underlying network congestion causes. Packet loss remains exceptionally low under stable conditions but exhibits sporadic bursts that threaten video decoding. These statistical insights provide a foundational baseline for developing realistic AR traffic models, optimizing network slicing, and designing QoS-aware scheduling policies to sustain the stringent Quality of Experience (QoE) demands of next generation immersive applications. Read More Read Less

Abstract The proliferation of online employer review platforms has generated vast amounts of Big Data, offering new avenues for labor market analytics. However, the relationship between an organization's digital visibility and its actual workplace quality remains underexplored. This study investigates the "Visibility Satisfaction Paradox" within the Indian labor market, examining whether employer popularity operationalized through web review volume translates into higher employee satisfaction. Utilizing a... The proliferation of online employer review platforms has generated vast amounts of Big Data, offering new avenues for labor market analytics. However, the relationship between an organization's digital visibility and its actual workplace quality remains underexplored. This study investigates the "Visibility Satisfaction Paradox" within the Indian labor market, examining whether employer popularity operationalized through web review volume translates into higher employee satisfaction. Utilizing a cross-sectional, observational design, we analyzed web scraped data comprising 1,760 companies. To address the severe right skewness inherent in user generated engagement metrics, we employed log-transformations, Kernel Density Estimation (KDE), and both parametric and non parametric correlation analyses. The findings reveal a near total decoupling of employer visibility and employee satisfaction; while the negative association between review volume and average ratings is statistically significant (Pearson's r = —0.054,  p 0.02), the effect size is practically negligible (R²  0.0029). Results highlight a "winner take most" dynamic where mega employers dominate platform visibility without necessarily securing superior satisfaction scores. This study challenges the heuristic that popular employers are inherently better workplaces, offering critical implications for job seekers, HR practitioners, and the algorithmic design of employment review platforms. Read More Read Less