Technical Report

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\fancyhead[L]{\small\color{techgray}NovaCast CDN --- Technical Report}
\fancyhead[R]{\small\color{techgray}TR-2026-003 Rev 1.2}
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\begin{titlepage}
\centering
\vspace*{1.5cm}
{\color{techblue}\rule{\textwidth}{2pt}}\\[20pt]
{\Huge\bfseries\color{techblue} Technical Report}\\[8pt]
{\LARGE\color{techblue} NovaCast Content Delivery Network}\\[4pt]
{\Large\color{techblue} Architecture, Performance Analysis, and\\Optimization Recommendations}\\[20pt]
{\color{techblue}\rule{\textwidth}{2pt}}\\[30pt]
\begin{tabular}{ll}
  \textbf{Document ID:} & TR-2026-003 \\
  \textbf{Revision:} & 1.2 \\
  \textbf{Date:} & February 1, 2026 \\
  \textbf{Classification:} & Internal --- Engineering \\
  \textbf{Authors:} & Kevin Zhao, Principal Systems Architect \\
  & Diana Petrov, Senior Performance Engineer \\
  & Samuel Obi, Network Operations Lead \\
  \textbf{Reviewed By:} & Rachel Kim, VP of Engineering \\
  \textbf{Approved By:} & James Harrington, CTO \\
\end{tabular}
\vfill
{\large NovaCast Technologies, Inc.}\\
{\normalsize 3500 Tech Center Drive, Seattle, WA 98109}\\[10pt]
{\small\color{techgray}\textit{\copyright\ 2026 NovaCast Technologies, Inc.\ All rights reserved.}}
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\tableofcontents
\newpage

\section{Introduction}

\subsection{Purpose}

This technical report presents a comprehensive analysis of the NovaCast Content Delivery Network (CDN) architecture, documents performance testing results from the Q4 2025 load evaluation, and provides recommendations for optimization ahead of the projected 3x traffic increase associated with the platform's expansion into video streaming services in H2 2026.

\subsection{Scope}

The report covers the following areas:
\begin{itemize}[leftmargin=*]
  \item Current system architecture and infrastructure topology
  \item Performance benchmarks under simulated peak traffic conditions
  \item Identified bottlenecks and failure modes
  \item Recommended architectural changes and capacity planning
  \item Security considerations for the expanded service model
\end{itemize}

\subsection{Background}

NovaCast CDN currently serves approximately 2.4 billion requests per day across 47 points of presence (PoPs) in 28 countries, delivering static assets, API responses, and live event streams for 340 enterprise clients. With the planned launch of NovaCast Video (a managed video transcoding and delivery service), projected daily requests will increase to 7--8 billion by Q4 2026, requiring significant infrastructure enhancements.

\section{System Architecture}

\subsection{Overview}

The NovaCast CDN operates on a three-tier hierarchical architecture:

\begin{enumerate}[leftmargin=*]
  \item \textbf{Edge Layer:} 47 PoPs deployed in Tier 1 ISP facilities and cloud provider regions, each consisting of 4--16 edge servers running a custom Rust-based HTTP proxy (NovaCast Edge, v3.8). Edge servers handle TLS termination, request routing, cache lookup, and response assembly.
  \item \textbf{Mid-Tier Cache Layer:} 8 regional cache clusters deployed in major metro areas (Ashburn, Frankfurt, Singapore, Tokyo, S\~ao Paulo, Mumbai, Sydney, London). Each cluster consists of 12--24 high-memory servers running Redis Cluster for hot content and NVMe-backed disk caches for warm content.
  \item \textbf{Origin Shield Layer:} 3 origin shield facilities (US-East, EU-West, APAC) that aggregate cache misses before forwarding to client origin servers. This layer reduces origin load by 94\% compared to direct edge-to-origin routing.
\end{enumerate}

\subsection{Key Technology Stack}

\begin{table}[h]
\centering
\caption{Core technology components.}
\begin{tabularx}{\textwidth}{lXl}
\toprule
\textbf{Component} & \textbf{Description} & \textbf{Version} \\
\midrule
Edge Proxy & Custom Rust HTTP/2 and HTTP/3 proxy & NovaCast Edge 3.8 \\
Cache Engine & Hybrid memory/disk caching layer & NovaCast Cache 2.4 \\
DNS & GeoDNS with latency-based routing & PowerDNS 4.9 + custom plugin \\
Load Balancer & L4/L7 load balancing & Envoy Proxy 1.29 \\
Monitoring & Metrics collection and alerting & Prometheus + Grafana \\
Log Pipeline & Real-time log processing & Apache Kafka 3.7 + ClickHouse \\
TLS & Certificate management and termination & Automated via Let's Encrypt + Vault \\
Orchestration & Container orchestration for control plane & Kubernetes 1.29 \\
\bottomrule
\end{tabularx}
\end{table}

\subsection{Network Topology}

Edge PoPs connect to mid-tier caches via dedicated 100 Gbps backbone links provided through peering agreements with Tier 1 transit providers (Cogent, NTT, Telia, PCCW). Internal traffic between tiers uses IPsec tunnels with AES-256-GCM encryption. Anycast routing directs client requests to the nearest edge PoP based on BGP path selection.

\section{Testing Methodology}

\subsection{Test Environment}

Performance testing was conducted between November 15 and December 20, 2025, using a combination of synthetic load generation and replay of anonymized production traffic patterns. Testing infrastructure included:

\begin{itemize}[leftmargin=*]
  \item \textbf{Load Generators:} 120 distributed load generators across 12 AWS regions running a custom Go-based traffic simulator capable of sustaining 500,000 requests per second per instance.
  \item \textbf{Traffic Profiles:} Four distinct profiles were tested --- static assets (images, CSS, JS), API acceleration, live event streaming (HLS/DASH), and mixed workload representative of projected 2026 traffic composition.
  \item \textbf{Test Duration:} Each test scenario ran for a minimum of 4 hours with a 30-minute ramp-up period to reach steady state.
\end{itemize}

\subsection{Test Scenarios}

\begin{table}[h]
\centering
\caption{Load test scenarios and parameters.}
\begin{tabular}{llrr}
\toprule
\textbf{Scenario} & \textbf{Traffic Type} & \textbf{Target RPS} & \textbf{Duration} \\
\midrule
S1: Baseline & Current production mix & 28,000 & 4 hours \\
S2: 2x Scale & Projected mid-2026 mix & 56,000 & 4 hours \\
S3: 3x Scale & Projected late-2026 mix & 84,000 & 6 hours \\
S4: Spike & Flash crowd simulation & 150,000 & 1 hour \\
S5: Failover & Regional PoP failure during S2 & 56,000 & 2 hours \\
S6: Video Heavy & 70\% video streaming workload & 42,000 & 4 hours \\
\bottomrule
\end{tabular}
\end{table}

\section{Performance Results}

\subsection{Latency Metrics}

\begin{table}[h]
\centering
\caption{Response latency by scenario (milliseconds).}
\begin{tabular}{lrrrr}
\toprule
\textbf{Scenario} & \textbf{P50} & \textbf{P95} & \textbf{P99} & \textbf{P99.9} \\
\midrule
S1: Baseline & 8.2 & 24.1 & 48.3 & 124.7 \\
S2: 2x Scale & 9.1 & 28.4 & 67.2 & 198.4 \\
S3: 3x Scale & 12.7 & 42.8 & 134.6 & 487.3 \\
S4: Spike & 18.4 & 89.2 & 312.4 & 1,247.8 \\
S5: Failover & 14.3 & 38.7 & 82.1 & 267.4 \\
S6: Video Heavy & 11.8 & 35.6 & 98.4 & 342.1 \\
\bottomrule
\end{tabular}
\end{table}

Performance remained within SLA targets (P99 $< 100$ms) through the 2x scale scenario. At 3x scale, P99 latency exceeded the target by 34.6\%, with tail latencies (P99.9) degrading significantly. The spike scenario revealed aggressive latency degradation, indicating insufficient headroom for flash crowd events.

\subsection{Throughput and Error Rates}

\begin{table}[h]
\centering
\caption{Throughput and error metrics by scenario.}
\begin{tabular}{lrrrr}
\toprule
\textbf{Scenario} & \textbf{Achieved RPS} & \textbf{Bandwidth (Gbps)} & \textbf{Error Rate} & \textbf{Cache Hit} \\
\midrule
S1: Baseline & 28,000 & 142 & 0.002\% & 96.4\% \\
S2: 2x Scale & 55,800 & 278 & 0.008\% & 95.1\% \\
S3: 3x Scale & 79,200 & 394 & 0.047\% & 91.8\% \\
S4: Spike & 128,400 & 612 & 0.312\% & 84.2\% \\
S5: Failover & 54,100 & 268 & 0.021\% & 93.7\% \\
S6: Video Heavy & 41,800 & 487 & 0.014\% & 89.3\% \\
\bottomrule
\end{tabular}
\end{table}

At 3x scale, the system failed to achieve the full target RPS (79,200 vs.\ 84,000), with throughput limited by mid-tier cache CPU saturation. Error rates remained below the 0.05\% SLA threshold for all scenarios except the spike test. Cache hit ratios declined with increasing load, primarily due to cache eviction pressure on the video-heavy workload.

\subsection{Resource Utilization}

Peak resource utilization across infrastructure tiers at 3x scale:

\begin{table}[h]
\centering
\caption{Peak resource utilization at 3x scale (S3).}
\begin{tabular}{lrrrr}
\toprule
\textbf{Tier} & \textbf{CPU} & \textbf{Memory} & \textbf{Network I/O} & \textbf{Disk I/O} \\
\midrule
Edge Servers & 72\% & 61\% & 84\% & 28\% \\
Mid-Tier Cache & 91\% & 78\% & 67\% & 82\% \\
Origin Shield & 54\% & 42\% & 38\% & 34\% \\
DNS/Routing & 34\% & 28\% & 12\% & N/A \\
\bottomrule
\end{tabular}
\end{table}

The mid-tier cache layer is the primary bottleneck, with CPU utilization at 91\% driven by TLS session resumption handling and cache serialization overhead.

\section{Identified Bottlenecks}

\subsection{Mid-Tier Cache CPU Saturation}

The Redis Cluster instances in the mid-tier cache layer reach CPU saturation at approximately 2.5x current traffic levels. Root cause analysis identified two contributing factors: (1) TLS session ticket rotation consuming 18\% of CPU cycles, and (2) cache serialization/deserialization overhead for large video segments (average 2MB per object vs.\ 45KB for static assets).

\subsection{Edge Server Memory Pressure}

Video streaming workloads require maintaining per-client session state for adaptive bitrate (ABR) decisions. At projected 2026 video traffic levels, each edge server would need to maintain approximately 180,000 concurrent sessions, requiring 14GB of memory for session state alone, leaving insufficient headroom on current 32GB servers.

\subsection{Cache Eviction Under Video Workloads}

The current LRU eviction policy is suboptimal for mixed static/video workloads. Large video segments evict many smaller static objects, reducing the overall cache hit ratio. At video-heavy loads, the hit ratio dropped to 84.2\%, resulting in a 6x increase in origin traffic.

\section{Recommendations}

\subsection{Infrastructure Scaling}

\begin{enumerate}[leftmargin=*]
  \item \textbf{Mid-Tier Cache Expansion:} Increase mid-tier cache cluster size from 12--24 servers to 24--48 servers per region, using AMD EPYC 9654 processors with 96 cores per node. Estimated cost: \$4.2M capex + \$1.8M/yr opex. Priority: Critical.
  \item \textbf{Edge Server Upgrade:} Replace current 32GB servers with 64GB configurations at the top 20 PoPs (by traffic volume). Estimated cost: \$1.6M capex. Priority: High.
  \item \textbf{New PoP Deployment:} Deploy 8 additional edge PoPs in underserved regions (Lagos, Johannesburg, Jakarta, Bangkok, Bogot\'a, Istanbul, Warsaw, Dubai) to reduce latency for growing markets. Estimated cost: \$3.4M capex + \$2.1M/yr opex. Priority: Medium.
\end{enumerate}

\subsection{Software Optimizations}

\begin{enumerate}[leftmargin=*]
  \item \textbf{TLS Session Handling:} Migrate from per-connection TLS session tickets to a shared session cache backed by a dedicated low-latency key-value store, reducing per-request CPU overhead by an estimated 15\%. Priority: Critical.
  \item \textbf{Tiered Cache Eviction:} Replace LRU with a size-aware, frequency-weighted eviction policy (W-TinyLFU) that partitions cache space between small objects (static assets) and large objects (video segments). Expected improvement: 8--12\% increase in cache hit ratio under mixed workloads. Priority: High.
  \item \textbf{Video Segment Prefetching:} Implement predictive prefetching for sequential video segments based on playback session analysis, reducing cache miss latency for video by an estimated 40\%. Priority: High.
  \item \textbf{HTTP/3 Acceleration:} Enable HTTP/3 (QUIC) at all edge PoPs. Current deployment covers 60\% of PoPs. Full deployment expected to reduce connection establishment latency by 30\% and improve performance on lossy mobile networks. Priority: Medium.
\end{enumerate}

\subsection{Architecture Enhancements}

\begin{enumerate}[leftmargin=*]
  \item \textbf{Dedicated Video Edge:} Deploy specialized video edge servers with higher memory (128GB), NVMe storage (8TB), and GPU-assisted transcoding at the top 15 PoPs. This separates video and static workloads, preventing resource contention. Priority: High.
  \item \textbf{Dynamic Origin Selection:} Implement intelligent origin routing that selects the fastest responding origin server based on real-time health metrics, rather than static geographic mapping. Priority: Medium.
\end{enumerate}

\section{Security Considerations}

The expansion into video streaming introduces additional security requirements:

\begin{itemize}[leftmargin=*]
  \item \textbf{DRM Integration:} Support for Widevine and FairPlay DRM requires secure key exchange at the edge layer. Hardware security modules (HSMs) will be deployed at origin shield facilities.
  \item \textbf{DDoS Mitigation:} Projected 3x traffic increase requires upgrading DDoS mitigation capacity from 2 Tbps to 6 Tbps. Integration with upstream scrubbing services (Cloudflare Spectrum, Akamai Prolexic) is recommended.
  \item \textbf{Token Authentication:} URL token signing for video content will be implemented using HMAC-SHA256 with rotating keys managed by HashiCorp Vault.
  \item \textbf{WAF Enhancement:} Upgrade web application firewall rules to detect and block video-specific attack vectors, including segment manipulation and manifest poisoning.
\end{itemize}

\section{Conclusion}

The NovaCast CDN architecture performs well at current traffic levels and can sustain up to 2x growth without significant modifications. However, the projected 3x increase driven by video streaming services will exceed the capacity of the current mid-tier cache layer and stress edge server resources. The recommendations in this report, requiring an estimated total investment of \$9.2M in capital expenditure and \$3.9M in annual operating costs, will position the platform to handle projected 2026 traffic levels with appropriate performance margins and reliability guarantees.

Implementation should begin immediately with critical-priority items (mid-tier expansion and TLS optimization), targeting completion by Q2 2026, followed by high-priority items by Q3 2026 and medium-priority items by Q4 2026.

\appendix

\section{Test Environment Configuration Details}

\begin{table}[h]
\centering
\caption{Edge server hardware specifications (current).}
\begin{tabular}{ll}
\toprule
\textbf{Component} & \textbf{Specification} \\
\midrule
CPU & AMD EPYC 7543 (32 cores, 2.8 GHz base) \\
Memory & 32 GB DDR4-3200 ECC \\
Storage & 2$\times$ 1.92 TB NVMe SSD (Intel D7-P5510) \\
Network & 2$\times$ 25 GbE (Mellanox ConnectX-6) \\
OS & Ubuntu 22.04 LTS (kernel 5.15) \\
\bottomrule
\end{tabular}
\end{table}

\section{Glossary}

\begin{tabularx}{\textwidth}{lX}
\toprule
\textbf{Term} & \textbf{Definition} \\
\midrule
ABR & Adaptive Bitrate Streaming \\
CDN & Content Delivery Network \\
DASH & Dynamic Adaptive Streaming over HTTP \\
HLS & HTTP Live Streaming \\
HSM & Hardware Security Module \\
LRU & Least Recently Used (eviction policy) \\
NVMe & Non-Volatile Memory Express \\
PoP & Point of Presence \\
RPS & Requests Per Second \\
TLS & Transport Layer Security \\
W-TinyLFU & Window Tiny Least Frequently Used (eviction policy) \\
\bottomrule
\end{tabularx}

\end{document}