The Mitce Dev Node System is a fully automated, data-driven network optimization platform designed to continuously measure, evaluate, and select the fastest and most stable network routes across different regions and ISPs. Its goal is simple but precise: to ensure that every user request travels through the lowest-latency, most reliable, and highest-throughput path available at any given moment.
The system consists of multiple distributed detection nodes, a central control hub (the DevOps Control Center), and an adaptive intelligent DNS engine. Currently operating with around 10 nodes in the Dev phase, the platform will scale to 30–40 nodes in production to achieve greater sampling density and coverage across regions and ISPs.
1. Node Deployment and System Role
Mitce deploys several detection servers in different regions and across multiple network operators (ISPs). Each node runs independently within its own network environment, representing a unique geographic and carrier context. This distributed design allows the system to construct a multi-perspective view of real-world network performance.
- Regional distribution: Nodes are placed near key access points and network exchange boundaries.
- ISP diversity: Each region includes multiple providers to observe cross-ISP performance differences.
- Infrastructure stability: Nodes operate on low-latency lines with continuous telemetry and self-monitoring.
Each node executes scheduled detection routines—typically every hour—initiating active TCP and TLS connections toward target IPs. Active probing provides immediate visibility into connection quality and latency.
2. Sampling Strategy and Measurement Scope
During each detection cycle, a node selects a small subset (approximately 3–5%) from a theoretical pool of about 13,000 target IPs. These IPs are derived from multiple /24 subnets, excluding gateway addresses (.1). Randomized sampling ensures balanced and representative coverage without overwhelming the system.
Each IP is tested multiple times using TCP and TLS handshakes to minimize random fluctuations. Each probe records key metrics:
- TCP handshake success rate and average round-trip latency (RTT)
- TLS handshake delay (including ServerHello and certificate response time)
- Packet loss rate and retransmission count
- Connection failure causes (timeouts, resets, refusals)
Collected data is aggregated locally, normalized, and converted into a compact performance snapshot, which is then transmitted to the DevOps control center for consolidation.
3. Stage One: Latency-Based Preselection
The first stage filters out high-latency targets. From each cycle’s sampled IPs, the node selects the top 30 with the lowest average TCP and TLS handshake latency. This identifies the routes with the shortest propagation time and minimal delay variance.
Outliers beyond three standard deviations are discarded. The top 30 candidates proceed to the next phase for stability testing.
4. Stage Two: Stability and Consistency Evaluation
Low latency alone doesn’t guarantee stability. In this stage, the node repeatedly tests each of the 30 candidates over a 10–15 minute window to measure jitter, packet loss, and reliability under short-term variation.
Each IP receives a weighted composite score:
Score = (LatencyWeight * AvgLatency) +
(JitterWeight * StdDeviation) +
(LossWeight * PacketLoss)
The lower the score, the better the overall stability. The top 15 IPs advance to throughput testing.
5. Stage Three: Real Download Throughput Tests
This stage simulates realistic data transfer scenarios. Each of the remaining 15 IPs performs an HTTP download test (using fixed-size 5MB objects) to evaluate sustained bandwidth and rate stability.
A “Bandwidth Utilization Efficiency” (BUE) metric is calculated to quantify real throughput quality. The five IPs with the highest BUE become the node’s local “best-of-set” for that hour.
6. Central DevOps Control and Cross-Node Verification
Each node uploads its top results to the DevOps Control Center, which aggregates performance data by ISP, for example:
ISP-A: Node-1A, Node-2A, Node-3A ISP-B: Node-1B, Node-2B, Node-3B ISP-C: Node-1C, Node-2C
Within each ISP, nodes validate each other’s best IPs. For instance, ISP-A-1’s top 5 IPs are cross-tested by ISP-A-2 and ISP-A-3 to verify consistency. This ensures that selected IPs perform well across multiple regions within the same provider’s network rather than only locally.
After correlation, the central system selects three optimal IPs per ISP and one backup, producing a clean, verified dataset for DNS routing updates.
7. Intelligent DNS Routing and Update Logic
The intelligent DNS layer acts as the bridge between system optimization and user access. When a user’s device queries DNS, the system determines the user’s ISP and region, returning the most suitable IP set accordingly.
DNS records are updated hourly, synchronized with node testing cycles. Within Mitce’s authoritative DNS servers, updates propagate within seconds; however, actual client-side effect depends on TTL and cache expiration—typically within several minutes for most environments.
8. Continuous Health Monitoring and Auto-Recovery
Beyond hourly tests, each node performs minute-level TLS handshake checks. This provides a realistic indicator of service availability at the application layer, beyond simple ICMP pings.
If handshake success rates decline or latency spikes are detected, the node alerts the control center. When multiple nodes report similar issues, the system automatically triggers a DNS update to replace the degraded IP with a pre-designated backup. All changes include rollback logic, allowing automated restoration if the backup performs worse.
9. Closed Feedback Loop and Predictive Learning
The Mitce system forms a closed feedback cycle, continuously learning from accumulated measurement data. The central controller maintains time-series records of latency, stability, and throughput metrics per ISP, identifying temporal patterns and long-term performance trends.
- Average latency trend curves
- Bandwidth utilization distributions
- Peak-hour degradation behavior
- Route failure frequency statistics
By analyzing these datasets, the system can predict potential degradation windows and preemptively select alternate nodes. This predictive switching reduces unnecessary instability and improves service continuity.
10. Current Dev Phase and Scaling Outlook
Currently, about ten nodes are active in the Dev phase, performing tens of thousands of TCP/TLS tests per hour and producing hundreds of megabytes of telemetry. Even at this scale, the platform provides statistically meaningful insights into inter-ISP performance variations.
Once expanded to 30–40 nodes, the system will gain higher spatial resolution and near real-time responsiveness. It will be capable of detecting routing anomalies, regional congestion, or ISP-level degradation within minutes and adjusting accordingly.
Future iterations will introduce a layered control structure (regional controllers + a main coordination layer) and incorporate BGP path awareness with AS-path correlation. This will further refine routing intelligence and make decision logic more deterministic.
11. Technical Summary
The Mitce Dev Node System is a real-time, measurement-driven, adaptive routing platform. It integrates TCP/TLS probing, cross-node validation, ISP-level data aggregation, and intelligent DNS-based redirection into a cohesive closed-loop control process.
The design philosophy is not to “pick the fastest route once,” but to continuously measure, learn, and adapt. Each node acts as an independent sensor; the control center fuses all results into actionable routing intelligence. Every DNS response is therefore a real-time decision based on current regional data rather than a static configuration.
As a result, users are automatically directed to the nodes offering the lowest latency, highest stability, and optimal throughput, regardless of their region or ISP.
This is not simple load balancing—it is an intelligent, self-aware network coordination system capable of re-evaluating and rewriting its own routing map continuously. Mitce is engineering a network that can learn, adapt, and autonomously find the fastest path.