Evaluating High-Availability DHCP Architectures: Migration from Legacy Linux DHCP to Infoblox Grid
Bibliographic record
Abstract
Dynamic Host Configuration Protocol (DHCP) is a foundational service in enterprise networks, responsible for the automated assignment of IP addresses and related configuration parameters to networked devices. As organizations scale and become increasingly dependent on continuous network availability, the resilience and manageability of DHCP infrastructure have emerged as critical operational concerns. Traditional Linux-based DHCP implementations, while flexible and cost-effective, often rely on manually configured failover mechanisms and decentralized management models that introduce complexity, operational risk, and limited scalability. This research evaluates the architectural, operational, and performance implications of migrating from a legacy Linux DHCP environment to an Infoblox Grid–based high-availability DHCP architecture. The study adopts a comparative evaluation methodology, examining availability models, failover behavior, response performance, administrative overhead, and security controls across both solutions. A real-world migration scenario is analyzed to assess the practical challenges and benefits associated with such a transition. Experimental results demonstrate that the Infoblox Grid architecture offers improved service availability, faster failover recovery, and significantly enhanced operational efficiency through centralized management and automation capabilities. However, these benefits are accompanied by trade-offs related to cost, vendor dependency, and reduced low-level configurability. The findings of this study provide network architects and decision-makers with empirical insights into the suitability of enterprise-grade DHCP platforms for modern high-availability requirements and offer guidance for organizations planning a structured migration from legacy DHCP systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".