Chapter 2
Pre-Deployment Planning and Design
Strategic planning is the invisible backbone behind every resilient oVirt deployment. This chapter equips you with the skills to anticipate operational scale, assess business requirements, and design for long-term adaptability before a single node is provisioned. Explore the intricate interplay of sizing calculations, network blueprints, security governance, and integration touchpoints to ensure every aspect of your virtualization landscape is engineered for excellence from day zero.
2.1 Workload Assessment and Sizing
Effective capacity planning hinges on a rigorous workload assessment that integrates application behavior profiles, anticipated growth trajectories, and stringent performance criteria. This analysis forms the cornerstone for precise calculation of CPU, memory, and storage requirements, ensuring infrastructural investments align with both current operations and sustained expansion goals.
Profiling application workloads requires detailed examination of each workload to capture resource utilization patterns and peak demands. Profiling must extend beyond average consumption metrics to include variability, burstiness, and I/O characteristics. Key quantitative parameters include:
- CPU Utilization: Measured as user-space, system-space, and idle times over representative time windows to account for periodic loads and synchronization delays.
- Memory Footprint: Includes resident set size (RSS), working set size (WSS), and swap usage, emphasizing both steady-state and transient peaks.
- Storage I/O Characteristics: Incorporates throughput (MBps), IOPS, and request size distributions, distinguishing between sequential and random patterns.
- Network Bandwidth: Although secondary in some contexts, network demand impacts cluster responsiveness and may influence storage or CPU contention indirectly.
Data collection methods should leverage instrumentation tools such as perf, vmstat, iostat, and custom telemetry gathered from application-specific logging. Longitudinal datasets enable statistical modeling of workload behavior, including percentile-based metrics (e.g., 95th or 99th percentile CPU usage) critical for robust sizing.
Predictive capacity planning necessitates incorporation of growth factors stemming from business drivers, user base expansion, and application feature proliferation. Growth rates can be linear, exponential, or cyclostationary, requiring flexible modeling approaches. Techniques include:
- Time Series Forecasting: Applying models such as ARIMA or Holt-Winters to historical utilization data to estimate future demand.
- Scenario Analysis: Defining optimistic, nominal, and pessimistic growth scenarios to bound planning outputs.
- Application Versioning Impact: Factoring in resource footprint changes associated with new releases or feature sets.
These projections inform temporal allocation of resources and define target capacity thresholds ensuring smooth scaling without degradation.
Capacity calculations must integrate service-level objectives (SLOs) such as latency bounds, throughput minima, and error rates. The relationship between resource provisioning and application performance is often nonlinear, necessitating capacity buffers or headroom to accommodate unexpected spikes and failover contingencies.
Two main approaches support this integration:
- Queuing Theory Models: Utilizing M/M/1 or M/M/c queue approximations to relate server capacity to expected response times under load.
- Empirical Benchmarking: Stress testing workloads on representative infrastructure to delineate thresholds beyond which performance deteriorates.
Buffer allocations typically range from 10% to 40% over forecasted peak demand but must be calibrated against cost constraints.
CPU requirements are deduced by aggregating per-thread or per-process CPU cycles needed to serve expected request volumes with desired latency. If D is the average CPU demand per request (in CPU-seconds) and R is the request rate (requests per second), the baseline CPU load L is:
The number of CPU cores required C is then calculated by applying an overhead factor H > 1 for context switching, interrupts, and scheduling inefficiencies:
where Umax is the maximum sustainable utilization per core, typically kept below 80-85% to maintain responsiveness.
Memory requirements must accommodate the working set size and additional caches or buffers needed by the process. Let Mws denote the working set size per request and Q the maximum concurrent requests:
Here, Mos accounts for operating system and ancillary service memory, while Mheadroom covers unexpected surges and fragmentation effects. Continuous monitoring of page faults and swap usage validates initial estimates.
Storage capacity is derived both from data volume and I/O performance requirements. Data volume includes primary datasets, replicas, snapshots, and logs:
I/O sizing requires evaluation of peak throughput and concurrency:
These metrics guide selection of storage technology (e.g., SSDs versus HDDs) and cluster topology.
Oversubscription-the practice of allocating more virtual resources than underlying physical capacity-maximizes utilization but risks performance degradation if not carefully managed. To incorporate oversubscription safely, define an oversubscription ratio a such that:
Best practices recommend maintaining a within empirically derived limits, often between 1.5 and 3.0 depending on workload contention profiles. Memory oversubscription should be treated more conservatively owing to higher risk of swapping.
Simulation models, such as discrete-event simulations or stochastic workload generators, can predict the impact of oversubscription on latency and throughput under varying load conditions, enabling informed trade-offs between utilization and performance guarantees.
Sustained future growth imposes additional considerations:
- Modular Scaling: Designing capacity increments in modular units (e.g., server racks or node groups) facilitates incremental expansion and investment smoothing.
- Resource Elasticity: Employing container orchestration and virtualized environments allows dynamic redistribution of resources to handle peaks.
- Capacity Rebalancing: Continuous reassessment of workload distribution and hot spot mitigation preserves cluster efficiency over time.
Advanced planners often adopt capacity dashboards integrating real-time telemetry with predictive analytics, alerting operators before critical thresholds are breached. These systems rely on automated workflows to provision resources proactively and adjust oversubscription ratios dynamically.
The synthesis of workload profiling, growth forecasting, and performance objectives into quantifiable resource needs constitutes a rigorous methodology that transcends heuristic planning. Accurate workload assessment and sizing impair neither system reliability nor scalability, but rather empower infrastructure teams to architect solutions that are both cost-effective and future-ready.
2.2 Network Topology Design
In virtualization architectures that demand security, multi-tenancy, and high availability, the network topology forms the critical foundation underpinning performance and resilience. Designing such topologies requires an intricate balance between segregation at Layer 2 (L2) and Layer 3 (L3), robust redundancy schemes, and well-defined failure domains, all while ensuring seamless compatibility with enterprise networking standards and compliance mandates.
Segmentation: L2 vs. L3 Considerations
L2 segmentation traditionally facilitates the creation of broadcast domains within a virtualized environment, often implemented by Virtual LANs (VLANs) or VXLAN overlays in large-scale deployments. While L2 domains provide simplicity in service discovery and migration-allowing virtual machines (VMs) to move without IP address changes-excessive L2 domains risk larger failure domains, broadcast storms, and possible security vulnerabilities if segmentation is weak.
Conversely, L3 segmentation introduces routing boundaries between tenants or services, significantly enhancing isolation and scalability. By leveraging subnetting and virtual routing and forwarding instances (VRFs), traffic is strictly confined to designated segments, reducing the impact surface for potential breaches and limiting blast radius upon failures. However, careful design must address route propagation, the complexity of inter-subnet communication, and routing security policies.
A hybrid approach combining L2 and L3 ...
