When applications scale, one of the first critical components you’ll need is a load balancer. Its job is simple yet essential: distribute incoming traffic across multiple servers so no single server gets overwhelmed, and users get a fast, reliable experience.
This article explains the difference between hardware and software load balancers, how to configure load balancers so they don’t become a single point of failure (SPOF), and shows how this applies to IBM Maximo Application Suite (MAS)—especially for airport operations.
What Is a Load Balancer?
A load balancer sits in front of your application servers and routes requests to available, healthy instances. It ensures:
- Better performance through parallel handling
- Higher reliability via health checks and automatic failover
- Cleaner operations by centralizing routing rules, TLS termination, and security policies
Hardware Load Balancers (Appliances)
Hardware load balancers are dedicated physical devices purpose‑built to manage and secure traffic at scale. They often include specialized hardware acceleration, advanced traffic management, and integrated security modules.
Typical characteristics:
- Form factor: Physical appliances in data centers
- Performance: High throughput, low latency, SSL/TLS offload
- Capabilities: L4/L7 balancing, WAF, DDoS mitigation, GSLB
- Operational model: Vendor-managed firmware/support
- Cost: High (CapEx + support)
Examples: F5 BIG‑IP, Citrix NetScaler, A10 Networks
Best for: Regulated environments (airports, banks), extremely high traffic, strict security/compliance.
Software Load Balancers
Software load balancers run as programs on general-purpose servers or VMs—often containerized and easily automated. They shine in cloud and Kubernetes/OpenShift environments due to flexibility and cost‑effectiveness.
Typical characteristics:
- Form factor: Software packages/services
- Performance: Scales horizontally by adding instances
- Capabilities: L4/L7 balancing, reverse proxy, observability integrations
- Operational model: DevOps-friendly, infrastructure as code
- Cost: Low to moderate; many open-source options
Examples: HAProxy, NGINX, Envoy, Kubernetes Ingress controllers (on OpenShift: HAProxy-based router pods)
Best for: Cloud‑native apps, microservices, rapid scaling, cost‑sensitive deployments.
Hardware vs Software: Quick Comparison
| Aspect | Hardware Load Balancer | Software Load Balancer |
|---|---|---|
| Nature | Dedicated physical appliance | Software running on servers/VMs |
| Cost | High | Low to moderate |
| Scaling | Scale up (bigger box) | Scale out (more instances) |
| Cloud fit | Limited | Excellent |
| Security features | Advanced, integrated | Add‑on or external integrations |
| Operations | Vendor‑managed | DevOps/IaC‑friendly |
The Hidden Risk: Load Balancer as a Single Point of Failure
If all traffic enters through a single load balancer instance, your entire application depends on that device. If it fails, everything goes down—that’s a single point of failure (SPOF).
To build resilient systems, you must make the load balancer layer itself highly available (HA).
How to Configure Load Balancers for High Availability
1) Active–Passive (Failover Pair)
- Setup: Two load balancers; one active, one standby.
- Mechanism: Heartbeats/health checks; the standby takes over on failure.
- Access pattern: Clients connect to a Virtual IP (VIP) that “floats” between nodes.
- Pros: Simple, predictable
- Cons: Standby capacity underutilized
2) Active–Active (Shared Traffic)
- Setup: Two or more LBs active simultaneously.
- Mechanism: Traffic split; surviving nodes absorb load on failure.
- Access pattern: DNS‑based load balancing, anycast, or upstream equal‑cost routes to multiple VIPs.
- Pros: Better utilization, higher throughput
- Cons: More complex routing/state sync
3) DNS‑Based Load Balancing and GSLB
- Concept: Spread traffic across multiple entry points (regions/data centers) using intelligent DNS.
- Mechanism: Health checks at DNS layer; route users to nearest healthy site.
- Pros: Geo redundancy, global failover
- Cons: DNS TTL propagation introduces lag; careful policies required
4) Cloud‑Native HA (Managed Services)
- Pattern: Use cloud managed LBs (AWS ALB/NLB, Azure Application Gateway/Front Door, Google Cloud LB).
- Mechanism: Provider manages multi‑AZ redundancy, VIPs, health checks automatically.
- Pros: Minimal ops burden, built‑in scaling/resilience
- Cons: Vendor lock‑in, cost, fewer deep customizations vs appliances
MAS on Red Hat OpenShift: Where Load Balancers Fit
In typical MAS deployments (on‑prem or cloud), OpenShift runs MAS services (Manage, Monitor, Health, Predict, Mobility). Traffic reaches MAS via external LB (F5 or cloud LB) and then through OpenShift Ingress/Router pods (HAProxy).
Key HA points:
- At least two router pods (OpenShift Ingress) per zone
- External LB health checks target router pods only
- Stateless services preferred; use shared stores for sessions if needed
Real‑World MAS Example: Airport Breakdown Maintenance
Scenario: An airport uses MAS Manage & Mobility for breakdown maintenance (e.g., a passenger boarding bridge or a conveyor subsystem fails). Users include operators, technicians, and planners.
Actors & Entry Points:
- Operator raises an Incident via web/Mobile
- Technician receives WO and executes via Mobility
- SCADA event triggers Monitor/Health → auto‑WO (optional)
What happens during failures:
- If F5‑A fails, the floating VIP moves to F5‑B (Active–Passive). Users continue seamlessly.
- If a router pod dies, OpenShift reschedules it; the external LB’s health checks stop sending traffic to the dead pod.
- If a MAS pod is unhealthy, the router stops routing to it (readiness/liveness probes), protecting user experience.
Business outcome:
- No downtime in user access while assets are being fixed
- Accurate downtime capture happens in execution by the technician
- Auditable KPIs (MTTR, Availability) remain trustworthy
Health Checks, Failover, and VIPs: Core Building Blocks
Regardless of appliance or software:
- Health Checks: Probe backends (HTTP 200, TCP connect) to avoid unhealthy targets
- Heartbeats: LB‑to‑LB signals to detect failure and trigger takeover
- Virtual IP (VIP): A single address exposed to clients; it moves to the healthy LB on failover
- State & Persistence: Prefer stateless apps; if stickiness needed, use cookie‑based persistence or shared session stores (e.g., Redis)