Scalability and Load Balancing

This topic covers handling load in the cloud. You compare vertical and horizontal scaling, understand load-balancing algorithms and session handling, and configure auto-scaling policies that add or remove capacity based on metrics.

Relation 1 —

Write this relation with symbols exactly as in Rajaraman Cloud — Standard reference before substituting numbers. Examiners award partial marks for a correct setup even when arithmetic slips.

Relation 2 —

Write this relation with symbols exactly as in Rajaraman Cloud — Standard reference before substituting numbers. Examiners award partial marks for a correct setup even when arithmetic slips.

Relation 3 —

Write this relation with symbols exactly as in Rajaraman Cloud — Standard reference before substituting numbers. Examiners award partial marks for a correct setup even when arithmetic slips.

Vertical scaling (a bigger server) is simple but bounded and creates a single point of failure, so cloud systems favour horizontal scaling — adding interchangeable instances behind a load balancer. This only works well when the application tier is stateless, so any instance can serve any request; when state is kept per-server, sticky sessions must pin users to one backend, undermining even load distribution and resilience. The load balancer spreads traffic by round-robin, least-connections or weighted rules, and auto-scaling closes the loop by watching metrics like CPU or request rate and adding instances during spikes and removing them when idle — the elasticity that makes cloud economics attractive by matching cost to actual demand.

State assumptions explicitly before using any relation for scalability and load balancing — steady state, uniform properties, linear elastic material, ideal gas, incompressible flow, etc., as applicable.

Wrong assumptions invalidate the entire solution even when the formula is correct. In Cloud Computing viva and GATE descriptive questions, listing valid assumptions often earns separate marks.

1. Read the question and list given data with SI units (common in Cloud Computing papers).
2. Draw a neat labelled diagram where applicable — examiners in Indian universities award diagram marks even when arithmetic slips.
3. Identify which relation from this topic applies to scalability and load balancing.
4. Use equation 1: .
5. Use equation 2: .
6. Substitute values, compute, and verify units and sign (direction).
7. State conclusion in one line — e.g. safe/unsafe, stable/unstable, feasible/infeasible.

Scalability and Load Balancing appears in SaaS/PaaS deployments. In Indian it software curricula this topic is tested because it connects theory to virtualization and cloud service models.

GATE and semester exams often combine scalability and load balancing with earlier units — revise prerequisites before attempting mixed problems.

Industry interview panels sometimes ask: "Where did you use scalability and load balancing?" — answer with a lab, mini-project, or plant visit example if possible.

Students design a stateful application tier and then struggle to scale out, forget that vertical scaling has hard limits and downtime, and confuse a load balancer with auto-scaling (distribution versus capacity change). Assuming sticky sessions are free of resilience cost is an oversight.

Before attempting scalability and load balancing problems, confirm you can:

1. A stateless application tier scales out easily
2. Sticky sessions bind a user to one backend
3. Elasticity means scaling in and out on demand

Revise the solved examples in Rajaraman Cloud — Standard reference and one previous-year GATE or university paper for this unit.