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+# Scalability
+
+## Definition
+
+- Ability to use more resources, without major change in original setup
+
+## Reasons ( Not important )
+
+- Increasing data
+- Globalization
+- New technologies
+
+## Trends
+
+### Moore's law
+
+### Parallel computing
+
+### Distributed and cloud computing
+
+### Virtualization
+
+### Cluster computing
+
+- Definition: loosely or tightly coupled pool of computers that work together
+  collectively and cooperatively as a single computing resource to solve the
+  same or common task
+- Benefits
+    - Enables scalable parallel computing
+    - Achieves high availability through stand along operation, and fail over
+    - Modular growth: easy to upgrade
+
+## Measurements of scalability
+
+- Functional scalability: Add new functions, and no degradation in memory usage
+  and performance
+- Geographical scalability: Distributive globally, no degradation in
+  performance.
+- Administrative scalability: Adding more users, no degradation
+- Heterogeneous and generational: Adding features and components from different
+  vendors and manufacturers, no degradation
+- Load scalability: Adding more load, no degradation
+
+## Strategies for scalability: horizontal and vertical
+
+- Horizontal: Add more nodes to and existing cluster
+- Vertical: Add more resource to **single node**
+
+## Performance and Hardware scalability
+
+### Relationship (Important)
+
+- Scaling **increases** performance
+- Performance is **not** directly proportional to resources added
+- Diminishing return occurs, then tuning is a better choice
+
+### Amdahl's law (Important)
+
+- formula
+    - Speedup factor:
+      $$S = T / (\alpha \times T + (1 - \alpha \times T / n)) = 1 / (\alpha + (1 - \alpha) / n)$$
+    - $\alpha$: fraction of serial computation
+    - 1 - alpha: part that can be parallelized
+    - $n$: processors used
+- Max speedup of n processors, is only achieved when alpha reaches zero, which
+  means the program is fully parallelized
+- TODO: work on page 19, draw graph
+- Assumption: use the **same amount of workload for both sets**
+- System efficiency formula:
+    - $$Efficiency = E = Speedup / n = 1 / ( \alpha \times n + 1 - \alpha )$$
+- Efficiency is low, when load is large, because most nodes are idling (waiting
+  for serial computation to complete)
+
+### Gustafson’s Law
+
+- To enhance efficiency, scale the workload to match the capacity
+- Also called the Scaled Worload Speedup
+- Formula
+    - Scale the workload to: $$Wnew= \alpha W + (1 - \alpha) \times n \times W$$
+    - $n$: processor count
+    - $W$: workload
+    - $Wnew$: Scaled workload
+- Only parallelizable portion is scaled
+- **Scaled workload** speedup
+    - $$S' = W' / W = a + (1-a) \times n$$
+- **Scaled** efficiency:
+    - $$S' / n = a / n + (1 - a)$$
+- TODO: do some calculation
+
+## Availaility (Important)
+
+- Formula:
+    - $$SA = MTTF / (MTTF + MTTR)$$
+    - $MTTF$: Mean time to **failure**, the longer the better
+    - $MTTR$: Mean time to **repair**, the shorter the better
+
+## Types of scaling
+
+- Strong: How performances changes, by increasing **processors** for **fixed** problem
+- Weak: How the performance changes by increasing **processors** for **problem per processor** 
+
+## Scalability in cloud computing
+
+- Elasticity: resource can be altered
+- Virtualization scaling: adding existing system to cloud
+    - Performance issues: Performance of virtualized components may be slower than bare metal
+- Scale by migrating resources: Regions and Availaility Zones
+    - Region: physical geographical location, that consists of one or more zones
+- Load balancing: AWS Elastic Load balancing, distributes incoming application
+  traffic across multiple targets.
+- Auto scaling: AWS EC2 auto scaling
+- CDN: AWS CloudFront, use edge caching on edge location to serve content to anywhere closer to the vieweer, in order to achieve lower **latency** and higher **transfer speed** 
+- TODO: work the questions