EBU6502_cloud_computing_notes/1-4-scalability.md

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)
• Also called fixed workload efficiency

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