Latency Optimization Techniques

Study Notes

Definition: Latency refers to the delay between the time data is sent and the time it is received.
Types of Latency:
- Network latency: delay caused by network transmission
- Disk latency: delay caused by disk I/O operations
- CPU latency: delay caused by CPU processing
Optimization Techniques:
- Caching: storing frequently accessed data in a faster, more accessible location
- Content Delivery Networks (CDNs): distributing content across multiple servers to reduce distance and latency
- Parallel processing: breaking down tasks into smaller, parallelizable parts to reduce processing time
- Lazy loading: delaying the loading of non-essential resources until needed

Definition: Scalability refers to a system's ability to handle increased load or demand without a decrease in performance.
Types of Scalability:
- Vertical scaling: increasing the power of a single server or node
- Horizontal scaling: adding more servers or nodes to distribute the load
Scalability Factors:
- Load balancing: distributing incoming traffic across multiple servers to ensure no single server is overwhelmed
- Database sharding: breaking down a large database into smaller, more manageable pieces
- Queue-based architectures: using message queues to handle large volumes of requests
Scalability Metrics:
- Response time: measuring the time it takes for a system to respond to a request
- Throughput: measuring the number of requests a system can handle within a given time period

Definition: Resource utilization refers to the efficient use of system resources such as CPU, memory, and I/O.
Resource Utilization Metrics:
- CPU utilization: measuring the percentage of CPU time used by a system
- Memory utilization: measuring the amount of memory used by a system
- Disk utilization: measuring the amount of disk space used by a system
Resource Utilization Optimization Techniques:
- Resource pooling: sharing resources across multiple systems or applications
- Resource allocation: dynamically allocating resources based on system demand
- Garbage collection: automatically managing memory allocation and deallocation to reduce waste

Latency refers to the delay between the time data is sent and the time it is received.
There are three types of latency: network latency, disk latency, and CPU latency.
Caching stores frequently accessed data in a faster, more accessible location to reduce latency.
Content Delivery Networks (CDNs) distribute content across multiple servers to reduce distance and latency.
Parallel processing breaks down tasks into smaller, parallelizable parts to reduce processing time and latency.
Lazy loading delays the loading of non-essential resources until needed to reduce latency.

Scalability refers to a system's ability to handle increased load or demand without a decrease in performance.
There are two types of scalability: vertical scaling and horizontal scaling.
Vertical scaling increases the power of a single server or node to handle increased load.
Horizontal scaling adds more servers or nodes to distribute the load and handle increased demand.
Load balancing distributes incoming traffic across multiple servers to ensure no single server is overwhelmed.
Database sharding breaks down a large database into smaller, more manageable pieces to handle increased load.
Queue-based architectures use message queues to handle large volumes of requests and ensure scalability.
Response time measures the time it takes for a system to respond to a request, and is a key metric for scalability.
Throughput measures the number of requests a system can handle within a given time period, and is a key metric for scalability.

Resource utilization refers to the efficient use of system resources such as CPU, memory, and I/O.
CPU utilization measures the percentage of CPU time used by a system.
Memory utilization measures the amount of memory used by a system.
Disk utilization measures the amount of disk space used by a system.
Resource pooling shares resources across multiple systems or applications to optimize resource utilization.
Resource allocation dynamically allocates resources based on system demand to optimize resource utilization.
Garbage collection automatically manages memory allocation and deallocation to reduce waste and optimize resource utilization.