Cache Eviction Policies

1. Overview

Definition: Cache eviction policies determine how and when data is removed from the cache to make space for new data.
Purpose: The main goal is to optimize cache usage while minimizing latency and maximizing hit rates.

Least Recently Used (LRU):
- Evicts the least recently accessed data.
- Assumes that data accessed recently will likely be needed again soon.
First In, First Out (FIFO):
- Evicts the oldest data first.
- Does not consider the frequency or recency of access.
Least Frequently Used (LFU):
- Evicts the data that has been accessed the least often.
- Keeps the most frequently accessed data in the cache.
Random Replacement:
- Chooses a random entry to evict.
- Simple and can be efficient in certain scenarios.
Most Recently Used (MRU):
- Often used when the most recent data is less needed than older data.
- Typically suited for scenarios with certain access patterns.
Adaptive Policies:
- Combine multiple strategies (e.g., LRU, LFU) to adapt based on access patterns.
- Can maximize performance based on observed workload distributions.

Workload Characteristics: Frequency and pattern of data access play a significant role in the effectiveness of an eviction scheme.
Memory Constraints: Limited cache size impacts how aggressively data is evicted.
System Architecture: Different architectures may favor specific policies depending on overall design.

Performance versus Complexity: LRU is often more effective in terms of hit rate but is more complex to implement than FIFO, which may perform poorly in certain workloads.
Recency vs. Frequency: LRU focuses on recency, while LFU emphasizes frequency; both aim to retain relevant data, but they do so with different assumptions.
Adaptivity: Adaptive policies build on the strengths of various methods, allowing systems to respond dynamically to changing access patterns.