Cache miss ratio
Cache miss ratio is the proportion of cache access requests for which the requested data or entry is absent.
Latency
Cache miss ratio is the proportion of cache accesses that do not find the requested data in the cache.
- How it is used
- Measures whether caching avoids slow backing-store lookups for a workload.
- How deep it goes
- Defines the ratio, interprets endpoint values, and connects misses to capacity, replacement policy, and latency.
- Examples
- Uses 10 MB and 1 MB working-set sizes plus latency curves showing cache-hit and cache-miss effects.
- What you can do after
- Diagnose cache effectiveness and prioritize capacity or replacement-policy changes to reduce backing-store accesses.
Designing Deep Learning Systems: A guide for software engineers
The proportion of cache lookup requests whose requested model is not present in the cache.
- How it is used
- Guides model-cache resource management in multitenant prediction services.
- How deep it goes
- Defines misses operationally and connects them to eviction and model-swapping strategies without detailing metric instrumentation.
- Examples
- Uses a multitenant prediction service that caches models under limited memory and GPU capacity.
- What you can do after
- Apply it via LRU eviction and model partitioning when tuning model-cache behavior.
Read how Designing Deep Learning Systems: A guide for software engineers explains cache miss ratio →
Related concepts
- hit ratecontrasts with · Cache hit rate is the proportion of cache lookups satisfied by a usable result already stored in the cache.
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