Implement Prefetching Technique Using Supply Chain Theory Lixiong Chen

Why Prefetching Disk I/O is extremely slow compared with direct data transfer within memory system Frequent exposure of disk operation to users can degrade their user experiences This can be avoided if data to be used is cached in memory before its actual request is issued from the users

Important Concerns What to fetch --- Choose data according to anticipation. Not an issue if fetching sequentially: Correlated data is always stored sequentially. When to fetch --- Remains a an issue for systems designed using queuing theory.

Correspondingly, associated with these two concerns are two terms: Prefetch degree: how much data is to be fetched upon a request Prefetch distance: how early to issue the next prefetch request

Existing prefetching technique may become troublesome when: Multiple data requests are issued concurrently, but with different frequency Each request is treated with equal importance Result: frequent data requests may end up with exhausted cached data while less frequent ones still have unconsumed resources

Supply Chain Model Supply chain model features: Multiple request-retrieve operations alone a single process Customer demands are desired to be serviced timely Resource allocation among different types of requests is managed

Problem Mapping Requests = Demand (normally distribution over data volume) Access time = Lead time (Trequest – Tretrieve, assumed to be i.i.d) Batched prefetching = Batched replenishment Multilevel prefetching = Multi-echelon inventory control

The key to the problem mapping is how to determine the “reorder point” and “safety inventory level”. In SCM, there are two types of basic algorithms: Equal Time Supplies (ETS) Equal Safety Factors (ESF)

ETS Applied if total inventory safety level is determined Individual safety inventory level is set proportional to demand rates

EFS Considers the demand uncertainties of demand streams (no longer stable) Highest uncertainty in demand deserves highest safety inventory level

Testbed Linux - trigger distance - prefetching degree Synthetic Benchmarks Real Linux Applications HTTP, VOD server simulation

Performance (benchmark)

Synthetic Benchmark

Real Linux Application

Server Benchmark (HTTP)

Server Benchmark (VOD)

Bullwhip Effect Variations of demand alone supply chain are amplified as orders are placed further away from customer In multi-level memory system, data demands become less stable as requests are issued further away from end users

Multi-level Distortion