retrieval_ps.m

% PS variant of retrieval_simple: a single processor-sharing retrieval station.
clc; clear solver AvgTable;
 
accessProb   = [49, 49, 49, 49, 7, 1, 1] / 205;   % per-item arrival probabilities (7 items)
serviceRates = ones(1, 7);                         % per-item PS fetch rates (mu_i = 1)
 
model = Network('DelayedHits');
 
n = numel(serviceRates);          % number of items
capacity = [6];                   % per-level cache capacity (total m = 6)
 
source    = Source(model, 'Source');
cacheNode = Cache(model, 'Cache', n, capacity, ReplacementStrategy.RR);
queue     = Queue(model, 'Queue', SchedStrategy.PS);
sink      = Sink(model, 'Sink');
 
jobClass  = OpenClass(model, 'InitClass', 0);
hitClass  = OpenClass(model, 'HitClass', 0);
missClass = OpenClass(model, 'MissClass', 0);
 
source.setArrival(jobClass, Exp(1));
 
pAccess = DiscreteSampler(accessProb);
cacheNode.setRead(jobClass, pAccess);
cacheNode.setHitClass(jobClass, hitClass);
cacheNode.setMissClass(jobClass, missClass);
 
% Queue -> Cache and Cache -> Queue inside the retrieval system
retrievalRoutingMatrix = [0.0, 1.0;   % Queue -> [Queue, Cache]
                          1.0, 0.0];  % Cache -> [Queue, Cache]
cacheNode.setRetrievalSystem(jobClass, missClass, queue, serviceRates, retrievalRoutingMatrix);
 
P = model.initRoutingMatrix();
P{jobClass, jobClass}(source, cacheNode)  = 1.0;
P{hitClass, hitClass}(cacheNode, sink)    = 1.0;
P{missClass, missClass}(cacheNode, sink)  = 1.0;
model.link(P);
 
% Simulation
SSA(model, 'samples', 5000, 'method', 'serial', 'seed', 1).getAvgCacheTable
LDES(model, 'samples', 1e6, 'seed', 1).getAvgCacheTable
 
% Analytical
MVA(model).getAvgCacheTable
NC(model).getAvgCacheTable
 
% Item-level cache occupancy
MVA(model).getAvgItemTable
NC(model).getAvgItemTable