Working on #6

examples/example_1 copy.jl

@@ -0,0 +1,82 @@
+using NAssets
+using Plots
+using GraphRecipes
+
+
+ntw_size = 9
+ntw_topo = 4
+n_steps = 100
+# Prepare base simulation configuration 
+bcfg = (
+        seed = 123,
+        ntw_topo = ntw_topo, 
+        size = ntw_size,
+        ctl_model = 7,
+        ctl_k = 3,
+        ctl_B = 0.7,
+        # prob_random_walks = 0.8,
+        n_steps = n_steps, 
+        deterioration = [ (rul,t,a) -> rul - a 1 ],
+        traffic_dist_params = [1.0, 0.07], 
+        #max_queue_ne = 4000,
+        #capacity_factor = 1.5,
+        # link_capacity = 1000,
+        #clear_cache_graph_freq = 5,
+        #pkt_per_tick = 5000,
+        #  max_msg_live = 5,
+        init_sne_params = (ids=[5,9],capacity_factor=[2,2]),
+        init_link_params = (ids=[(2,5),(4,5),(5,8),(5,6)],capacities=Int.(ones(4)*1000)),
+        ntw_services = [
+            (3, 6),
+            #(1, 4),
+            (9,1),
+            (8,4)
+            ] 
+        )
+
+#obtain the configuration object. This method will create the control and underlying graphs as well as other objects required to run the simulation.
+cfg = NAssets.config(bcfg)
+
+
+# run simulation
+ctl_ags, vnes, modbin = NAssets.single_run(cfg,log_to_file=false)
+
+# Three objects are returned with the state trajectory of the control agents, network elements (assets) and the entire model
+
+
+# Condition deterioration
+
+prul = plot(title="Network Assets RUL")
+
+for ne in 1:size(vnes,1)
+    plot!(prul,[ ne_st.rul for ne_st in vnes[ne] ], linestyle= ne == 2 ? :dash : :dot )
+end
+prul
+# hline!(p,[10], linestyle=:dash, c=:red, label="")
+# hline!(p,[0.1], linestyle=:dash, c=:red, label="")
+
+# print(modbin)
+
+
+
+#rings
+#gp = NAssets.get_graph(-1,40,NAssets.GraphModel(7);k=2,B=0)
+
+#grid?
+
+gp = NAssets.get_graph(-1,ntw_size,NAssets.GraphModel(5))
+graphplot(gp)
+
+# tpt_p = plot(
+#         ylabel!(NAssets.plot_tpt_step(rd[1].snes_ts,rd[4].snes_ts,rd[1].model_ts,t),"Throughput\n(MB/time)",guidefontsize=6)
+#         ,plot_tpt_step(rd[2].snes_ts,rd[4].snes_ts,rd[2].model_ts,t)
+#         ,plot_tpt_step(rd[3].snes_ts,rd[4].snes_ts,rd[3].model_ts,t)
+#         ,layout=(1,3))
+
+p = plot(title="End-to-end Throughput")
+for ne in 1:size(vnes,1)
+    plot!(p,NAssets.get_throughput_trj(vnes[ne],n_steps), linestyle= :solid , label = "Asset_$ne", legend = :outerright
+    )
+end
+p
+

examples/example_1.jl

@@ -1,16 +1,57 @@
 using NAssets
 using Plots
+using GraphRecipes
 
 
+tst = (rul,t,a) -> begin
+    println("rul: $rul --- a: $a")
+    return last(rul) - ( a * t )
+end
 
+ntw_size = 9
+ntw_topo = 4
+n_steps = 100
 # Prepare base simulation configuration 
 bcfg = (
-        ntw_topo = 2, 
-        size = 5,
-        ctl_model = 1, 
-        n_steps = 80, 
-        traffic_dist_params = [1.0, 0.05], 
-        ntw_services = [(3, 5), (1, 4)] 
+        seed = 123,
+        ntw_topo = ntw_topo, 
+        size = ntw_size,
+        ctl_model = 7,
+        ctl_k = 3,
+        ctl_B = 0.7,
+        # prob_random_walks = 0.8,
+        n_steps = n_steps, 
+        deterioration = [ (rul,t,a) -> rul - a 1 ],
+        prediction = [ tst 1 ],
+        traffic_dist_params = [1.0, 0.07], 
+        traffic_packets = 100,
+        #max_queue_ne = 100,
+        #capacity_factor = 5,
+        link_capacity = 2000,
+        max_msg_live = 10,
+        #clear_cache_graph_freq = 5,
+        pkt_per_tick = 3100,
+        #  max_msg_live = 5,
+        mnt_bc_duration = 1,
+        mnt_bc_cost = 0.5,
+        mnt_wc_duration = 5,
+        mnt_wc_cost = 5,
+        mnt_policy = 0,
+        predictive_freq = 10,
+        prediction_window = 10,
+        init_sne_params = 
+        #(ids=[9],capacity_factor=[4]),
+        (ids=[9],mnt_policy=[1]),
+        #  init_link_params = (
+        # #     # ids=[(2,5),(4,5),(5,8),(5,6),(1,2),(3,6),(6,9),(8,9)],capacities=[2000,4000,4000,500,4000,5000,500,500]
+        #      ids = [(7,8)],
+        #      capacities = [2000]
+        #      ),
+        ntw_services = [
+            (3, 7),
+            (9,1),
+            (8,4)
+            ] 
         )
 
 #obtain the configuration object. This method will create the control and underlying graphs as well as other objects required to run the simulation.
@@ -22,13 +63,51 @@ ctl_ags, vnes, modbin = NAssets.single_run(cfg,log_to_file=false)
 
 # Three objects are returned with the state trajectory of the control agents, network elements (assets) and the entire model
 
-#
-p = plot(title="Network Assets RUL")
+
+# Condition deterioration
+
+prul = plot(title="Network Assets RUL")
 
 for ne in 1:size(vnes,1)
-    plot!(p,[ ne_st.rul for ne_st in vnes[ne] ], linestyle= ne == 2 ? :dash : :dot )
+    plot!(prul,[ ne_st.rul for ne_st in vnes[ne] ], linestyle= ne == 2 ? :dash : :dot )
 end
-hline!(p,[10], linestyle=:dash, c=:red, label="")
-hline!(p,[0.1], linestyle=:dash, c=:red, label="")
-p
+prul
+hline!(prul,[10], linestyle=:dash, c=:red, label="")
+prul
+# hline!(p,[0.1], linestyle=:dash, c=:red, label="")
+
+# print(modbin)
+
+
+
+#rings
+#gp = NAssets.get_graph(-1,40,NAssets.GraphModel(7);k=2,B=0)
+
+#grid?
+
+# gp = NAssets.get_graph(-1,ntw_size,NAssets.GraphModel(5))
+# graphplot(gp)
+
+# tpt_p = plot(
+#         ylabel!(NAssets.plot_tpt_step(rd[1].snes_ts,rd[4].snes_ts,rd[1].model_ts,t),"Throughput\n(MB/time)",guidefontsize=6)
+#         ,plot_tpt_step(rd[2].snes_ts,rd[4].snes_ts,rd[2].model_ts,t)
+#         ,plot_tpt_step(rd[3].snes_ts,rd[4].snes_ts,rd[3].model_ts,t)
+#         ,layout=(1,3))
+
+# p = plot(title="End-to-end Throughput")
+# for ne in 1:size(vnes,1)
+#     plot!(p,NAssets.get_throughput_trj(vnes[ne],n_steps), linestyle= :solid , label = "Asset_$ne", legend = :outerright
+#     )
+# end
+# p
+
+
+
+# pls = plot(title="Packet loss")
+# for ne in 1:size(vnes,1)
+#     plot!(pls,[ vnes[ne][i].drop_pkt for i=1:n_steps ], linestyle= :solid , label = "Asset_$ne", legend = :outerright
+#     )
+# end
+# pls
+
 

src/ctl/queries_multiple.jl

@@ -21,7 +21,7 @@ function do_query!(msg::OFMessage,a::Agent,model)
 
         path = do_query(query_time,query,query_graph,query_paths)
 
-        # log_info(model.ticks,a.id,"query: $(query) ----path found: $path ----> in precalc paths: $(query_paths)")
+        # log_info(model.ticks,a.id,"query: $(query) ----path found: $path ----> in precalc paths: $(query_paths) -- query graph: $(nv(query_graph)))")
 
         # For no centralised control topo
         if model.ctrl_model != GraphModel(1) # ¬ centralised
@@ -33,7 +33,7 @@ function do_query!(msg::OFMessage,a::Agent,model)
 
             #valid path not found?, need to send queries to nbs
             if isempty(path)
-                #should always query nbs even if has already queried with no reply?
+                #TODO should always query nbs even if has already queried with no reply?
                 sent_to = query_nbs!(a,msg,query_graph,query,[a.id],model)
                 record_pending_query!(a,model.ticks,sent_to,query)
                 push!(a.pending,(model.ticks,msg,false)) #false:not reprocess next time

src/model/netManModel.jl

@@ -62,6 +62,7 @@ function create_sim_asset_agents!(model)
 
         mnt_policy = typeof(model.init_sne_params) <: NamedTuple && id in model.init_sne_params.ids && Symbol("mnt_policy") in keys(model.init_sne_params) ? model.init_sne_params.mnt_policy[first(indexin(id,model.init_sne_params.ids))] : model.mnt_policy
 
+        log_info(model.ticks,"Prediction in init: $(last(prediction))")
         mnt = @match mnt_policy begin
             1 => MaintenanceInfoPreventive(deterioration,prediction,model)
             2 => MaintenanceInfoPredictive(deterioration,prediction,model)
@@ -260,10 +261,9 @@ end
 Initialise control agents
 """
 function init_agent!(a::Agent,model)
-
+    nodes = sort([get_controlled_assets(a.id,model)...])
     if model.ctrl_model != GraphModel(1)
         #Calculate sub graphs and init msg channels among agents
-        nodes = [get_controlled_assets(a.id,model)...]
         sub_g = get_subgraph(model.ntw_graph,nodes,:eid)
 
         nodes = [a.id]
@@ -284,17 +284,24 @@ function init_agent!(a::Agent,model)
     end
 
     ##
-    if a.maintenance.policy !=  CorrectiveM
-        schedule_event!(a,CTL_Event(4),a.maintenance.predictive_freq,Array{Int64,1}())
-        if a.maintenance.policy ==  PredictiveM
-            if Symbol("py_integration") in keys(model.properties)
-                #conversion to py
-                ajm_py =  model.py_integration.np.matrix(adjacency_matrix(a.ntw_graph))
-                model.py_integration.opt_init.optimisation_initialisation( ajm_py,
-                model.traffic_dist_params
-                #[1,0.05]
-                , model.mnt_bc_cost, model.mnt_bc_duration, model.mnt_wc_cost, model.mnt_wc_duration)
-            end
+
+    snes = [ getindex(model,ca) for ca in nodes ]
+
+    with_predictions = [ sne.id for sne in snes if sne.maintenance.policy != CorrectiveM ]
+
+    log_info(model.ticks,a.id,"with_predictions: $(with_predictions)")
+
+    with_py_plan = [ sne.id for sne in snes if sne.maintenance.policy == PredictiveM ]
+
+    schedule_event!(a,CTL_Event(4),a.maintenance.predictive_freq,with_predictions)
+
+    if !isempty(with_py_plan)
+        if Symbol("py_integration") in keys(model.properties)
+                    #conversion to py
+                    ajm_py =  model.py_integration.np.matrix(adjacency_matrix(a.ntw_graph))
+                    model.py_integration.opt_init.optimisation_initialisation( ajm_py,
+                    model.traffic_dist_params
+                    , model.mnt_bc_cost, model.mnt_bc_duration, model.mnt_wc_cost, model.mnt_wc_duration)
         end
     end
 end