With　the　development　of　lightweight　container　virtualization　technology,　it　becomes　possible　to　quickly　and　efficiently　deploy　software　in　edge　computing　environments.　When　allocating　resources　to　containers　in　edge　nodes,　end-to-end　latency　is　an　important　performance　indicator　for　evaluating　strategies.　However,　it　is　often　difficult　to　develop　a　delay　model　of　the　container　cluster　to　obtain　the　real-time　end-to-end　delay　of　the　service　request　flow.　The　state　information　is　always　extremely　complicated　in　edge　computing　environment.　Different　container　service　combinations　reach　edge　computing　nodes　at　different　time　periods.　The　arrival　rate　of　each　container　service　flow　also　changes　over　time.　In　this　paper,　we　first　develop　a　dynamic　M/D/1　queuing　model　to　analyze　the　end-to-end　delay　of　the　data　packets　of　the　container　service　flow　and　use　the　average　packet　delay　as　the　optimization　goal　of　the　Edge　container　resource　allocation　problem.　Then　a　delay-sensitive　resource　allocation　algorithm　based　on　A3C　(Asynchronous　Advantage　Actor?Critic)　is　proposed　to　solve　this　problem.　Finally,　we　utilize　the　ESN　(Echo　state　network)　to　improve　the　traditional　A3C　algorithm.　Simulation　shows　that　the　ESN-based　critic　A3C(EC-A3C)　algorithm　has　better　performance　by　at　least　10.9%　than　other　algorithms　in　terms　of　latency　and　throughput　and　greatly　improves　the　convergence　speed　of　the　network.