%PDF-1.5 % 4 0 obj << /S /GoTo /D (section.1) >> endobj 7 0 obj (1 Introduction) endobj 8 0 obj << /S /GoTo /D (subsection.1.1) >> endobj 11 0 obj (1.1 Background and Aim) endobj 12 0 obj << /S /GoTo /D (subsection.1.2) >> endobj 15 0 obj (1.2 Main Contributions) endobj 16 0 obj << /S /GoTo /D (subsection.1.3) >> endobj 19 0 obj (1.3 Paper Organization) endobj 20 0 obj << /S /GoTo /D (section.2) >> endobj 23 0 obj (2 Reference Scenario and Architecture) endobj 24 0 obj << /S /GoTo /D (section.3) >> endobj 27 0 obj (3 Problem Description and Model) endobj 28 0 obj << /S /GoTo /D (subsection.3.1) >> endobj 31 0 obj (3.1 Problem Modeling) endobj 32 0 obj << /S /GoTo /D (subsection.3.2) >> endobj 35 0 obj (3.2 Problem Statement) endobj 36 0 obj << /S /GoTo /D (section.4) >> endobj 39 0 obj (4 Related Work) endobj 40 0 obj << /S /GoTo /D (section.5) >> endobj 43 0 obj (5 Service Mapping Based on Reinforcement Learning) endobj 44 0 obj << /S /GoTo /D (subsection.5.1) >> endobj 47 0 obj (5.1 Markov Decision Process Modelling of the Service Mapping Problem) endobj 48 0 obj << /S /GoTo /D (subsubsection.5.1.1) >> endobj 51 0 obj (5.1.1 State Space .) endobj 52 0 obj << /S /GoTo /D (subsubsection.5.1.2) >> endobj 55 0 obj (5.1.2 Action Space .) endobj 56 0 obj << /S /GoTo /D (subsubsection.5.1.3) >> endobj 59 0 obj (5.1.3 Transition Matrix .) endobj 60 0 obj << /S /GoTo /D (subsubsection.5.1.4) >> endobj 63 0 obj (5.1.4 Reward Function .) endobj 64 0 obj << /S /GoTo /D (subsection.5.2) >> endobj 67 0 obj (5.2 Proposed Service Mapping Algorithm Based on Reinforcement Learning) endobj 68 0 obj << /S /GoTo /D (section.6) >> endobj 71 0 obj (6 Service Mapping Based on Integer Linear Programming) endobj 72 0 obj << /S /GoTo /D (section.7) >> endobj 75 0 obj (7 Simulation Results) endobj 76 0 obj << /S /GoTo /D (subsection.7.1) >> endobj 79 0 obj (7.1 Simulation Setup) endobj 80 0 obj << /S /GoTo /D (subsection.7.2) >> endobj 83 0 obj (7.2 Maximization of total service acceptance) endobj 84 0 obj << /S /GoTo /D (subsection.7.3) >> endobj 87 0 obj (7.3 Maximization of cumulative mapping reward) endobj 88 0 obj << /S /GoTo /D (section.8) >> endobj 91 0 obj (8 Conclusions) endobj 92 0 obj << /S /GoTo /D [93 0 R /Fit] >> endobj 105 0 obj << /Length 2979 /Filter /FlateDecode >> stream xڭY[w6~St }YvN}J{z
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