Publicación: AN EDGE SERVER PLACEMENT METHOD BASED ON REINFORCEMENT LEARNING
Fecha
2022
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Entropy
Resumen
IN MOBILE EDGE COMPUTING SYSTEMS, THE EDGE SERVER PLACEMENT PROBLEM IS MAINLY TACKLED AS A MULTI-OBJECTIVE OPTIMIZATION PROBLEM AND SOLVED WITH MIXED INTEGER PROGRAMMING, HEURISTIC OR META-HEURISTIC ALGORITHMS, ETC. THESE METHODS, HOWEVER, HAVE PROFOUND DEFECT IMPLICATIONS SUCH AS POOR SCALABILITY, LOCAL OPTIMAL SOLUTIONS, AND PARAMETER TUNING DIFFICULTIES. TO OVERCOME THESE DEFECTS, WE PROPOSE A NOVEL EDGE SERVER PLACEMENT ALGORITHM BASED ON DEEP Q-NETWORK AND REINFORCEMENT LEARNING, DUBBED DQN-ESPA, WHICH CAN ACHIEVE OPTIMAL PLACEMENTS WITHOUT RELYING ON PREVIOUS PLACEMENT EXPERIENCE. IN DQN-ESPA, THE EDGE SERVER PLACEMENT PROBLEM IS MODELED AS A MARKOV DECISION PROCESS, WHICH IS FORMALIZED WITH THE STATE SPACE, ACTION SPACE AND REWARD FUNCTION, AND IT IS SUBSEQUENTLY SOLVED USING A REINFORCEMENT LEARNING ALGORITHM. EXPERIMENTAL RESULTS USING REAL DATASETS FROM SHANGHAI TELECOM SHOW THAT DQN-ESPA OUTPERFORMS STATE-OF-THE-ART ALGORITHMS SUCH AS SIMULATED ANNEALING PLACEMENT ALGORITHM (SAPA), TOP-K PLACEMENT ALGORITHM (TKPA), K-MEANS PLACEMENT ALGORITHM (KMPA), AND RANDOM PLACEMENT ALGORITHM (RPA). IN PARTICULAR, WITH A COMPREHENSIVE CONSIDERATION OF ACCESS DELAY AND WORKLOAD BALANCE, DQN-ESPA ACHIEVES UP TO 13.40% AND 15.54% BETTER PLACEMENT PERFORMANCE FOR 100 AND 300 EDGE SERVERS RESPECTIVELY.
