In this paper, we proposed a campus equipment ubiquitous-management system which is based on a genetic algorithm approach in cloud server. The system uses radio frequency identification （RFID） to monitor the status ...In this paper, we proposed a campus equipment ubiquitous-management system which is based on a genetic algorithm approach in cloud server. The system uses radio frequency identification （RFID） to monitor the status of equipment in real time, and uses wire or wireless network to send real-time situation to display on manager＇s PC or PDA. In addition, the system will also synchronize with database to record and reserve message. Furthermore, the status will display not only to a single manager but also a number of managers. In order to increase efficiency between graphical user interface （GUI） and database, the system adopts SqlDependency object of ADO.NET so that any changed situation of the database could be known immediately and synchronized with manager＇s PC or PDA. Because the problem of the equipment utilization is an NP-complete （non-deterministic polynomial） problem, we apply genetic algorithm to enhance the efficiency of finding optimum solution for equipment utilization. We assign constraints into the system, and the system will post back the optimum solution simultaneously on the screen. As a consequence, we compare our genetic algorithm based approach （GA） with the simulated annealing based approach （SA） for maximizing the equipment utilization. Experimental result shows that our GA approach achieves an average 79.66% improvement in equipment utilization in an acceptable run time.展开更多
The problem of joint radio and cloud resources allocation is studied for heterogeneous mobile cloud computing networks. The objective of the proposed joint resource allocation schemes is to maximize the total utility ...The problem of joint radio and cloud resources allocation is studied for heterogeneous mobile cloud computing networks. The objective of the proposed joint resource allocation schemes is to maximize the total utility of users as well as satisfy the required quality of service(QoS) such as the end-to-end response latency experienced by each user. We formulate the problem of joint resource allocation as a combinatorial optimization problem. Three evolutionary approaches are considered to solve the problem: genetic algorithm(GA), ant colony optimization with genetic algorithm(ACO-GA), and quantum genetic algorithm(QGA). To decrease the time complexity, we propose a mapping process between the resource allocation matrix and the chromosome of GA, ACO-GA, and QGA, search the available radio and cloud resource pairs based on the resource availability matrixes for ACOGA, and encode the difference value between the allocated resources and the minimum resource requirement for QGA. Extensive simulation results show that our proposed methods greatly outperform the existing algorithms in terms of running time, the accuracy of final results, the total utility, resource utilization and the end-to-end response latency guaranteeing.展开更多
文摘In this paper, we proposed a campus equipment ubiquitous-management system which is based on a genetic algorithm approach in cloud server. The system uses radio frequency identification （RFID） to monitor the status of equipment in real time, and uses wire or wireless network to send real-time situation to display on manager＇s PC or PDA. In addition, the system will also synchronize with database to record and reserve message. Furthermore, the status will display not only to a single manager but also a number of managers. In order to increase efficiency between graphical user interface （GUI） and database, the system adopts SqlDependency object of ADO.NET so that any changed situation of the database could be known immediately and synchronized with manager＇s PC or PDA. Because the problem of the equipment utilization is an NP-complete （non-deterministic polynomial） problem, we apply genetic algorithm to enhance the efficiency of finding optimum solution for equipment utilization. We assign constraints into the system, and the system will post back the optimum solution simultaneously on the screen. As a consequence, we compare our genetic algorithm based approach （GA） with the simulated annealing based approach （SA） for maximizing the equipment utilization. Experimental result shows that our GA approach achieves an average 79.66% improvement in equipment utilization in an acceptable run time.
基金supported by the National Natural Science Foundation of China (No. 61741102, No. 61471164)China Scholarship Council
文摘The problem of joint radio and cloud resources allocation is studied for heterogeneous mobile cloud computing networks. The objective of the proposed joint resource allocation schemes is to maximize the total utility of users as well as satisfy the required quality of service(QoS) such as the end-to-end response latency experienced by each user. We formulate the problem of joint resource allocation as a combinatorial optimization problem. Three evolutionary approaches are considered to solve the problem: genetic algorithm(GA), ant colony optimization with genetic algorithm(ACO-GA), and quantum genetic algorithm(QGA). To decrease the time complexity, we propose a mapping process between the resource allocation matrix and the chromosome of GA, ACO-GA, and QGA, search the available radio and cloud resource pairs based on the resource availability matrixes for ACOGA, and encode the difference value between the allocated resources and the minimum resource requirement for QGA. Extensive simulation results show that our proposed methods greatly outperform the existing algorithms in terms of running time, the accuracy of final results, the total utility, resource utilization and the end-to-end response latency guaranteeing.
