In order to solve the problem of efficiently assigning tasks in an ad-hoc mobile cloud( AMC),a task assignment algorithm based on the heuristic algorithm is proposed. The proposed task assignment algorithm based on pa...In order to solve the problem of efficiently assigning tasks in an ad-hoc mobile cloud( AMC),a task assignment algorithm based on the heuristic algorithm is proposed. The proposed task assignment algorithm based on particle swarm optimization and simulated annealing( PSO-SA) transforms the dependencies between tasks into a directed acyclic graph( DAG) model. The number in each node represents the computation workload of each task and the number on each edge represents the workload produced by the transmission. In order to simulate the environment of task assignment in AMC,mathematical models are developed to describe the dependencies between tasks and the costs of each task are defined. PSO-SA is used to make the decision for task assignment and for minimizing the cost of all devices,which includes the energy consumption and time delay of all devices.PSO-SA also takes the advantage of both particle swarm optimization and simulated annealing by selecting an optimal solution with a certain probability to avoid falling into local optimal solution and to guarantee the convergence speed. The simulation results show that compared with other existing algorithms,the PSO-SA has a smaller cost and the result of PSO-SA can be very close to the optimal solution.展开更多
To improve the quality of computation experience for mobile devices,mobile edge computing(MEC)is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network,which s...To improve the quality of computation experience for mobile devices,mobile edge computing(MEC)is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network,which supports both traditional communication and MEC services.However,this kind of intensive computing problem is a high dimensional NP hard problem,and some machine learning methods do not have a good effect on solving this problem.In this paper,the Markov decision process model is established to find the excellent task offloading scheme,which maximizes the long-term utility performance,so as to make the best offloading decision according to the queue state,energy queue state and channel quality between mobile users and BS.In order to explore the curse of high dimension in state space,a candidate network is proposed based on edge computing optimize offloading(ECOO)algorithm with the application of deep deterministic policy gradient algorithm.Through simulation experiments,it is proved that the ECOO algorithm is superior to some deep reinforcement learning algorithms in terms of energy consumption and time delay.So the ECOO is good at dealing with high dimensional problems.展开更多
In E-Commerce, consumers and service suppliers can find the services through the searching of Mobile Agents (MA). The suppliers disassemble the service requests of consumers into the sub-requests. Then suppliers respo...In E-Commerce, consumers and service suppliers can find the services through the searching of Mobile Agents (MA). The suppliers disassemble the service requests of consumers into the sub-requests. Then suppliers respond the sub-requests cooperatively. Thus the Service Supply Chain (SSC) can be formed. But the existing bottom-up and up-bottom supply chain formation fashions cannot be adapted to the SSC in distributed environment of E-Commerce. Task Dependency Network is exploited to illustrate the service relationship among consumers and suppliers. The formation of SSC with some simulations is elaborated. Then the influence on the formation of SSC caused by the type of service suppliers, the quantities of MA and its variety in number is elucidated.展开更多
基金The National Natural Science Foundation of China(No.61741102,61471164,61601122)the Fundamental Research Funds for the Central Universities(No.SJLX_160040)
文摘In order to solve the problem of efficiently assigning tasks in an ad-hoc mobile cloud( AMC),a task assignment algorithm based on the heuristic algorithm is proposed. The proposed task assignment algorithm based on particle swarm optimization and simulated annealing( PSO-SA) transforms the dependencies between tasks into a directed acyclic graph( DAG) model. The number in each node represents the computation workload of each task and the number on each edge represents the workload produced by the transmission. In order to simulate the environment of task assignment in AMC,mathematical models are developed to describe the dependencies between tasks and the costs of each task are defined. PSO-SA is used to make the decision for task assignment and for minimizing the cost of all devices,which includes the energy consumption and time delay of all devices.PSO-SA also takes the advantage of both particle swarm optimization and simulated annealing by selecting an optimal solution with a certain probability to avoid falling into local optimal solution and to guarantee the convergence speed. The simulation results show that compared with other existing algorithms,the PSO-SA has a smaller cost and the result of PSO-SA can be very close to the optimal solution.
基金National Natural Science Foundation of China(No.11461038)Science and Technology Support Program of Gansu Province(No.144NKCA040)。
文摘To improve the quality of computation experience for mobile devices,mobile edge computing(MEC)is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network,which supports both traditional communication and MEC services.However,this kind of intensive computing problem is a high dimensional NP hard problem,and some machine learning methods do not have a good effect on solving this problem.In this paper,the Markov decision process model is established to find the excellent task offloading scheme,which maximizes the long-term utility performance,so as to make the best offloading decision according to the queue state,energy queue state and channel quality between mobile users and BS.In order to explore the curse of high dimension in state space,a candidate network is proposed based on edge computing optimize offloading(ECOO)algorithm with the application of deep deterministic policy gradient algorithm.Through simulation experiments,it is proved that the ECOO algorithm is superior to some deep reinforcement learning algorithms in terms of energy consumption and time delay.So the ECOO is good at dealing with high dimensional problems.
文摘In E-Commerce, consumers and service suppliers can find the services through the searching of Mobile Agents (MA). The suppliers disassemble the service requests of consumers into the sub-requests. Then suppliers respond the sub-requests cooperatively. Thus the Service Supply Chain (SSC) can be formed. But the existing bottom-up and up-bottom supply chain formation fashions cannot be adapted to the SSC in distributed environment of E-Commerce. Task Dependency Network is exploited to illustrate the service relationship among consumers and suppliers. The formation of SSC with some simulations is elaborated. Then the influence on the formation of SSC caused by the type of service suppliers, the quantities of MA and its variety in number is elucidated.
