This paper proposed a multi-dimensional QoS cloud computing task scheduling algorithm based on improved ant colony algorithm, considering QoS demand of users and load balancing of cloud platform comprehensively. First, this paper defines a QoS model composed of the completion time and execution cost of tasks, and defines the cloud platform load balancing constraint function. Secondly, in view of the shortcomings of ant colony algorithm such as slow convergence speed and easy to fall into local optimum, the pheromone update method and expected heuristic function are improved, and the pheromone strength is dynamically changed. Finally, the simulation is carried out in cloudsim and compared with the ACS algorithm and the MMAS algorithm. Experimental results show that the algorithm in this paper is better than these two algorithms in terms of user satisfaction and cloud platform load.
With the development of wireless communication technologies and mobile devices, the P2P network technology has the conditions to be used in wireless environments. Resource search is to discover requested object in mobile P2P network (mobile peer-to-peer network), which is one of the core issues of mobile P2P application. Aiming at low efficiency of resource search in mobile P2P network, a search strategy using improved random walk based on node reputation (SSRWBR) is proposed in the paper. By introducing a reputation mechanism based on the random walk model, the neighbor ultrapeer with maximum reputation value is selected to forward the query walker during the search process, which can solve the problem of low resource efficient searching.
Aimed at the real-time request of dynamic scheduling to product resource, an optimization method of parallel dynamic chain real-time available resources was put forward. Proceed from real-time tracing of resource information influencing scheduling tasks dynamic property, established a resource informations real-time tracing back and optimization model, which used module and parallel process mechanism to different kinds of real-time traced back resource information. The mechanism processed global dynamic feedback tracing for every module and optimized available resources primarily, on this basis, graded the primary available resources and gave a real-time candidate resource set. Through one example of one gear production scheduling, the methods validity was tested.
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