Research On Task Scheduling Strategy Based On Multi-target Revenue Maximization

As a new computing model, the cloud computing can provide a flexible and on-demand storage and computing resources to users via Internet. Virtualization technology is a way to be able to represent abstract methods of computer resources as enabler and important technical support of Cloud computing. The simulation, gathering, sharing and isolation of resources can be achieved through virtualization technology. Furthermore, virtualization technology can also take advantage of the virtual machine to provide the necessary environment for the reliable operation of a variety of applications and rapid deployment. The core feature of cloud computing is on-demand service, which makes cloud computing task allocation and resource scheduling to become technical problems. Although there are interest conflicts between the Ordinary User, Infrastructures Providers and Service Providers, current studies only focus on one of them and how to make one of them benefit. From another perspective, in fact, these three communities of interest. Thus during cloud resource management, you must put the interests of the three as a whole, not only maximize cloud service providers and infrastructure providers profits, but also improve the general user satisfaction.In the stage of service provider (SP) provides cloud services for ordinary users, the user satisfaction and profit upgrade are taken into consideration. In the stage of SP purchase virtual resources from the infrastructure provider (IP), a virtual machine providing model is created. And the dynamic double subpopulation particle swarm optimization is introduced, the particle velocity and position of algorithm are redefined based on virtual machine providing model. In order to improve the convergence speed of double subpopulation particle swarm optimization, the particle velocity update weights is dynamically adjusted based on the fitness value of particles in an iterative process changes. PSO algorithm is easy to fall into local optimum, the immune algorithm is introduced to enhance the diversity of particles, making the algorithm can adjust the global factor dynamically. The improved PSO algorithm can not only find more solution at the beginning of search, but also capable of rapid convergence in the latter so as to find the optimal solution. The fusion of ant colony algorithm and genetic algorithm scheduling policy is introduced when SP provide cloud services to ordinary users. First, genetic operators is used in globally quick search, the initial value of ant colony algorithm’s pheromone is the result of global genetic algorithm, then the exact solution of task scheduling is achieved by using ACO operator, full use the dual advantages of the ant colony algorithm and genetic algorithms on solving NP problems. Experiments show that, in two stages of purchase virtual resources and provide cloud resources to the general users, the two algorithms mentioned above can not only improve SP profits, but also improve customer satisfaction.In stage of IP provides cloud service for SP, in order to maximize the profits of IP, the energy consumption is taken into consideration. This paper presents a predictive model based on gray and credibility ant colony scheduling algorithm for virtual machine migration scheduling policy. CPU resource utilization is an important reference for live migration of virtual machines, and when there is a mutation in the arrival of CPU utilization, if there is no effective scheduling policy, the virtual machine migration occurs unnecessary, thus wasting system overhead. Grey prediction model can estimate the future utilization of a period of virtual machine CPU node. If a load on the host at the current time the CPU utilization is greater than the larger threshold (CPU utilization is less than a small threshold value), and the next three consecutive load prediction values are greater than the threshold (smaller than the threshold), the virtual machine will perform the migration. Experiments show that the algorithm can effectively avoid the frequent migration of virtual machine as a result of the shock caused by the change in CPU utilization, reduce energy consumption, improve IP gains.