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Optimization of cloud computing task execution time and user QoS utility by improved particle swarm optimization

机译：通过改进的粒子群优化优化云计算任务执行时间和用户QoS实用程序的优化

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摘要

In order to optimize the quality of service (QoS) and execution time of task, a new resource scheduling based on improved particle swarm optimization (IPSO) is proposed to improve the efficiency and superiority. In cloud computing, the first principle of resource scheduling is to meet the needs of users, and the goal is to optimize the resource scheduling scheme and maximize the overall efficiency. This requires that the scheduling of cloud computing resources should be flexible, real-time and efficient. In this way, the mass resources of cloud computing can effectively meet the needs of the cloud users. Field Programmable Gate Arrays (FPGA), high performance and energy efficiency in one field. Most of them would have been the particle algorithm. The current technological development is still in-depth at super-resolution image research at an unprecedentedly fast pace. In particular, systemic origin applications get a lot of attention because they have a wide range of abnormal results. The scientific resource scheduling algorithm is the key to improve the efficiency of cloud computing resources distribution and the level of cloud services. In addition, the physical model of cloud computing resource scheduling is established. The performance of the IPSO algorithm applied to cloud computing resource scheduling is analysed in the design experiment. The comparison result shows that the new algorithm improves the PSO by taking full account of the user's Qu's requirements and the load balance of the cloud environment. In conclusion, the research on cloud computing resource scheduling based on IPSO can solve the problem of resource scheduling to a certain extent.
机译：为了优化服务质量（QoS）和任务的执行时间，提出了一种基于改进的粒子群优化（IPSO）的新资源调度，以提高效率和优越性。在云计算中，资源调度的第一个原则是满足用户的需求，目标是优化资源调度方案并最大限度地提高整体效率。这要求云计算资源的调度应该是灵活的，实时和高效的。通过这种方式，云计算的大众资源可以有效地满足云用户的需求。现场可编程门阵列（FPGA），高性能和能效在一个字段中。其中大多数都是粒子算法。目前的技术发展仍深入了解超级分辨率的图像研究，以前所未有的快速节奏。特别是，系统原产地应用得很多，因为它们具有广泛的异常结果。科学资源调度算法是提高云计算资源分布效率和云服务级别的关键。此外，建立了云计算资源调度的物理模型。在设计实验中分析了应用于云计算资源调度的IPSO算法的性能。比较结果表明，新算法通过在用户的QU的要求和云环境的负载余额完全叙述来改善PSO。总之，基于IPSO的云计算资源调度研究可以解决一定程度的资源调度问题。

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