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Dynamic combination of improved max-min and ant colony algorithm for load balancing in cloud system

机译:改进的最大-最小与蚁群算法的动态结合以实现云系统中的负载均衡

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Cloud Computing is a new technology. Cloud Computing provides many facilities like on demand self-services, unlimited resources, rapid elasticity and measured services to end users. All users access these resources directly through internet. Users can use these resources and services as they want on pay per use concept. In cloud computing architecture load balancing is a very important issue. There are many algorithms for load balancing in cloud computing. All algorithms work different ways. We proposed a Improved Max-Min Ant colony Algorithm. Improved Max-Min used the concept of original Max-Min. Improved Max-Min is based on the execution time not on completion time as a selection basis. The main motive of our work is to balance the total load of cloud system .We try to minimizing the total makespan. We simulated results using the CloudSim toolkit. Results show the comparison between improved max min and new hybrid improved Max-Min ant approach. It mainly focuses on total processing time and processing cost.
机译:云计算是一项新技术。云计算为最终用户提供了许多功能,例如按需自助服务,无限资源,快速弹性和可衡量的服务。所有用户都直接通过Internet访问这些资源。用户可以按需使用按需使用这些资源和服务。在云计算架构中,负载平衡是一个非常重要的问题。云计算中有许多用于负载平衡的算法。所有算法的工作方式都不同。我们提出了一种改进的最大-最小蚁群算法。改进的最大-最小使用原始最大-最小的概念。改进的最大-最小基于执行时间而不是基于完成时间作为选择依据。我们工作的主要动机是平衡云系统的总负载。我们试图使总有效期最小化。我们使用CloudSim工具包模拟了结果。结果显示了改进的最大最小和新的混合改进的最大最小蚂蚁方法之间的比较。它主要关注总处理时间和处理成本。

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