Nowadays, a trending technology that provides a virtualized computer resources based on the internet is named as cloud computing, these clouds performance mostly depends on the various factors among the load balancing. The allocation of the dynamic workload in between the cloud systems and equally shares the resources so that no database server is overloaded or under loaded is technically referred to as load balancing (LB). Therefore, in cloud an active load balancing scheme can perhaps enhance the reliability, services and the utilization of resources as well. In this manuscript, the benefits are integrated for Harries Hawks Optimization and Pigeon inspired Optimization Algorithm to create efficient load balancing scheme, which ensures the optimal resources utilizations with tasks response time. The proposed approach is implemented in JAVA Net beans IDE incorporated in the cloudsim framework that is analyzed based on different number of task in order to assess the performance. However, the simulation outcomes demonstrate that the proposed Hawks Optimization and Pigeon inspired Optimization algorithm based load balancing scheme is significantly balance the load optimally amid the Virtual Machines within a shorter period of time than the existing algorithms. The efficiency of the proposed method is 97% compared to the other existing methods. The computational time, cost, throughput analysis, make span, latency, execution time are determined and gets analysed, compared with the Harries Hawks Optimization, Spider Monkey Algorithm, Ant Colony Optimization and Honey Bee Optimization.

如今，一种基于Internet提供虚拟化计算机资源的趋势技术被称为云计算，这些云的性能主要取决于负载平衡之间的各种因素。在云系统之间分配动态工作负载并平均分配资源，以使数据库服务器不会过载或负载不足，在技术上称为负载平衡（LB）。因此，在云中，主动负载平衡方案也许可以提高可靠性，服务和资源利用率。在本手稿中，Harris Hawks Optimization和Pigeon启发式优化算法的优势被整合在一起，以创建有效的负载平衡方案，从而确保在任务响应时间下实现最佳资源利用。所提出的方法是在纳入cloudimsim框架的JAVA Net bean IDE中实现的，该框架基于不同任务数进行了分析，以评估性能。但是，仿真结果表明，所提出的基于Hawks优化和Pigeon启发式优化算法的负载平衡方案可以在比现有算法短的时间内显着平衡虚拟机之间的最佳负载。与其他现有方法相比，该方法的效率为97％。与Harries Hawks优化，蜘蛛猴算法，蚁群优化和蜜蜂优化相比，确定并分析了计算时间，成本，吞吐量分析，制造跨度，等待时间，执行时间。