The cloud environment allows enhanced task scheduling techniques for allocating tasks efficiently for smart devices. In this article, the task scheduling technique of artificial immune system (AIS), randomized gossip algorithm (RGA), and particle swarm optimization (PSO) implemented as proposed design to achieve uniform distribution in an optimized manner. The AIS technique is mainly focused on optimization and network security which is comprised of many applications. The peer-to-peer networks of sharing the information and make the interconnection possible are achieved by a RGA. For this kind of broadcasting the information, the RGA algorithms are mainly suitable. The PSO algorithm was executed for the independent task and allocated in a sensible self-organized way. The proposed method response time, performance ratio, and the makespan ratio defines as the total length of the schedule measured and compared with other time scheduling algorithms discussed later in this method. The above-proposed algorithm is used to allocate the resources efficiently even though the tasks have increased further. The comparative analysis of this proposed work was figured and tabulated. The decrease in makespan ratio, reduced response time, uniform distribution of tasks, no failures or crashes as disruption, and reduced overload make the proposed system optimized.

云环境允许使用增强的任务调度技术来为智能设备有效分配任务。在本文中，人工免疫系统（AIS），随机八卦算法（RGA）和粒子群优化（PSO）的任务调度技术可作为拟议设计实现，以优化方式实现均匀分布。AIS技术主要集中在优化和网络安全性上，它由许多应用程序组成。RGA实现了共享信息并使互连成为可能的对等网络。对于这种广播信息，RGA算法主要适用。PSO算法针对独立任务执行，并以明智的自组织方式进行分配。建议的方法响应时间，性能比，延展率定义为所测得的时间表的总长度，并与该方法后面讨论的其他时间表算法进行比较。即使任务进一步增加，上述提议的算法仍可用于有效地分配资源。对该拟议工作进行了比较分析，并将其制成表格。制造时间比率的降低，响应时间的缩短，任务的均匀分配，无故障或因中断而导致的崩溃以及过载的减少，使所建议的系统得到了优化。