Abstract. Recently, increasing battery lifetime in wireless sensor networks has turned out to be one of the major challenges faced by researchers. The sensor nodes in wireless sensor networks use a battery as their power source, which is hard to replace during deployment. Low Energy Adaptive Clustering Hierarchy (LEACH) is one of the most prominent wireless sensor network routing protocols that have been proposed to improve network lifetime by utilizing energy-efficient clustering. However, LEACH has some issues related to clusterhead selection, where the selection is done randomly. This leads to rapid loss of energy in the network. Improved LEACH is a LEACH alternative that has the ability to increase network lifetime by using the nodes' residual energy and their distance to the base station to select cluster-head nodes. However, Improved LEACH causes reduced stability, where the stability period is the duration before the death of the first node. The network stability period is important for applications that require reliable feedback from the network. Thus, we were motivated to investigate the Improved LEACH algorithm and to try to solve the stability problem. A new protocol is proposed in this paper: Stable Improved Low Energy Adaptive Clustering Hierarchy (SILEACH), which was developed to overcome the flaws of the Improved LEACH protocol. SILEACH balances the load between the nodes by utilizing an optimized method that considers the nodes' distance to the base station and their residual energy to select the clusterhead nodes and considers the nodes' distance to the cluster head and the base station to form clusters. The simulation results revealed that SILEACH is significantly more efficient than Improved LEACH in terms of stability period and network lifetime.

Virtual machines are assigned to hosts, depending on its current resource usage and not considering their overall utilization. It is one of the main problems in cloud computing that can reduces the system performance. The scheduling is used to schedule tasks for better utilization of resources by allocating certain tasks to particular resources at a particular time. The purpose of scheduling is to select the most excellent and suitable resource available to execute the tasks or to assign computer machines to execute tasks with minimal completion time is but still feasible. An efficient task scheduling algorithm is needed for improve the system performance. In this paper, the focus is on improving the virtual machines scheduling performance for makespan and cost. The proposed process of scheduling includes three main processes. The first process is the Clustering Formation based on the characteristics such as Processor, Memory and Bandwidth. The second process is known as the Hyper Analytical Task Scheduling Algorithm, and based on the scheduled tasks, the Policy-based Profit Maximization Algorithm was proposed in the final process. The performance comparison of the proposed work is analyzed through some empirical results. The result shows that the proposed work significantly reduces the makespan of task scheduling and gives high profit compared with the other scheduling algorithms.

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