Load balancing is an important task on virtual machines (VMs) and also an essential aspect of task scheduling in clouds. When some Virtual machines are overloaded with tasks and other virtual machines are under loaded, the load needs to be balanced to accomplish optimum machine utilization. This paper represents an existing technique “artificial bee colony algorithm” which shows a low convergence rate to the global minimum even at high numbers of dimensions. The objective of this paper is to propose the integration of artificial bee colony with tabu search technique for cloud computing environment to enhance energy consumption rate. The main improvement is makespan 28.4 which aim to attain a well balanced load across virtual machines. The simulation result shows that the proposed algorithm is beneficial when compared with existing algorithms.

WSNs are becoming popular in real world uses. Due to resource-constrained and battery-aware features of the sensor; WSNs energy use in a major research topic was found to be interesting. WSNs contain battery powered nodes that for certain action or tasks are connected with the base station. The lifetime of battery-powered sensor nodes, after the consumption of the battery will be dead in WSNs. To prolong the lifetime of WSNs the energy has to be well organized. The cluster head selection and assignment, and construction of clusters require additional overhead. Cluster nodes distributed algorithms designed incorrectly can lead to isolation from cluster heads. Such isolated nodes waste large amount of energy to communicate with the sink. In this paper, energy conservation techniques related to WSNs are discussed. Also, the existing energy conservation algorithms are reviewed and the comparative analysis is also performed.