In the mobile ad hoc network (MANET), nodes are unenergetic nodes; also, it does not provide valuable routing, since it has the limited size for routing information storage for every node, and node multiple path takes more energy for small size of information sharing from sender node to destination node. It maximizes energy consumption and end-to-end delay and reduces network lifetime. In the proposed Energetic and Valuable Path Compendium Routing (EVPC) technique for obtaining energy saving enrichment in mobile ad hoc network process by separating the network into groups and chosen as heads within the groups by using path compendium technique also referred as arbitrary group head chosen depends on communication scheme. Path compendium is known to play an essential task to contain the issues of routing scalability in the network communication process. Through the increasing amount of nodes linked to the network surroundings, emerges the requirement to improve the communication table dimension to hold the improved nodes. To overcome this path compendium, a transmitter scheme is applied. The frustration free communication dimension extension algorithm is used by overriding set of paths and altering advertising node to energetic node with shortest distance path. The frustration free communication dimension extension procedure offers more effectiveness in enhancing the different metrics and principally minimizes the energy consumption by 25% and end-to-end delay by 15% and improves the network lifetime by 35%.

Hadoop Distributed File System is used for storage along with a programming framework MapReduce for processing large datasets allowing parallel processing. The process of handling such complex and vast data and maintaining the performance parameters up to certain level is a difficult task. Hence, an improvised mechanism is proposed here that will enhance the job scheduling capabilities of Hadoop and optimize allocation and utilization of resources. Significantly, an aggregator node is added to the default HDFS framework architecture to improve the performance of Hadoop Name node. In this paper, four entities viz., the name node, secondary name node, aggregator nodes, and data nodes have been modified. Here, the aggregator node assigns jobs to data node, while Name node tracks aggregator nodes. Also, based on the job size and expected execution time, an improvised ant colony optimization method is developed for scheduling jobs.In the end, the results demonstrate notable improvisation over native Hadoop and other approaches.

The process or the framework for MapReduce works in two parts as Mapper and Reducer. The reducer algorithm analyzes the input from the tasks characteristics and generate recommendations for the applicable allocation of the work and the mapper algorithm analyses the perfect or the best fit for the task or the programming running on the Hadoop clusters. The primary challenge is to manage the migration of the virtual machines to make these arrangements suitable to the Hadoop scheduling capabilities. Hence the demand from the research is to justly the Hadoop scheduling capabilities and test the performances of the scheduler strategies for diversified workloads. Also, it is important to design a virtual machine migration algorithm to justify the demands of low power consumptions. Accordingly, this work also coined an energy efficient technique for Hadoop MapReduce jobs scheduling and migration technique. The work results into a novel algorithm and provide significant improvement of the energy consumption. The outcome of the work also analyzes the improvement of other performance parameters like identification of ill-scheduled job and total execution time. This work demonstrates a significant 30% reduction of energy with nearly 40% reduction in job identification and migration time.