With the tremendous increase in the research and development of Internet of Things and Internet of Vehicles, the research scope of Mobile Ad Hoc networks has also increased, rapidly. A number of routing protocols have been proposed to transfer data packets from source to destination nodes. Among these routing protocols, some of them have focused upon multiple paths for data transfer so that route re-discovery time could be minimized. Thus, the end-to-end delay has reduced. The main purpose of these multi-path routing protocols is to balance the load among all or some of the available routes. However, the selection of proper parameters is very crucial and important for load balancing. In this paper, we develop a load-balanced multi-path routing protocol with energy constraints (EE-LB-AOMDV). It first categorized the multiple paths based upon three parameters: hop-count, round-trip time, and residual energy. Then only, it starts data transmission based upon the quality of the path. The simulation results show that the performance of the proposed routing protocol has been improved significantly in terms of packet delivery ratio (PDR), average end-to-end delay (delay), routing overhead (overhead), energy exhausted nodes, and route rediscovery frequencies.
The rapid advancement of the internet has given birth to many technologies. Cloud computing is one of the most emerging technology which aim to process large scale data by using the computational capabilities of shared resources. It gives support to the distributed parallel processing. Using cloud computing, we can process data by paying according to its uses which eliminates the requirement of device by individual users. As cloud computing grows, more users get attracted towards it. However, providing an efficient execution time and load distribution is a major challenging issue in the distributed systems. In our approach, weighted round robin algorithm is used and benefits of Fibonacci sequence is combined which results in better execution time than static round robin. Relevant virtual machines are chosen and jobs are assigned to them. Also, number of resources being utilized concurrently is reduced, which leads to resource saving thereby reducing the cost. There is no need to deploy new resources as resources such as virtual machines are already available.
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