Cloud Computing offers indispensable infrastructure for storage and computing facilities for development of diversified services. The large utilization of resources leads to increased energy consumption that has imposed a limit on performance growth. Owing to high operational costs and carbon dioxide footprints, an efficient energy management technique needs to be developed and deployed that reduces overall energy consumption of a cloud environment while maximizing the resource utilization. In the first phase of this research, some virtual machine migration techniques were explored. In the second phase, a virtual machine migration technique has been implemented which aims at reducing energy consumption in cloud datacentres.
General TermsVirtual Machine Migration, Bin Packing Algorithms.
In this paper, we describe the approaches for routing in Wireless Sensor Networks. Their characteristics are discussed. An efficient routing approach is then proposed using genetic algorithm which is based on energy equations and use of advanced nodes having high energy than normal nodes. This intelligent cluster routing is then compared with SEP routing.
Due to significant advancement in wireless communication, wireless sensor networks (WSNs) have attracted great attention in recent years. WSNs are randomly deployed, battery operated autonomous systems consisting of large number of sensors nodes which are responsible for transmitting the real-time sensed data for a specific application in the monitoring area to the base station where it can be further processed and analyzed. However, due to wireless communication, the network is easily compromised. Solutions dedicated to wire networks are not suited in the resource constrained wireless network. There is still a scope for wide research potential in the field of wireless sensor network security. In this paper, we analyzed the issues related to security in WSNs and also highlight the research area in the field of wireless sensor networks.
Vehicle Routing Problem with Time Window (VRPTW), an extension of VRP, is a complex combinatorial problem having many real life applications. It can be described as the problem of minimizing the total route cost while satisfying the capacity and time window constraint. Ant Colony System (ACS) is a meta heuristic that is often applied to solve VRPTW. In this paper an attempt has been made to enhance the already existing ant colony system to solve the problem efficiently. Experimentation with the Solomon data sheet is performed and compared with best known results available in literature.
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