International Journal of Computer Applications (0975 – 8887) Volume 142 – No.2, May 2016 1 An Energy Computation in Distributed Computing Environment through Bellman-Ford Algorithm Kamlesh Kumar Verma Department of Computer Science Babasaheb Bhimrao Ambedkar University Vidya Vihar Raebariely Road Lucknow 226025, India Vipin Saxena Department of Computer Science Babasaheb Bhimrao Ambedkar University Vidya Vihar Raebariely Road Lucknow 226025, India ABSTRACT In the current scenario, energy optimization for the electrical components used in the hand-held devices is a broad area of research. Energy principle for information technology contains its own specific energy behavior. The energy costs in server centre are now compatibility to the cost of hardware devices and other compare devices. In the processor device system, heat existing is a major cause of limiting changes in the performance evaluation. In laptop, scanners, computers, cell phones, printers, i-pods, and other digital devices, which are portable, reduced the power consumption converts into the battery long life manner. The energy consumption is now presenting challenges as a performance measure in computers, processing the task execution time. The energy is linked with the execution time capacity, where comparable patterns have been recognized by a combination of hardware devices, software devices, and algorithms. In this paper, the design of energy-efficient computer systems is proposed by the use of Bellman-ford algorithmic approach. A model is proposed for finding the performance of the system. Computed results are depicted in the form of tables and graphs. Keywords Energy Optimization, Wired Networks Topology, Packet Routing, QoS, Bellman-ford Algorithm Optimization Techniques. 1. INTRODUCTION Today’s networks energy saving techniques is very challenging area in the Information and Communication Technology (ICT) world. The energy consumption is very costly in the network for the system’s parameters like CPU, motherboard, hard disk and the power level systems. There are three important factors for the energy consumptions namely (a) data forwarding or data transmitting from source to destination. (a) Speed to the flow of data. (b) Transmission of data packets. There are three types of methods for finding the energy consumptions (a) dynamic voltage scaling (b) dynamic frequency scaling (c) clock gating. These methods are very precious for network energy consumptions. At the dynamic voltage scaling, all devices are working with the voltage dependent component. In voltage scaling, the voltage varies with the network performance as if network’s flow in high, then data transmission is capturing high energy over the system configuration. If the networks gain, the voltage is low then the data speed is low and energy consumption will be getting low. So frequency works, whereas the same as working with voltage if frequency and voltage with high, then energy is high consumption. But either frequency or voltage are related with reciprocal with each other. In electrically. Voltage = Charge/Capacitance Volt, (1) Where, the charge is Q= i×t Coulomb. Now consider frequency scaling which explains to the technique that reduces the energy consumption by lower the processor’s frequency. If high frequency enables high energy and lower frequency makes low energy consumption. If constraint occurs for the reduce voltage, energy to the workload balance will be reduced. These methods can be applied to other energy-consuming devices, as hard disk drives, motherboard, processor, and electromechanical devices. There are algorithms to find the minimal speed of data throughput and schedules for transmission packet scaling and finding the speed scaling. The power performance varies the main challenges for device system architecture and CPU temperature. In high temperatures performance varies according to reliability and cooling systems. The temperatures increase the energy consumption. The power optimization performance has been done for optimizing energy required for data storage and access data transmission. 2. RELATED WORK The goal of the present work is to reduce the energy consumption in network environment. Let us briefly explain the literature review on the present work. Lange et al. [1] have predicted for energy consumption in broadband network and the highest energy consumption growth rates are foreseen in the data centers and IP backbone networks. They proposed a load factor networking and energy aware system. Bolla et al. [2] have given two features of green networking, firstly power consumption for next generation networking and secondly provided a detail survey for energy performance design mechanism issues. Tseng et al. [3] have described a compression algorithm, which is designed to solve the problem of link on/off and weight assignment problem issues to minimize a network’s energy consumption. Ballga et al. [4] have described analysis and technology for energy consumption of data throughput in network’s like DSL, HFC networks, passive optical networks, point to point optical systems, W-CDMA, WiMAX. Gaona et al. [5] have given the design of energy-efficient in hardware transactional memory systems. Chiaraviglio et al. [6] have given the model based energy consumption mechanism such as device architecture and load for networks. They have proposed also algorithm for energy saving capabilities in sleep mode and active mode. Castene et al. [7] have proposed a framework formula, E=mc 2 for energy performance modelling in distributing computing. They have used Icancloud for simulations. Schien et al. [8] have proposed a model to analyze and assessing variability for the energy consumption at the during downloading multimedia applications. Lin et al. [9] have proposed a method for minimizing the energy for NP-complete problem solution by Dijkstra’s algorithm and Yen’s k-shortest paths algorithm. They have evaluated in Abilene network (eg. Real