ENERGY EFFICIENT LOAD BALANCING FOR CLOUD DATA CENTER

Cloud computing is the latest trend in large-scale distributed computing. It provides diverse services on demand to distributive resources such asservers, software, and databases. One of the challenging problems in cloud data centers is to manage the load of different reconfigurable virtual machines over one another. Thus, in the near future of cloud computing field, providing a mechanism for efficient resource management will be very significant. Many load balancing algorithms have been already implemented and executed to manage the resources efficiently and adequately. The objective of this paper is to analyze shortcomings of existing algorithms and implement a new algorithm which will give optimized load balancingresult

Designing of a simulator architecture for greener data center through knowledge transfer by partners in different sectors

In recent years, increasing demand for internet service providers, cloud services and the information and communications technologies, data centers (DCs) have become a very important place in the energy consumption industry. Furthermore, the energy consumed by the IT industry including DCs reached about 10% of the world's electricity generation. Therefore data centers have become a significant source of CO2 emissions and a crucial player in the electrical power system. In order to predict energy demand better and reduce energy consumption and CO2 emissions in specific national DCs, the GreenDC Project aims developing a decision support tool. This project was funded by EU through H2020 Marie Skłodowska-Curie. It is progressing by secondment activities that consist of knowledge transfer between academic and industrial partners. This paper aims to define and explain the architecture of its decision support tool, GreenDC DSS. The GreenDC DSS architecture composed of four interactional layers which are data layer, math model layer, business logic layer, and user interface layer. In this paper, the design of each layer has been described and the process of entire GreenDC DSS Tool has been examined with an example user scenario which is formed by considering the user requirements of data center managers. Also, it has been shown that a skeleton of the simulator gives optimum working strategies to data center manager

Розробка оптимізаційної моделі розбудови "розумних" енергетичних мереж

Об’єкт дослідження – процес багатокритеріальної оптимізації енергомереж з урахуванням фінансових та часових обмежень, ресурсного потенціалу регіонів та оптимальних логістичних рішень щодо геопросторового розміщення енергопотужностей. Метою дослідження є розробка оптимізаційної моделі розбудови «розумних» та еколого-безпечних енергомереж, реалізація якої дозволить виконання кількісного та якісного оцінювання впливу фінансових, ресурсних, геопросторових та часових факторів на екологічну, економічну та енергетичну ефективність енергомереж

Software Defined Networks based Smart Grid Communication: A Comprehensive Survey

The current power grid is no longer a feasible solution due to ever-increasing user demand of electricity, old infrastructure, and reliability issues and thus require transformation to a better grid a.k.a., smart grid (SG). The key features that distinguish SG from the conventional electrical power grid are its capability to perform two-way communication, demand side management, and real time pricing. Despite all these advantages that SG will bring, there are certain issues which are specific to SG communication system. For instance, network management of current SG systems is complex, time consuming, and done manually. Moreover, SG communication (SGC) system is built on different vendor specific devices and protocols. Therefore, the current SG systems are not protocol independent, thus leading to interoperability issue. Software defined network (SDN) has been proposed to monitor and manage the communication networks globally. This article serves as a comprehensive survey on SDN-based SGC. In this article, we first discuss taxonomy of advantages of SDNbased SGC.We then discuss SDN-based SGC architectures, along with case studies. Our article provides an in-depth discussion on routing schemes for SDN-based SGC. We also provide detailed survey of security and privacy schemes applied to SDN-based SGC. We furthermore present challenges, open issues, and future research directions related to SDN-based SGC.Comment: Accepte

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

Utility of High Resolution Human Settlement Data for Assessment of Electricity Usage Patterns

Electricity is vital for modern human civilization, and its demands are expected to significantly rise due to urban growth, transportation modernization, and increasing industrialization and energy accessibility. Meeting the present and future demands while minimizing the environmental degradation from electricity generation pathways presents a significant sustainability challenge. Urban areas consume around 75% of global energy supply yet urban energy statistics are scarce all over the world, creating a severe hindrance for the much-needed energy sustainability studies. This work explores the scope of geospatial data-driven analysis and modeling to address this challenge. Identification and measurements of human habitats, a key measure, is severely misconceived. A multi-scale analysis of high, medium, and coarse resolution datasets in Egypt and Taiwan illustrates the increasing discrepancies from global to local scales. Analysis of urban morphology revealed that high-resolution datasets could perform much better at all scales in diverse geographies while the power of other datasets rapidly diminishes from the urban core to peripheries. A functional inventory of urban settlements was developed for three cities in the developing world using very high-resolution images and texture analysis. Analysis of correspondence between nighttime lights emission, a proxy of electricity consumption, and the settlement inventory was the conducted. The results highlight the statistically significant relationship between functional settlement types and corresponding light emission, and underline the potential of remote sensing data-driven methods in urban energy usage assessment. Lastly, the lack of urban electricity data was addressed by a geospatial modeling approach in the United States. The estimated urban electricity consumption was externally validated and subsequently used to quantify the effects of urbanization on electricity consumption. The results indicate a 23% lowering of electricity consumption corresponding to a 100% increase in urban population. The results highlight the potential of urbanization in lowering per-capita energy usage. The opportunity and limits to such energy efficiency were identified with regards to urban population density. The findings from this work validate the applicability of geospatial data in urban energy studies and provide unique insights into the relationship between urbanization and electricity demands. The insights from this work could be useful for other sustainability studies