Search results

The study visualizes the link between environment accounting & triple bottom line, quantitative environmental reporting & standard method, voluntary environmental disclosure & legal requirement, size of company & volume of environmental disclosure, material flow analysis & life cycle assessment to achieve sustainable development in Bangladeshi corporation. Therefore, the purpose of the study is to investigate the role of these factors to achieve sustainable development in Bangladeshi corporation. To investigate the role of these factors, ten factors that significantly contribute to achieve sustainable development were determined. A set of closed-minded questionnaire was developed on the basis of these factors to collect the data from employees & employers. Questionnaire was administered by using statistical tools such as matrix, cross tabulation & Paired Samples Tests as a data collection tool and analyses. Research finding shows that sustainability of corporation was associated with the performance of economic, social, and environment. Other factors like quantitative environmental reporting, standard method, voluntary environmental disclosure, legal requirement, size of the company, volume of environmental disclosure, material flow analysis & life cycle assessment were found that they worked as a complement to enhance the performance of economic, social, and environment to achieve sustainable development in Bangladeshi corporation.

The purpose of this paper is to present an approach to reduce energy consumption in data centers. Subsequently, it reduces electricity bills and carbon dioxide footprints resulting from their use.

The authors present a mathematical model of the energy dissipation optimization problem. The authors formulate analytically the server selection problem and the supply air temperature as a non-linear programming, and propose an algorithm to solve it dynamically.

A simulation study on SimWare, using real workload traces, shows considerable savings for different data center sizes and utilization rates as compared to three other classic algorithms. The results prove that the proposed algorithm is efficient in handling the energy-performance trade-off, and that the proposed algorithm provides significant energy savings and maintains a relatively homogenous and stable thermal state at the different rack units in the data center.

The proposed algorithm ensures energy provisioning, performance optimization over existing state-of-the-art heuristics, and on-demand workload allocation.