Networked computing systems for bio-diversity and environmental preservation

Abstract

Computing platforms have a high water footprint that poses threat to biodiversity preservation. The high water footprint reduces water availability for habitat preservation. Hence, approaches that reduce the water footprint are needful. The presented research proposes an approach that reduces the need for water in future computing platforms. It proposes a hybrid computing platform that comprises terrestrial and non-terrestrial computing platforms. The performance benefit of using hybrid computing platforms is evaluated using the novel water potential metric. The water potential (WP) quantifies the need for water (for cooling) by computing platforms. A low WP shows that computing platforms have reduced the need for water and indicates better performance than a high WP from the perspective of reducing water footprint. Evaluation is done via performance formulation and stochastic simulation of the WP metric. Analysis shows that using the hybrid computing platform instead of the existing approach that utilizes only water-cooled terrestrial data centres reduces the WP by (4.9–93) % on average.

A Sustainable and Secure Load Management Model for Green Cloud Data Centres

“…The electrical energy consumption of CDCs is likely to increase about 15-fold by 2030, i.e., approximately 8% of projected global demand which is estimated to account for more than 3.2 percent of the total worldwide greenhouse gas emissions [1]. The power supply avenue has a huge impact on carbon footprint emission such as high carbon emitting source (for example, coal) dominates lower carbon sources such as renewable energy (for example, wind, sun) in carbon footprint production [2], [3]. Therefore, by establishing the proactive sustainability and efficiency measures at inception, leveraging the latest technology CDCs have to explore using renewable energy such as wind, hydro or solar to power data centres and optimising or upgrading technology to improve its efficiency and operating temperature while reducing carbon emission [4].…”

“…Step 2 evaluates fitness of N solutions with O(N ) complexity. Steps [3][4][5], steps [6][7][8][9][10][11][12], and steps [13][14][15] iterate K times have equal time complexity of O(K). Assume steps [16][17][18][19][20][21][22][23][24][25][26][27][28][29] repeat for t intervals, wherein steps [19][20][21] have O(K) while steps [24][25][26][27][28] have O(N ) complexities.…”

A sustainable and secure load management model for green cloud data centres

“…The electrical energy consumption of CDCs would increase up to 15-fold by 2030, i.e., approximately 8 per cent of projected global demand which is estimated to account for more than 3.2 per cent of the total worldwide greenhouse gas emissions 1 . The power supply avenue has a huge impact on carbon footprint emission such as high carbon emitting source (for example, coal) dominates lower carbon sources such as renewable energy (for example, wind, sun) in carbon footprint production 2 , 3 . Therefore, by establishing the proactive sustainability and efficiency measures at inception, and leveraging the latest technology CDCs have to explore using renewable energy such as wind, hydro or solar to power data centres and optimising technology to improve its efficiency and operating temperature while reducing carbon emission 4 .…”