Considering the flexible attitude maneuver and the narrow field of view of agile Earth observation satellite(AEOS)together,a comprehensive task clustering(CTC)is proposed to improve the observation scheduling problem ...Considering the flexible attitude maneuver and the narrow field of view of agile Earth observation satellite(AEOS)together,a comprehensive task clustering(CTC)is proposed to improve the observation scheduling problem for AEOS(OSPFAS).Since the observation scheduling problem for AEOS with comprehensive task clustering(OSWCTC)is a dynamic combination optimization problem,two optimization objectives,the loss rate(LR)of the image quality and the energy consumption(EC),are proposed to format OSWCTC as a bi-objective optimization model.Harnessing the power of an adaptive large neighborhood search(ALNS)algorithm with a nondominated sorting genetic algorithm II(NSGA-II),a bi-objective optimization algorithm,ALNS+NSGA-II,is developed to solve OSWCTC.Based on the existing instances,the efficiency of ALNS+NSGA-II is analyzed from several aspects,meanwhile,results of extensive computational experiments are presented which disclose that OSPFAS considering CTC produces superior outcomes.展开更多
The amount and scale of worldwide data centers grow rapidly in the era of big data,leading to massive energy consumption and formidable carbon emission.To achieve the efficient and sustainable development of informati...The amount and scale of worldwide data centers grow rapidly in the era of big data,leading to massive energy consumption and formidable carbon emission.To achieve the efficient and sustainable development of information technology(IT)industry,researchers propose to schedule data or data analytics jobs to data centers with low electricity prices and carbon emission rates.However,due to the highly heterogeneous and dynamic nature of geo-distributed data centers in terms of resource capacity,electricity price,and the rate of carbon emissions,it is quite difficult to optimize the electricity cost and carbon emission of data centers over a long period.In this paper,we propose an energy-aware data backup and job scheduling method with minimal cost(EDJC)to minimize the electricity cost of geo-distributed data analytics jobs,and simultaneously ensure the long-term carbon emission budget of each data center.Specifically,we firstly design a cost-effective data backup algorithm to generate a data backup strategy that minimizes cost based on historical job requirements.After that,based on the data backup strategy,we utilize an online carbon-aware job scheduling algorithm to calculate the job scheduling strategy in each time slot.In this algorithm,we use the Lyapunov optimization to decompose the long-term job scheduling optimization problem into a series of real-time job scheduling optimization subproblems,and thereby minimize the electricity cost and satisfy the budget of carbon emission.The experimental results show that the EDJC method can significantly reduce the total electricity cost of the data center and meet the carbon emission constraints of the data center at the same time.展开更多
文摘Considering the flexible attitude maneuver and the narrow field of view of agile Earth observation satellite(AEOS)together,a comprehensive task clustering(CTC)is proposed to improve the observation scheduling problem for AEOS(OSPFAS).Since the observation scheduling problem for AEOS with comprehensive task clustering(OSWCTC)is a dynamic combination optimization problem,two optimization objectives,the loss rate(LR)of the image quality and the energy consumption(EC),are proposed to format OSWCTC as a bi-objective optimization model.Harnessing the power of an adaptive large neighborhood search(ALNS)algorithm with a nondominated sorting genetic algorithm II(NSGA-II),a bi-objective optimization algorithm,ALNS+NSGA-II,is developed to solve OSWCTC.Based on the existing instances,the efficiency of ALNS+NSGA-II is analyzed from several aspects,meanwhile,results of extensive computational experiments are presented which disclose that OSPFAS considering CTC produces superior outcomes.
基金supported by the National Natural Science Foundation of China under Grant Nos.U23B2004 and 62162018,the Guangxi Natural Science Foundation under Grant Nos.2025GXNSFBA069389 and 2025GXNSFBA069101the Key Research and Development Program of Guangxi under Grant No.AB25069157+1 种基金the Hunan Provincial Excellent Youth Fund under Grant No.2023JJ20055the Open Project Program of Guangxi Key Laboratory of Digital Infrastructure under Grant No.GXDIOP2024005.
文摘The amount and scale of worldwide data centers grow rapidly in the era of big data,leading to massive energy consumption and formidable carbon emission.To achieve the efficient and sustainable development of information technology(IT)industry,researchers propose to schedule data or data analytics jobs to data centers with low electricity prices and carbon emission rates.However,due to the highly heterogeneous and dynamic nature of geo-distributed data centers in terms of resource capacity,electricity price,and the rate of carbon emissions,it is quite difficult to optimize the electricity cost and carbon emission of data centers over a long period.In this paper,we propose an energy-aware data backup and job scheduling method with minimal cost(EDJC)to minimize the electricity cost of geo-distributed data analytics jobs,and simultaneously ensure the long-term carbon emission budget of each data center.Specifically,we firstly design a cost-effective data backup algorithm to generate a data backup strategy that minimizes cost based on historical job requirements.After that,based on the data backup strategy,we utilize an online carbon-aware job scheduling algorithm to calculate the job scheduling strategy in each time slot.In this algorithm,we use the Lyapunov optimization to decompose the long-term job scheduling optimization problem into a series of real-time job scheduling optimization subproblems,and thereby minimize the electricity cost and satisfy the budget of carbon emission.The experimental results show that the EDJC method can significantly reduce the total electricity cost of the data center and meet the carbon emission constraints of the data center at the same time.
