AI-based Models for Resource Allocation and Resource Demand Forecasting Systems in Aviation: A Survey and Analytical Study

The primary attraction of IaaS is providing elastic resources on demand. It becomes imperative that IaaS-users have an effective methodology for learning what resources they require, how many resources and for how long they need. However, the heterogeneity of resources, the diversity resource demands of different cloud applications and the variation of application-user behaviors pose IaaS-users big challenge. In this paper, we purpose a unified resource demand forecasting model suiting for different applications, various resources and diverse time-varying workload patterns. With the model, taking input from parameterized applications, resources and workload scenarios, the corresponding resources demands during any time interval can be deduced as output. The experiments configure concrete functions and parameters to help understanding the above model.

Download Full-text

China's Copper Demand Forecasting Based on System Dynamics Model

10.20944/preprints201703.0231.v1 ◽

2017 ◽

Cited By ~ 3

Author(s):

Jianbo Yang ◽

Xin Li ◽

Qunyi Liu

Keyword(s):

System Dynamics ◽

Moving Average ◽

Social Indicators ◽

Demand Forecasting ◽

System Dynamics Model ◽

Dynamics Model ◽

Vector Autoregressive ◽

Resource Demand ◽

Demand Growth ◽

Autoregressive Moving Average Model

Copper demand for a country's copper industry has a greater pull effect. China's copper consumption in 2015 has accounted for 50% of the world. The scientific forecast of China's copper demands trend is also an important basis for analyzing its future environmental impact. This paper assumes that China's economy will be developing high, medium and low scenarios, and forecasts economic and social indicators such as total GDP, population and per capita GDP in China from 2016 to 2030. Then, predicted the demand of copper resources in China from 2016 to 2030 by the combination of system dynamics model, vector autoregressive moving average model and inverted U-type empirical model. The results show that: (1) in 2020, 2025 and 2030, China's refined copper demand will be 13 Mt, 15 Mt and 15.5 Mt. (2) China's copper demand growth slowed down significantly from 2016-2030. (3) 2025-2030, China's copper resource demand is stable, into the platform of demand growth, the highest peak value in 2027 will be 15.5 Mt. (4) 2030 years later, China's copper resource demand will enter a slow decline.

Download Full-text

Resource Management for Minimizing Energy and Cost of Geo-Distributed Data Centers

Walailak Journal of Science and Technology (WJST) ◽

10.48048/wjst.2021.9619 ◽

2021 ◽

Vol 18 (13) ◽

Author(s):

Moh Moh THAN

Keyword(s):

Resource Allocation ◽

Energy Consumption ◽

Resource Management ◽

Energy Efficient ◽

Real Life ◽

Cost Effective ◽

Turnaround Time ◽

Electricity Price ◽

Resource Demand ◽

Demand Prediction

Geo-distributed data centers (GDCs) house computing resources and provide cloud services across the world. As cloud computing flourishes, energy consumption and electricity cost for powering servers of GDCs also soar high. Energy consumption and cost minimization for GDCs has become the main challenge for the cloud service providers. This paper proposes a resource management framework that accomplishes resource demand prediction, ensuring service level objective (SLO), electricity price prediction, and energy-efficient and cost-effective resource allocation through GDCs. This paper also proposes an energy-efficient and cost-effective resource allocation (EECERA) algorithm which deploys energy efficiency factors and incorporates the electricity price diversity of GDCs. Extensive evaluations were performed based on real-world workload traces and real-life electricity price data of GDC locations. The evaluation results showed that the resource demand prediction model could predict the right amount of dynamic resource demand while achieving SLO, and also, the electricity price prediction model could provide promising accuracy. The performances of resource allocation algorithms were evaluated on CloudSim. This work contributes to minimizing the energy consumption and the average turnaround time taken to complete the task and offers cost-saving. HIGHLIGHTS SLO guaranteed, energy-efficient and cost-effective resource management framework Energy-efficient and cost-effective resource allocation (EECERA) algorithm Extensive evaluations based on real-world workload traces and real-life electricity price data of GDC locations Performances of resource allocation algorithms evaluated on CloudSim Minimizing the energy consumption and the average turnaround time taken to complete the task and also cost-saving GRAPHICAL ABSTRACT

Download Full-text