Energy management of highly dynamic server workloads in an heterogeneous data center

Author(s):  
Efraim Rotem ◽  
Uri C. Weisser ◽  
Avi Mendelson ◽  
Ahmad Yassin ◽  
Ran Ginosar
Author(s):  
Yanis Hadj Said ◽  
Stephane Bergeon ◽  
Stephane Ploix ◽  
Quang Huy Giap ◽  
Iulia Dumitru

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3164
Author(s):  
Rasool Bukhsh ◽  
Muhammad Umar Javed ◽  
Aisha Fatima ◽  
Nadeem Javaid ◽  
Muhammad Shafiq ◽  
...  

The computing devices in data centers of cloud and fog remain in continues running cycle to provide services. The long execution state of large number of computing devices consumes a significant amount of power, which emits an equivalent amount of heat in the environment. The performance of the devices is compromised in heating environment. The high powered cooling systems are installed to cool the data centers. Accordingly, data centers demand high electricity for computing devices and cooling systems. Moreover, in Smart Grid (SG) managing energy consumption to reduce the electricity cost for consumers and minimum rely on fossil fuel based power supply (utility) is an interesting domain for researchers. The SG applications are time-sensitive. In this paper, fog based model is proposed for a community to ensure real-time energy management service provision. Three scenarios are implemented to analyze cost efficient energy management for power-users. In first scenario, community’s and fog’s power demand is fulfilled from the utility. In second scenario, community’s Renewable Energy Resources (RES) based Microgrid (MG) is integrated with the utility to meet the demand. In third scenario, the demand is fulfilled by integrating fog’s MG, community’s MG and the utility. In the scenarios, the energy demand of fog is evaluated with proposed mechanism. The required amount of energy to run computing devices against number of requests and amount of power require cooling down the devices are calculated to find energy demand by fog’s data center. The simulations of case studies show that the energy cost to meet the demand of the community and fog’s data center in third scenario is 15.09% and 1.2% more efficient as compared to first and second scenarios, respectively. In this paper, an energy contract is also proposed that ensures the participation of all power generating stakeholders. The results advocate the cost efficiency of proposed contract as compared to third scenario. The integration of RES reduce the energy cost and reduce emission of CO 2 . The simulations for energy management and plots of results are performed in Matlab. The simulation for fog’s resource management, measuring processing, and response time are performed in CloudAnalyst.


2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Yanping Chen ◽  
Mingdao Zhao ◽  
Hong Xia ◽  
Xiaodong Jin ◽  
Zhongmin Wang ◽  
...  

Intelligent factory has the characteristics of wide data sources, high data dimensions, and strong data relevance. Intelligent factories need to make different decisions for different needs, so they need to efficiently analyze these data and explore the inherent laws contained in them. At the same time, the increasing amount of data brings various burdens to the network infrastructure between users and smart devices. For the above needs, this paper proposes a tension-based heterogeneous data fusion model in the edge computing layer, which represents the multisource heterogeneous data in the industrial scene as a tensor model, and uses the incremental decomposition algorithm to extract high-quality core data. The model reduces the data flow between the data center and the central cloud while retaining the core data set. Experiments show that the approximate tensor reconstructed from the tensor with 15% core data can guarantee 90% accuracy.


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