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Approaches for reducing network energy consumption were surveyed from two aspects:realizing energy-proportional computing in data transmission,and reducing duplicated data transmission in the network.Approaches endeavor to realize energy-proportional computing include network presence proxy in edge networks,energy-efficient Ethernet and energy-aware routing techniques in core network.The asynchronous data access nature and the heavy-tailed frequency distribution for data access offer opportunities to reduce duplicated data transmission.Several existing and experimental content distribution mechanisms (e.g.,CDN,P2P,CCN and dually structured internet) and their effects in enhancing energy efficiency in data transmission,were compared and investigated.

Approaches for reducing network energy consumption were surveyed from two aspects:realizing energy-proportional computing in data transmission,and reducing duplicated data transmission in the network.Approaches endeavor to realize energy-proportional computing include network presence proxy in edge networks,energy-efficient Ethernet and energy-aware routing techniques in core network.The asynchronous data access nature and the heavy-tailed frequency distribution for data access offer opportunities to reduce duplicated data transmission.Several existing and experimental content distribution mechanisms (e.g.,CDN,P2P,CCN and dually structured internet) and their effects in enhancing energy efficiency in data transmission,were compared and investigated.

Anomaly detection for time series data is one of the important research problems in data analysis.Its main challenge is to detect if there are any anomalies and locate anomalies with low delay according to context.Most of existing anomaly detection methods capture anomalies using the probability density ratio to measure similarity between sequences.These methods need to use the cross-validation method to estimate the parameters of probability density ratio.However,cross-validation can increase the computational complexity,resulting in low computational efficiency and a high time delay.To address these issues,this paper proposes a detection method based on total variation ratio separation distance,in which total variation is adopted to extract sequence fluctuation features.Due to the fact that the total variation ratio is better than probability density ratio,the proposed method achieves higher computational efficiency and lower time delay.To reduce noise interference and further improve the detection accuracy,the proposed method is combined with the relative total variation.Experimental results show that the proposed method performs well in terms of detection accuracy,low delay and computational efficiency.