Edge computing and Internet of Things (IoT) devices have been widely deployed in smart city applications due to the rapid promotion and implementation of 5G communication technology. Limited by power supply and hardware computing capability, applications on edge servers are mostly deployed and run in the form of container microservices to improve resource utilization. Therefore, identifying the power consumption at the container granularity is of great importance for the load scheduling and service quality assurance of edge servers. In this article, a gradient boosting piecewise linear regression tree (GBRT-PL)-based container power prediction method is proposed. Performance metrics with a strong correlation between container and server power consumption are selected for power consumption modeling. To effectively suit the nonlinear relationship between container performance metrics and server power consumption, the integrated predictive capabilities of multiple regression trees (RTs) and the segmented linear model of single RT leaf nodes are applied. The data from the experiments prove that the GBRT-PL model predicts power consumption more accurately than other models for single and multiple container groups. The highest average relative error rate in the four multicontainer group tests is 6.72%, whereas the highest relative error rate in the 90% quantile is 11.66%. In addition, it can accurately predict the majority of power consumption peaks, which contributes to the precise detection of power consumption anomalies.

Servers have different energy efficiency and energy proportionality (EP) due to their hardware configuration (i.e., CPU generation and memory installation) and workload. However, current virtual machine (VM) scheduling in virtualized environments will saturate servers without considering their energy efficiency and EP differences. This article will discuss EASE, the energy efficiency and proportionality aware VM scheduling approach. EASE first executes customized computing intensive, memory intensive, and hybrid benchmarks to calculate a server's energy efficiency and EP. Then it schedules VMs to servers to keep them working at their peak energy efficiency point (or optimal working range). This step improves the overall energy efficiency of the cluster and the data center. For performance guarantee, EASE migrates VMs from servers under highly contending conditions. The experimental results on real clusters show that power consumption can be saved 37.07% ~ 49.98% in the homogeneous cluster. The average completion time of the computing intensive VMs increases only 0.31 % ~ 8.49%. In the heterogeneous nodes, the power consumption of the computing intensive VMs can be reduced by 44.22 %. The job completion time can be saved by 53.80%.

The authors propose to use formatting templates and implicit formatting semantics information for automatic metadata identification and segmentation. The pure texts and their corresponding formatting information including line height, font type, and font size, are recognized in parallel to guide metadata identification. The authors use implicit formatting semantics, such as the change of formatting, formatting templates and implications, explicit formatting layouts, as well as predefined frequently occurred keywords database to increase the extraction accuracy. Unlike other OCR-based approaches, the authors use open source PDFBox package as the basic preprocessing tool to get pure texts and formatting values of the document contents. On top of PDFBox they built their own pipeline program, namely, PAXAT, to implement their approaches for metadata extraction. 10177 papers from arXiv, ACM, ACL and other publicly accessed and institution-subscribed sources are tested. The overall extraction accuracy of title, authors, affiliations, author-affiliation matching are 0.9798, 0.9425, 0.9298, and 0.9109, respectively.

The huge energy consumption in data centers produces not only high electricity bill but also tremendous carbon footprints. Although today's servers and data centers of leading internet companies are more energy efficient than ever before, the fluctuations in external workload and internal resource utilization calls for energy proportional computing. Insight into server energy proportionality can help improve workload placement while also reducing energy consumption. In this paper, we investigate all 477 valid published results of SPECpower_ssj benchmark from 2007 to 2016Q3 and reorganize them by hardware availability year for more accurate analysis on production servers. Through comprehensive analysis we find that: (1) The specious stagnation of energy proportionality in recent years is mainly caused by the adoption of processors of specific microarchitecture and is not the indicative trend of energy proportionality improvement. (2) Microarchitecture evolution has more influence on energy efficiency improvement than energy proportionality. (3) Today's servers' peak energy efficiencies are shifting from 100% resource utilization to 80% or 70% utilization and server energy proportionality improves with such shifting. We then conduct extensive experiments on 4 rack servers to investigate the energy efficiency variations under different hardware configurations, including memory per core installation and processor frequency scaling. Our experiments show that hardware configuration has significant impact on server's energy efficiency. Our findings presented in this paper provide useful insights and guidance to system designers, as well as data center operators for energy proportionality aware workload placement and energy savings.