Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting
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doi.org/10.1109/cloud.2011.42 →Countries where authors are citing Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting
This map shows the geographic impact of Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting more than expected).
Fields of papers citing Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting
This network shows the impact of Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/10.1109/cloud.2011.42.