Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches
- Journal
- ACM Computing Surveys
In The Last Decade
doi.org/10.1145/2788397 →Countries where authors are citing Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches
This map shows the geographic impact of Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches. 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 Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches more than expected).
Fields of papers citing Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches
This network shows the impact of Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches.
About Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches
This paper, published in 2015, received 361 indexed citations . Written by Zhi‐Hui Zhan, Xiaofang Liu, Yue‐Jiao Gong, Jun Zhang, Henry Shu-Hung Chung and Yun Li covering the research area of Computer Networks and Communications and Information Systems. It is primarily cited by scholars working on Computer Networks and Communications (264 citations), Information Systems (255 citations) and Artificial Intelligence (76 citations). Published in ACM Computing Surveys.
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/10.1145/2788397.