Making scheduling cool: temperature-aware workload placement in data centers
- Journal
- USENIX Annual Technical Conference
In The Last Decade
doi.org/w15122258 →Countries where authors are citing Making scheduling cool: temperature-aware workload placement in data centers
This map shows the geographic impact of Making scheduling cool: temperature-aware workload placement in data centers. 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 Making scheduling cool: temperature-aware workload placement in data centers with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Making scheduling cool: temperature-aware workload placement in data centers more than expected).
Fields of papers citing Making scheduling cool: temperature-aware workload placement in data centers
This network shows the impact of Making scheduling cool: temperature-aware workload placement in data centers. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Making scheduling cool: temperature-aware workload placement in data centers.
About Making scheduling cool: temperature-aware workload placement in data centers
This paper, published in 2005, received 486 indexed citations . Written by Justin Moore, Jeff Chase, Parthasarathy Ranganathan and Ratnesh Sharma covering the research area of Computer Networks and Communications, Hardware and Architecture and Information Systems. It is primarily cited by scholars working on Information Systems (405 citations), Computer Networks and Communications (394 citations) and Hardware and Architecture (222 citations). Published in USENIX Annual Technical Conference.
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/w15122258.