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- W4247015269 abstract "The Proceedings of the ACM series present the highest quality research conducted in diverse areas of computer science, as represented by the ACM Special Interest Groups (SIGs). The ACM Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS) focuses on the measurement and performance evaluation of computer systems and operates in close collaboration with the Special Interest Group SIGMETRICS. All the papers in the last three issues of POMACS will be presented during the joint ACM SIGMETRICS / IFIP Performance 2019 conference. Despite the extraordinary progress of networks and computer systems in the last decades, these are far from perfect. There are still performance issues and security threats, and the complexity of these systems make them often difficult to manage and operate. Research is expected to contribute to the further improvements of these systems, to make them more efficient, more robust, more flexible and more predictable. In the era of global warming, there is also an urgent need to make the digital economy more green, and thus beneficial to all in the long term. The papers that have appeared in POMACS during the past year reflect the ability of the journal to attract high-quality submissions in this area. The last three issues have included papers on mainstream topics of the ACM SIGMETRICS / IFIP Performance communities, including load balancing, scheduling, resource allocation, performance evaluation and measurement, as well as emerging topics that have become prominent in recent years, such as virtualization, network science, and machine learning. As part of a new initiative to highlight work from outside the SIGMETRICS community that may spur research within the community, the 2019 conference will include a Highlights Beyond SIGMETRICS session featuring leading papers from conferences in neighboring disciplines. We thank Adam Wierman for leading this initiative." @default.
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- W4247015269 date "2019-06-19" @default.
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- W4247015269 title "Editorial" @default.
- W4247015269 doi "https://doi.org/10.1145/3341617.3326134" @default.
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