Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

Future AI applications require performance, reliability and privacy that the existing, cloud-dependant system architectures cannot provide. In this article, we study orchestration in the device-edge-cloud continuum, and focus on AI for edge, that is, the AI methods used in resource orchestration. We claim that to support the constantly growing requirements of intelligent applications in the device-edge-cloud computing continuum, resource orchestration needs to embrace edge AI and emphasize local autonomy and intelligence. To justify the claim, we provide a general definition for continuum orchestration, and look at how current and emerging orchestration paradigms are suitable for the computing continuum. We describe certain major emerging research themes that may affect future orchestration, and provide an early vision of an orchestration paradigm that embraces those research themes. Finally, we survey current key edge AI methods and look at how they may contribute into fulfilling the vision of future continuum orchestration.Comment: 50 pages, 8 figures (Revised content in all sections, added figures and new section

National Aeronautics and Space Administration (NASA)/American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program, 1989, volume 1

The 1989 Johnson Space Center (JSC) National Aeronautics and Space Administration (NASA)/American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program was conducted by Texas A and M University and JSC. The 10-week program was operated under the auspices of the ASEE. The program at JSC, as well as the programs at other NASA Centers, was funded by the Office of University Affairs, NASA Headquarters, Washington, D.C. The objectives of the program, which began nationally in 1964 and at JSC in 1965, are: (1) to further the professional knowledge of qualified engineering and science faculty members; (2) to stimulate an exchange of ideas between participants and NASA; (3) to enrich and refresh the research and teaching activities of participants' institutions; and (4) to contribute to the research objective of the NASA Centers

Efficient and predictable high-speed storage access for real-time embedded systems

As the speed, size, reliability and power efficiency of non-volatile storage media increases, and the data demands of many application domains grow, operating systems are being put under escalating pressure to provide high-speed access to storage. Traditional models of storage access assume devices to be slow, expecting plenty of slack time in which to process data between requests being serviced, and that all significant variations in timing will be down to the storage device itself. Modern high-speed storage devices break this assumption, causing storage applications to become processor-bound, rather than I/O-bound, in an increasing number of situations. This is especially an issue in real-time embedded systems, where limited processing resources and strict timing and predictability requirements amplify any issues caused by the complexity of the software storage stack. This thesis explores the issues related to accessing high-speed storage from real-time embedded systems, providing a thorough analysis of storage operations based on metrics relevant to the area. From this analysis, a number of alternative storage architectures are proposed and explored, showing that a simpler, more direct path from applications to storage can have a positive impact on efficiency and predictability in such systems