A simulation test-bed for mobile adaptive architectures |
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| Affiliation: | 1. Dipartimento di Ingegneria, Università degli Studi di Palermo (UNIPA), viale delle Scienze Ed.6, 90128 Palermo, Italy;2. Institute of Engineering Thermodynamics, DLR, Stuttgart, Germany;3. ResourSEAs SrL, viale delle Scienze Ed. 16, 90128 Palermo, Italy;1. Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece;2. School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece;1. 2nd Department of Obstetrics and Gynecology, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400012, Cluj-Napoca, Romania;2. Department of Histology, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400012, Cluj-Napoca, Romania;1. Department of Mechanical Engineering, University of Akron, Akron, OH, United States;2. NOAA National Severe Storms Laboratory, Norman, OK, United States;1. Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA;2. Department of Physics & Astronomy, University of Wyoming, Laramie, WY 82071, USA;3. Department of Mechanical Engineering & Mechanics, Lehigh University, Bethlehem, PA 18015, USA |
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| Abstract: | Existing mobile systems are typically highly constrained with regards to their run-time resources: CPU, memory, communication bandwidth, screen real-estate, battery, and so forth. In current mobile systems, resource allocation decisions are almost always fixed at the time of system creation. However, this situation is arguably changing as mobile systems are becoming more powerful and as the demands being placed upon them are also increasing dramatically. For this reason, such systems need effective methods to manage and control their resources at run-time, particularly in the face of changing environmental conditions and user needs. This paper presents a simulation test-bed for experimenting with architectural design decisions such as communication and negotiation strategies among components, scheduling algorithms, and usability considerations. One significant area that we have begun to experiment with is the use of user-defined “utility” as a means of making dynamic resource allocation decisions. We will discuss the use of utility as a guide for scheduling, describe the test-bed, and present some examples of the results that we have derived, comparing utility-based scheduling with traditional scheduling methods. |
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