Items where Author is "Sanz Marco, Vicent"

Journal Article

Elhabbash, Abdessalam and Elkhatib, Yehia and Nundloll, Vatsala and Sanz Marco, Vicent and Blair, Gordon S. (2024) Principled and automated system of systems composition using an ontological architecture. Future Generation Computer Systems, 157. pp. 499-515. ISSN 0167-739X

Sanz Marco, Vicent and Taylor, Ben and Wang, Zheng and Elkhatib, Yehia (2020) Optimizing Deep Learning Inference on Embedded Systems Through Adaptive Model Selection. ACM Transactions on Embedded Computing, 19 (1): 2. ISSN 1539-9087

Contribution in Book/Report/Proceedings

Elhabbash, Abdessalam and Nundloll, Vatsala and Elkhatib, Yehia and Blair, Gordon and Sanz Marco, Vicent (2020) An Ontological Architecture for Principled and Automated System of Systems Composition. In: 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems :. ACM, 85–95. ISBN 9781450379625

Taylor, Ben and Sanz Marco, Vicent and Wolff, Willy and Elkhatib, Yehia and Wang, Zheng (2018) Adaptive Deep Learning Model Selection on Embedded Systems. In: LCTES 2018 Proceedings of the 19th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems :. ACM, New York, pp. 31-43. ISBN 9781450358033

Sanz Marco, Vicent and Taylor, Ben and Porter, Barry Francis and Wang, Zheng (2017) Improving Spark Application Throughput Via Memory Aware Task Co-location : A Mixture of Experts Approach. In: Middleware '17 Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference :. ACM, New York, pp. 95-108. ISBN 9781450347204

Taylor, Ben and Sanz Marco, Vicent and Wang, Zheng (2017) Adaptive optimization for OpenCL programs on embedded heterogeneous systems. In: LCTES 2017 Proceedings of the 18th ACM SIGPLAN/SIGBED Conference on Languages, Compilers, and Tools for Embedded Systems :. ACM, New York, pp. 11-20. ISBN 9781450350303

Sanz Marco, Vicent and Wang, Zheng and Porter, Barry Francis (2017) Real-time power cycling in video on demand data centres using online Bayesian prediction. In: 2017 IEEE 37th International Conference on Distributed Computing Systems :. IEEE, pp. 2125-2130. ISBN 9781538617939