Transfer Learning for Cross-Model Regression in Performance Modeling for the Cloud

Autor: Ali B. Hashemi, Francesco Iorio, Michael Tao, Cristiana Amza

Publikováno v: CloudCom

Performance characteristics of a complex system in different configurations are expensive to obtain due to the cost of sampling the system performance. We introduce ModelMap, a novel transfer learning technique for the performance modeling of configu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b88a32cf142a8017f94544581ee75778
https://doi.org/10.1109/cloudcom.2019.00015

Ensemble: A Tool for Performance Modeling of Applications in Cloud Data Centers

Autor: Cristiana Amza, Jin Chen, Gokul Soundararajan, Ali B. Hashemi, Francesco Iorio, Saeed Ghanbari

Publikováno v: IEEE Transactions on Cloud Computing. 4:20-33

We introduce Ensemble, a runtime framework and associated tools for building application performance models on-the-fly. These dynamic performance models can be used to support complex, highly dimensional resource allocation, and/or what-if performanc

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c3e5dd1f7de6101be89ba61c0b88d359
https://doi.org/10.1109/tcc.2015.2469656

DreamSketch

Autor: Rubaiat Habib Kazi, George Fitzmaurice, Ali B. Hashemi, Tovi Grossman, Hyunmin Cheong

Publikováno v: UIST

We present DreamSketch, a novel 3D design interface that combines the free-form and expressive qualities of sketching with the computational power of generative design algorithms. In DreamSketch, a user coarsely defines the problem by sketching the d

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9268b968c094815182d2da09550102ed
https://doi.org/10.1145/3126594.3126662

Ontology for Big Systems: The Ontology Summit 2012 Communiqué

Autor: Todd Schneider, Mary Brady, Ram D. Sriram, Michael Gruninger, Nicola Guarino, Steve Ray, Amanda Vizedom, Ali B. Hashemi, Mike Bennett, Trish Whetzel, Anatoly Levenchuk, Ernie Lucier, Peter Yim, Matthew West, Henson Graves, Leo Obrst, Cory Casanave

Publikováno v: Applied ontology 7 (2012): 357–371. doi:10.3233/AO-2012-0111
info:cnr-pdr/source/autori:Schneider, Todd; Hashemi, Ali; Bennett, Mike; Brady, Mary; Casanave, Cory; Graves, Henson; Gruninger, Michael; Guarino, Nicola; Levenchuk, Anatoly; Lucier, Ernie; Obrst, Leo; Ray, Steve; Sriram, Ram D.; Vizedom, Amanda; West, Matthew; Whetzel, Trish; Yim, Peter/titolo:Ontology for Big Systems: The Ontology Summit 2012 Communique/doi:10.3233%2FAO-2012-0111/rivista:Applied ontology/anno:2012/pagina_da:357/pagina_a:371/intervallo_pagine:357–371/volume:7

The Ontology Summit 2012 explored the current and potential uses of ontology, its methods and paradigms, in big systems and big data: How ontology can be used to design, develop, and operate such systems. The systems addressed were not just software

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b5308a2fb6980d3b36d085b101f7f2e1
https://doi.org/10.3233/ao-2012-0111

A note on the learning automata based algorithms for adaptive parameter selection in PSO

Autor: Mohammad Reza Meybodi, Ali B. Hashemi

Publikováno v: Applied Soft Computing. 11:689-705

PSO, like many stochastic search methods, is very sensitive to efficient parameter setting such that modifying a single parameter may cause a considerable change in the result. In this paper, we study the ability of learning automata for adaptive PSO

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2c61643b1898ca782f413daf09b6c209
https://doi.org/10.1016/j.asoc.2009.12.030