Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Mounesan, Motahare"'
Balancing mutually diverging performance metrics, such as, processing latency, outcome accuracy, and end device energy consumption is a challenging undertaking for deep learning model inference in ad-hoc edge environments. In this paper, we propose E
Externí odkaz:
http://arxiv.org/abs/2410.12221
Autor:
Yeddulapalli, Hemanth Sai, Alarcon, Mauro Lemus, Roy, Upasana, Neupane, Roshan Lal, Gafurov, Durbek, Mounesan, Motahare, Debroy, Saptarshi, Calyam, Prasad
Volunteer Edge-Cloud (VEC) computing has a significant potential to support scientific workflows in user communities contributing volunteer edge nodes. However, managing heterogeneous and intermittent resources to support machine/deep learning (ML/DL
Externí odkaz:
http://arxiv.org/abs/2409.03057
In recent times, Volunteer Edge-Cloud (VEC) has gained traction as a cost-effective, community computing paradigm to support data-intensive scientific workflows. However, due to the highly distributed and heterogeneous nature of VEC resources, centra
Externí odkaz:
http://arxiv.org/abs/2407.01428
Introducing meeting points to ride-pooling (RP) services has been shown to increase the satisfaction level of both riders and service providers. Passengers may choose to walk to a meeting point for a cost reduction. Drivers may also get matched with
Externí odkaz:
http://arxiv.org/abs/2105.00994